Dissertations / Theses on the topic 'Field robotics'
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Cordie, Troy P. "Modular reconfigurable field robotics." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/230503/1/Troy_Cordie_Thesis.pdf.
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This thesis explored the use of modular robots in the field. This work represents a step forward in robotics with technology emerging from the laboratory into the outdoors. Innovation is demonstrated through the ability to build custom robots on demand in the field. This customisation enabled by modular-reconfigurable components allowed a single person to deploy multiple configurations rapidly. Additionally, it was demonstrated that a modular reconfigurable approach to robotics improved robustness to failure. Finally, this work demonstrated that the ability to self-reconfigure allowed a field-deployed robot to explore further.
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Dansereau, Donald Gilbert. "Plenoptic Signal Processing for Robust Vision in Field Robotics." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9929.
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This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications.
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Cui, Yan. "Interval analysis techniques for field mapping and geolocation." Thesis, Purdue University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10151584.
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Field mapping and estimation become a challenging problem, with their various applications on non-linear estimation, geolocation, and positioning systems. In this research, we develop novel algorithms based on interval analysis and introduce a solution for autonomous map construction, field mapping, geolocation, and simultaneous localization and mapping (SLAM), providing applications on indoor geolocation and other potential areas.
Generally, the localization algorithm includes a quasi-state estimation and a dynamic estimation. Quasi-static estimation collects each single measurement and give a group of estimation intervals on the pre-constructed field map. Results from quasi-static estimation are processed into the dynamic estimation algorithm, having properties of removing redundant intervals while keep the best estimation results. Sizes of estimation from quasi-static estimation are proved to be related to the resolution of the map and the quality of the sensor. Based on quasi-state estimation algorithm, we develop an algorithm to fuse different type of measurements and discuss the condition when this algorithm an be applied effectively.
Having theoretical guarantees, we apply these algorithms to augment the accuracy of cell phone geolocation by taking advantage of local variations of magnetic intensity. Thus, the sources of disturbances to magnetometer readings caused indoors are effectively used as beacons for localization. We construct a magnetic intensity map for an indoor environment by collecting magnetic field data over each floor tile. We then test the algorithms without position initialization and obtain indoor geolocation to within 2m while slowly walking over a complex path of 80 meters. The geolocation errors are smaller in the vicinity of large magnetic disturbances. After fusing the magnetometer measurement with inertial measurements on the cell phone, the algorithm yields even smaller geolocation errors of under 50cm for a moving user.
The map construction and geolocation algorithms are then extended to realize the SLAM, with hierarchical structure of estimation update and localization update. When a new user steps into a random map, the dead reckoning algorithm with assistance of IMU and Kalman filter provides initial estimation of position on the map, which coordinates the corresponding reading of magnetic field intensity as well as all other sources such as WiFi received signal strength (RSS), to construct an initial map. Based on the initial map, we then apply the localization algorithm to estimate new geolocations consequently and fuse the estimation intervals both from IMU and from crowd-sourced field maps to reduce the estimation size and eventually revise the map as well as the geolocation.
In this research, we have built up mathematical model and developed mathematical solutions with corresponding theories and proofs. Our theoretical results connect geolocation accuracy to combinations of sensor and map properties.
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Chen, Changhe. "Robot feasibility for trimming and shaping field-grown nursery plants." Connect to resource, 1987. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1201633112.
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Heath, Gerhardus. "Dynamic reconfigurable platform for swarm robotics." Thesis, Stellenbosch : University of Stellenbosch, 2011. http://hdl.handle.net/10019.1/6814.
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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2011.ENGLISH ABSTRACT: Swarm intelligence research was inspired by biological systems in nature. Working ants and bees has captivated researchers for centuries, with the ant playing a major role in shaping the future of robotic swarm applications. The ants foraging activity can be adapted for different applications of robotic swarm intelligence. Numerous researchers have conducted theoretical analysis and experiments on the ants foraging activities and communication styles. Combining this information with modern reconfigurable computing opens the door to more complex behaviour with improved system dynamics. Reconfigurable computing has numerous applications in swarm intelligence such as true hardware parallel processing, dynamic power save algorithms and dynamic peripheral changes to the CPU core. In this research a brief study is made of swarm intelligence and its applications. The ants' foraging activities were studied in greater detail with the emphasis on a layered control system designed implementation in a robotic agent. The robotic agent’s hardware was designed using a partial self reconfigurable FPGA as the main building element. The hardware was designed with the emphasis on system flexibility for swarm application drawing attention to power reduction and battery life. All of this was packaged into a differential drive chassis designed specifically for this project.
AFRIKAANSE OPSOMMING: Die motivering vir swerm robotika kom van die natuur. Vir eeue fassineer swerm insekte soos bye en miere navorsers. Dit is verstommend hoe ’n groep klein en nietige insekte sulke groot take kan verrig. Die mier speel ‘n belangrike rol en is die sentrale tema van menige publikasies. Die mier se kos-soek aktiwiteit kan aangepas word vir swerm robotika toepassings. Hierdie aktiwiteit vervat verskeie sleutel konsepte wat belangrik is vir robotika toepassings. Deur bv. die mier se aktiwiteite te kombineer met dinamies herkonfigureerbare hardeware, kan meer komplekse gedrag bestudeer word. Die stelsel dinamika verbeter ook, aangesien dit nou moontlik is om sekere take in parallel uit te voer. Deur ’n interne prosesseerder in die herkonfigureerbare hardeware in te sluit, is dit nou vir die stelsel moontlik om homself te verander tydens taak verrigting. Komplekse krag bestuur gedrag is ook moontlik deurdat die prosesseerder die spoed en rand apparaat kan verander soos benodig. ‘n Verdere voordeel is dat die stelsel aanpasbaar is en dus vir verskeie navorsingsprojekte gebruik kan word. In hierdie navorsing word ’n literatuur studie van swerm robotika gemaak en word daar ook na toepassings gekyk. Met die klem op praktiese implementering, word die mier se kos-soek aktiwiteit in detail ondersoek deur gebruik te maak van ’n laag beheerstelsel. In hierdie laag beheerstelsel verteenwoordig elke laag ’n hoër vlak gedrag. Stelsel aanpasbaarheid en lae kragverbruik speel ’n deurslaggewende rol in die ontwerp, en om hierdie rede vorm ’n FPGA die hart van die sisteem.
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Ross, Patrick J. "Vision-based traversability estimation in field environments." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/96033/1/Patrick_Ross_Thesis.pdf.
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Robust obstacle detection and traversability estimation remain a challenges for mobile robots traversing outdoor field environments. Illumination and environmental variances limit the applicability of appearance cues, while vegetation limits structure cues. Systems that combine multiple cues can potentially overcome deficiencies in individual cues. A key challenge in designing multi-sensor systems is to automatically and appropriately combine these cues in an unsupervised manner. This thesis presents methods for online obstacle detection and traversability estimation in field environments which continuously learn online about environmental and illumination conditions, and can operate in the presence of significant vegetation. The results demonstrate these methods in online field experiments and show that they give competitive performance without the requirement of pre-training or environment-specific tuning.
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Kronquist, Jan. "Extending the electric field approach." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4668.
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Potential fields is popular technique in autonomous robotics. The Electric Field Approach is such a technique used for high level robot control. The approach was developed by Team Sweden for use in RoboCup 2000, a soccer competition for mobile robots. This thesis describes several improvements that has been made while preparing for RoboCup 2001. The improvements are used to create simple behaviors which achieve a form of implicit cooperation and also more active defence. This thesis also describes improvement ideas that failed to show the de-sired improvement and the reason for this is discussed, thereby deepening the understanding of the Electric Field Approach.
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Long, Matthew T. "Creating a distributed field robot architecture for multiple robots." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000539.
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Asmar, Daniel. "Vision-Inertial SLAM using Natural Features in Outdoor Environments." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2843.
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Simultaneous Localization and Mapping (SLAM) is a recursive probabilistic inferencing process used for robot navigation when Global Positioning Systems (GPS) are unavailable. SLAM operates by building a map of the robot environment, while concurrently localizing the robot within this map. The ultimate goal of SLAM is to operate anywhere using the environment's natural features as landmarks. Such a goal is difficult to achieve for several reasons. Firstly, different environments contain different types of natural features, each exhibiting large variance in its shape and appearance. Secondly, objects look differently from different viewpoints and it is therefore difficult to always recognize them. Thirdly, in most outdoor environments it is not possible to predict the motion of a vehicle using wheel encoders because of errors caused by slippage. Finally, the design of a SLAM system to operate in a large-scale outdoor setting is in itself a challenge. The above issues are addressed as follows. Firstly, a camera is used to recognize the environmental context (e. g. , indoor office, outdoor park) by analyzing the holistic spectral content of images of the robot's surroundings. A type of feature (e. g. , trees for a park) is then chosen for SLAM that is likely observable in the recognized setting. A novel tree detection system is introduced, which is based on perceptually organizing the content of images into quasi-vertical structures and marking those structures that intersect ground level as tree trunks. Secondly, a new tree recognition system is proposed, which is based on extracting Scale Invariant Feature Transform (SIFT) features on each tree trunk region and matching trees in feature space. Thirdly, dead-reckoning is performed via an Inertial Navigation System (INS), bounded by non-holonomic constraints. INS are insensitive to slippage and varying ground conditions. Finally, the developed Computer Vision and Inertial systems are integrated within the framework of an Extended Kalman Filter into a working Vision-INS SLAM system, named VisSLAM.
VisSLAM is tested on data collected during a real test run in an outdoor unstructured environment. Three test scenarios are proposed, ranging from semi-automatic detection, recognition, and initialization to a fully automated SLAM system. The first two scenarios are used to verify the presented inertial and Computer Vision algorithms in the context of localization, where results indicate accurate vehicle pose estimation for the majority of its journey. The final scenario evaluates the application of the proposed systems for SLAM, where results indicate successful operation for a long portion of the vehicle journey. Although the scope of this thesis is to operate in an outdoor park setting using tree trunks as landmarks, the developed techniques lend themselves to other environments using different natural objects as landmarks.
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Sarker, Md Omar Faruque. "Self-regulated multi-robot task allocation." Thesis, University of South Wales, 2010. https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html.
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To deploy a large group of autonomous robots in dynamic multi-tasking environments, a suitable multi-robot task-allocation (MRTA) solution is required. This must be scalable to variable number of robots and tasks. Recent studies show that biology-inspired self-organized approaches can effectively handle task-allocation in large multi-robot systems. However most existing MRTA approaches have overlooked the role of different communication and sensing strategies found in selfregulated biological societies. This dissertation proposes to solve the MRTA problem using a set of previously published generic rules for division of labour derived from the observation of ant,human and robotic social systems. The concrete form of these rules, the attractive field model (AFM), provides sufficient abstraction to local communication and sensing which is uncommon in existing MRTA solutions. This dissertation validates the effectiveness of AFM to address MRTA using two bio-inspired communication and sensing strategies: "global sensing - no communication" and "local sensing - local communication". The former is realized using a centralized communication system and the latter is emulated under a peer-topeer local communication scheme. They are applied in a manufacturing shop-floor scenario using 16 e-puck robots. A robotic interpretation of AFM is presented that maps the generic parameters of AFM to the properties of a manufacturing shopfloor. A flexible multi-robot control architecture, hybrid event-driven architecture on D-Bus, has been outlined which uses the state-of-the-art D-Bus interprocess communication to integrate heterogeneous software components. Based-on the organization of task-allocation, communication and interaction among robots, a novel taxonomy of MRTA solutions has been proposed to remove the ambiguities found in existing MRTA solutions. Besides, a set of domainindependent metrics, e.g., plasticity, task-specialization and energy usage, has been formalized to compare the performances of the above two strategies. The presented comparisons extend our general understanding of the role of information exchange strategies to achieve the distributed task-allocations among various social groups.
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Clem, Garrett Stuart. "An Optimized Circulating Vector Field Obstacle Avoidance Guidance for UnmannedAerial Vehicles." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1530874969780028.
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Wallace, Nathan Daniel. "Energy-aware Planning and Control of Off-road Wheeled Mobile Robots." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23221.
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In modern agriculture, many of the tasks necessary for the smooth operation of a farm can be categorised as dull, dirty, or dangerous. This makes such operations a prime candidate for automation. However, most agricultural properties are vast, often encompassing large areas of undulating off-road terrain with varying traction conditions, making them difficult to traverse for wheeled platforms. Furthermore, the large scale of these properties necessitates careful consideration and management of resources---especially energy or fuel---in the planning of missions, to traverse these large distances and successfully achieve objectives without the risk of stranding the platform out in the field. These challenges need to be addressed before autonomous wheeled mobile robots are ready for deployment in agricultural contexts. This thesis seeks to address some of the key issues that impact the feasibility of deploying wheeled mobile robots in the field, focusing on off-road mobility and the generation of energy-efficient plans for these platforms. In the case of agricultural applications, where the robot may be required to operate near crops and livestock, accurate navigation is of utmost importance, and achieving this in the presence of difficult off-road conditions is a major challenge. The solution proposed in this work is an optimisation-based estimation and control strategy for accurate trajectory tracking in the presence of slip. A novel structured parameter blocking extension to this strategy is developed and tested, and shown to improve state estimation and path tracking performance for traversal over rapidly varying terrain conditions. To address the problem of efficiency, a data-driven energy cost of motion model for an omnidirectional wheeled mobile robot is then developed, and asymptotically optimal sampling-based planners exploiting this model, along with consideration of various terrain features and the configuration of the platform itself, are presented.
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Carlson, Jennifer. "Analysis of How Mobile Robots Fail in the Field." Scholar Commons, 2004. https://scholarcommons.usf.edu/etd/980.
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The considerable risk to human life associated with modern military operations in urban terrain (MOUT) and urban search and rescue (USAR) has led professionals in these domains to explore the use of robots to improve safety. Recent studies on mobile robot use in the field have shown a noticeable lack of reliability in real field conditions. Improving mobile robot reliability for applications such as USAR and MOUT requires an understanding of how mobile robots fail in field environments.
This paper provides a detailed investigation of how ground-based mobile robots fail in the field. Forty-four representative examples of failures from 13 studies of mobile robot reliability in the USAR and MOUT domains are gathered, examined, and classified. A novel taxonomy sufficient to cover any failure a ground-based mobile robot may encounter in the field is presented. This classification scheme draws from established standards in the dependability computing [30] and human-computer interaction [40] communities, as well as recent work [6] in the robotics domain. Both physical failures (failures within the robotic system) and human failures are considered.
Overall robot reliability in field environments is low with between 6 and 20 hours mean time between failures (MTBF), depending on the criteria used to determine if a failure has occurred. Common issues with existing platforms appear to be the following: unstable control systems, chassis and effectors designed and tested for a narrow range of environmental conditions, limited wireless communication range in urban environments, and insufficient wireless bandwidth. Effectors and the control system are the most common sources of physical failures. Of the human failures examined, slips are more common than mistakes. Two-thirds of the failures examined in [6] and [7] could be repaired in the field. Failures which resulted in the suspension of the robot's task until the repair was completed are also more common with 94% of the failures reported in [13].
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Vallivaara, I. (Ilari). "Simultaneous localization and mapping using the indoor magnetic field." Doctoral thesis, Oulun yliopisto, 2018. http://urn.fi/urn:isbn:9789526217741.
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Abstract The Earth’s magnetic field (MF) has been used for navigation for centuries. Man-made metallic structures, such as steel reinforcements in buildings, cause local distortions to the Earth’s magnetic field. Up until the recent decade, these distortions have been mostly considered as a source of error in indoor localization, as they interfere with the compass direction. However, as the distortions are temporally stable and spatially distinctive, they provide a unique magnetic landscape that can be used for constructing a map for indoor localization purposes, as noted by recent research in the field. Most approaches rely on manually collecting the magnetic field map, a process that can be both tedious and error-prone. In this thesis, the map is collected by a robotic platform with minimal sensor equipment. It is shown that a mere magnetometer along with odometric information suffices to construct the map via a simultaneous localization and mapping (SLAM) procedure that builds on the Rao-Blackwellized particle filter as means for recursive Bayesian estimation. Furthermore, the maps are shown to achieve decimeter level localization accuracy that combined with the extremely low-cost hardware requirements makes the presented methods very lucrative for domestic robots. In addition, general auxiliary methods for effective sampling and dealing with uncertainties are presented. Although the methods presented here are devised in mobile robotics context, most of them are also applicable to mobile device-based localization, for example, with little modifications. Magnetic field localization offers a promising alternative to WiFi-based methods for achieving GPS-level localization indoors. This is motivated by the rapidly growing indoor location marketTiivistelmä Maan magneettikenttään perustuvat kompassit ovat ohjanneet merenkäyntiä vuosisatojen ajan. Rakennusten metallirakenteet aiheuttavat paikallisia häiriöitä tähän magneettikenttään, minkä vuoksi kompasseja on pidetty epäluotettavina sisätiloissa. Vasta viimeisen vuosikymmenen aikana on huomattu, että koska nämä häiriöt ovat ajallisesti pysyviä ja paikallisesti hyvin erottelevia, niistä voidaan muodostaa jokaiselle rakennukselle yksilöllinen häiriöihin perustuva magneettinen kartta, jota voidaan käyttää sisätiloissa paikantamiseen. Suurin osa tämänhetkisistä magneettikarttojen sovelluksista perustuu kartan käsin keräämiseen, mikä on sekä työlästä että tarjoaa mahdollisuuden inhimillisiin virheisiin. Tämä väitöstutkimus tarttuu ongelmaan laittamalla robotin hoitamaan kartoitustyön ja näyttää, että robotti pystyy itsenäisesti keräämään magneettisen kartan hyödyntäen pelkästään magnetometriä ja renkaiden antamia matkalukemia. Ratkaisu perustuu faktoroituun partikkelisuodattimeen (RBPF), joka approksimoi täsmällistä rekursiivista bayesilaista ratkaisua. Robotin keräämien karttojen tarkkuus mahdollistaa paikannuksen n. 10 senttimetrin tarkkuudella. Vähäisten sensori- ja muiden vaatimusten takia menetelmä soveltuu erityisen hyvin koti- ja parvirobotiikkaan, joissa hinta on usein ratkaiseva tekijä. Tutkimuksessa esitellään lisäksi uusia apumenetelmiä tehokkaaseen näytteistykseen ja epävarmuuden hallintaan. Näiden käyttöala ei rajoitu pelkästään magneettipaikannukseen- ja kartoitukseen. Robotiikan sovellusten lisäksi tutkimusta motivoi voimakkaasti kasvava tarve älylaitteissa toimivalle sisätilapaikannukselle. Tämä avaa uusia mahdollisuuksia paikannukselle ympäristöissä, joissa GPS ei perinteisesti toimi
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Vaidya, Varun, and Kushal Bheemesh. "Adaptive Warning Field System." Thesis, Högskolan i Halmstad, CAISR Centrum för tillämpade intelligenta system (IS-lab), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-35312.
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This thesis is based on the work carried out in the field of safety systems for Autonomous Guided Vehicles(AGV). With autonomous vehicles being more prominent today, safe traversing of these is a major concern. The same is true for AGVs working in industry environment like forklift trucks etc. Our work applies to industrial robots. The method described here is developed by closely following an algorithm developed for safe traversing of a robot using a warning field. The report describes the literature review with work related to the safe traversing, path planning and collision avoidance in robots. The next part is dedicated to describing the methodology of implementation of the Adaptive Warning Field Method and the Dynamic Window Approach. The evaluation of the Adaptive Warning Method with the previous developed Warning Field Methods is done and test cases are designed to test the working of the designed method. Vrep simulation environment and Industrial data is used to run a simulation of the robot using the method developed in this work. We find that the method performs better compared to the previous methods in the designed scenarios. Lastly we conclude the report with the future work that can be carried out to improve and extend the algorithm.
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Ceco, Ema. "Image Analysis in the Field of Oil Contamination Monitoring." Thesis, Linköpings universitet, Datorseende, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68750.
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Monitoring wear particles in lubricating oils allows specialists to evaluate thehealth and functionality of a mechanical system. The main analysis techniquesavailable today are manual particle analysis and automatic optical analysis. Man-ual particle analysis is effective and reliable since the analyst continuously seeswhat is being counted . The drawback is that the technique is quite time demand-ing and dependent of the skills of the analyst. Automatic optical particle countingconstitutes of a closed system not allowing for the objects counted to be observedin real-time. This has resulted in a number of sources of error for the instrument.In this thesis a new method for counting particles based on light microscopywith image analysis is proposed. It has proven to be a fast and effective methodthat eliminates the sources of error of the previously described methods. Thenew method correlates very well with manual analysis which is used as a refer-ence method throughout this study. Size estimation of particles and detectionof metallic particles has also shown to be possible with the current image analy-sis setup. With more advanced software and analysis instrumentation, the imageanalysis method could be further developed to a decision based machine allowingfor declarations about which wear mode is occurring in a mechanical system.
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Upham, Ellis Linda. "Perception and displays for teleoperated robots." Doctoral diss., Orlando, Fla. : University of Central Florida, 2008. http://purl.fcla.edu/fcla/etd/CFE0002330.
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Seyyedhasani, Hasan. "INTELLIGENT UAV SCOUTING FOR FIELD CONDITION MONITORING." UKnowledge, 2018. https://uknowledge.uky.edu/ece_etds/113.
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Precision agriculture requires detailed and timely information about field condition. In less than the short flight time a UAV (Unmanned Aerial Vehicle) can provide, an entire field can be scanned at the highest allowed altitude. The resulting NDVI (Normalized Difference Vegetation Index) imagery can then be used to classify each point in the field using a FIS (Fuzzy Inference System). This identifies areas that are expected to be similar, but only closer inspection can quantify and diagnose crop properties. In the remaining flight time, the goal is to scout a set of representative points maximizing the quality of actionable information about the field condition. This quality is defined by two new metrics: the average sampling probability (ASP) and the total scouting luminance (TSL). In simulations, the scouting flight plan created using a GA (Genetic Algorithm) significantly outperformed plans created by grid sampling or human experts, obtaining over 99% ASP while improving TSL by an average of 285%.
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Azhari, Faris. "Automated crack detection and characterisation from 3D point clouds of unstructured surfaces." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/234510/1/Faris_Azhari_Thesis.pdf.
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This thesis proposes a novel automated crack detection and characterisation method on unstructured surfaces using 3D point cloud. Crack detection on unstructured surfaces poses a challenge compared to flat surfaces such as pavements and concrete, which typically utilise image-based sensors. The detection method utilises a point cloud-based deep learning method to perform point-wise classification. The detected points are then automatically characterised to estimate the detected cracks’ properties such as width profile, orientation, and length. The proposed method enables the deployment of autonomous systems to conduct reliable surveys in environments risky to humans.
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Stanislas, Leo. "Detecting airborne particles in sensor data with deep learning for robust robot perception in adverse environments." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200382/1/Leo_Stanislas_Thesis.pdf.
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This thesis presents a novel method to detect airborne particles such as dust, fog, or smoke, in the data from LiDAR sensors and stereo cameras, two types of perception sensors commonly used in robotics. The proposed approach uses deep learning classification and stochastic data fusion to detect and correctly interpret sensor data points impacted by airborne particles. The work from this thesis will enable robots to reliably perform complex tasks in challenging and unpredictable environments such as mines, agricultural fields, or roads, including in adverse weather conditions.
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Hojnik, Tim. "Dynamically configurable centre of rotation wheels." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/212361/1/Tim_Hojnik_Thesis.pdf.
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This thesis reinvents the wheel to develop a locomotion system that exhibits high efficiency and exceptional obstacle clearing ability, the Posable Hub. This is achieved by using a rigid rim with an actively movable centre hub, through the use of linear actuators. The centre hub can be adjusted in a number of ways, exhibiting functionality that ultimately increases the wheel’s traversability.
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Ramos, Fabio Tozeto. "Recognising, Representing and Mapping Natural Features in Unstructured Environments." Australian Centre for Field Robotics, Department of Aerospace, Mechanical and Mechatronic Engineering, 2008. http://hdl.handle.net/2123/2322.
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Doctor of Philosophy (PhD)This thesis addresses the problem of building statistical models for multi-sensor perception in unstructured outdoor environments. The perception problem is divided into three distinct tasks: recognition, representation and association. Recognition is cast as a statistical classification problem where inputs are images or a combination of images and ranging information. Given the complexity and variability of natural environments, this thesis investigates the use of Bayesian statistics and supervised dimensionality reduction to incorporate prior information and fuse sensory data. A compact probabilistic representation of natural objects is essential for many problems in field robotics. This thesis presents techniques for combining non-linear dimensionality reduction with parametric learning through Expectation Maximisation to build general representations of natural features. Once created these models need to be rapidly processed to account for incoming information. To this end, techniques for efficient probabilistic inference are proposed. The robustness of localisation and mapping algorithms is directly related to reliable data association. Conventional algorithms employ only geometric information which can become inconsistent for large trajectories. A new data association algorithm incorporating visual and geometric information is proposed to improve the reliability of this task. The method uses a compact probabilistic representation of objects to fuse visual and geometric information for the association decision. The main contributions of this thesis are: 1) a stochastic representation of objects through non-linear dimensionality reduction; 2) a landmark recognition system using a visual and ranging sensors; 3) a data association algorithm combining appearance and position properties; 4) a real-time algorithm for detection and segmentation of natural objects from few training images and 5) a real-time place recognition system combining dimensionality reduction and Bayesian learning. The theoretical contributions of this thesis are demonstrated with a series of experiments in unstructured environments. In particular, the combination of recognition, representation and association algorithms is applied to the Simultaneous Localisation and Mapping problem (SLAM) to close large loops in outdoor trajectories, proving the benefits of the proposed methodology.
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Ramos, Fabio Tozeto. "Recognising, Representing and Mapping Natural Features in Unstructured Environments." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/2322.
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This thesis addresses the problem of building statistical models for multi-sensor perception in unstructured outdoor environments. The perception problem is divided into three distinct tasks: recognition, representation and association. Recognition is cast as a statistical classification problem where inputs are images or a combination of images and ranging information. Given the complexity and variability of natural environments, this thesis investigates the use of Bayesian statistics and supervised dimensionality reduction to incorporate prior information and fuse sensory data. A compact probabilistic representation of natural objects is essential for many problems in field robotics. This thesis presents techniques for combining non-linear dimensionality reduction with parametric learning through Expectation Maximisation to build general representations of natural features. Once created these models need to be rapidly processed to account for incoming information. To this end, techniques for efficient probabilistic inference are proposed. The robustness of localisation and mapping algorithms is directly related to reliable data association. Conventional algorithms employ only geometric information which can become inconsistent for large trajectories. A new data association algorithm incorporating visual and geometric information is proposed to improve the reliability of this task. The method uses a compact probabilistic representation of objects to fuse visual and geometric information for the association decision. The main contributions of this thesis are: 1) a stochastic representation of objects through non-linear dimensionality reduction; 2) a landmark recognition system using a visual and ranging sensors; 3) a data association algorithm combining appearance and position properties; 4) a real-time algorithm for detection and segmentation of natural objects from few training images and 5) a real-time place recognition system combining dimensionality reduction and Bayesian learning. The theoretical contributions of this thesis are demonstrated with a series of experiments in unstructured environments. In particular, the combination of recognition, representation and association algorithms is applied to the Simultaneous Localisation and Mapping problem (SLAM) to close large loops in outdoor trajectories, proving the benefits of the proposed methodology.
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English, Andrew R. "Visually-aided localisation for autonomous agricultural vehicles." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/112916/1/Andrew_English_Thesis.pdf.
Full textAbstract:
This thesis presents an approach to visually-aided navigation of agricultural robots in cropping fields. In doing so it developed several novel visually crop-row tracking methods, along with sensor fusion methods enable practical, reliable and cost effective localisation systems suitable for navigating future fleets of agricultural robots.
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Migliore, Shane A. "Control of robotic joints using principles from the equilibrium point hypothesis of animal motor control." Available online, Georgia Institute of Technology, 2004:, 2004. http://etd.gatech.edu/theses/available/etd-05202004-001736/unrestricted/migliore%5Fshane%5Fa%5F200407%5Fms.pdf.
Full textAbstract:
Thesis (M.S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2005. Directed by Stephen DeWeerth.Dr. Stephen DeWeerth, Committee Chair ; Dr. Robert Butera, Committee Member ; Dr. Lena Ting, Committee Member. Includes bibliographical references.
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Silver, David. "Learning Preference Models for Autonomous Mobile Robots in Complex Domains." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/551.
Full textAbstract:
Achieving robust and reliable autonomous operation even in complex unstructured environments is a central goal of field robotics. As the environments and scenarios to which robots are applied have continued to grow in complexity, so has the challenge of properly defining preferences and tradeoffs between various actions and the terrains they result in traversing. These definitions and parameters encode the desired behavior of the robot; therefore their correctness is of the utmost importance. Current manual approaches to creating and adjusting these preference models and cost functions have proven to be incredibly tedious and time-consuming, while typically not producing optimal results except in the simplest of circumstances.
This thesis presents the development and application of machine learning techniques that automate the construction and tuning of preference models within complex mobile robotic systems. Utilizing the framework of inverse optimal control, expert examples of robot behavior can be used to construct models that generalize demonstrated preferences and reproduce similar behavior. Novel learning from demonstration approaches are developed that offer the possibility of significantly reducing the amount of human interaction necessary to tune a system, while also improving its final performance. Techniques to account for the inevitability of noisy and imperfect demonstration are presented, along with additional methods for improving the efficiency of expert demonstration and feedback.
The effectiveness of these approaches is confirmed through application to several real world domains, such as the interpretation of static and dynamic perceptual data in unstructured environments and the learning of human driving styles and maneuver preferences. Extensive testing and experimentation both in simulation and in the field with multiple mobile robotic systems provides empirical confirmation of superior autonomous performance, with less expert interaction and no hand tuning. These experiments validate the potential applicability of the developed algorithms to a large variety of future mobile robotic systems.
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Sabaliauskas, Rokas. "Tikslo paieškos navigacija naudojant aplinkos žymėjimą." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2009~D_20090811_132201-69608.
Full textAbstract:
Magistrinio darbo tikslas – įgyvendinti, graužikų biologinio, aplinkos žymėjimo kvapais ir navigacijos, proceso modelį robotikos srityje. Graužikai aplinkoje orientuojasi analizuodami informaciją gautą iš skirtingų penkių jutimo organų. Regėjimas padeda suvokti aplinkos erdvę, reljefą, atstumus iki objektų. Klausa, suteikia informaciją apie aplink esančius garso šaltinius, besiartinančius gyvūnus. Lytėjimas, padeda nustatyti objekto paviršiaus savybes: karštas ar šaltas, drėgnas ar sausas, minkštas ar kietas. Uoslė ir skonio receptoriai naudojami nustatyti maisto šaltiniui ar teritorijos riboms. Realiame biologiniame pasaulyje gyvūnai gali lengvai analizuoti informaciją gautą iš skirtingų jutimo organų ir ją derinti erdvinės navigacijos procese. Dirbtinėje sistemoje šie duomenys yra gaunami iš sensorių pasižyminčių skirtingomis savybėmis ir techniniais parametrais. Projekto tikslas - išspręsti robotikos uždavinį: tikslo paieškos navigacija naudojant aplinkos žymėjimą. Sukurta sistema imituoja graužiko erdvinės navigacijos procesą naudojant du skirtingus jutimo sensorius: dirbtinę regą ir dirbtinę uoslę. Projekte pristatoma sukurta sistema ir realizuoti erdvinės navigacijos algoritmai naudojantys vaizdinę ir kvapo informaciją. Aprašoma eksperimento vykdymo metodika ir priemonės.The aim of the master thesis is to implement, rodent’s biological, goal directed navigation based on self-marking process in robotics. Rodents in environment navigate using analyzed information that gets from five different senses. Seeing helps to realize environment’s space, relief, distance from the objects. Hearing sense gives information about sound position of sound sources or other animals. Touch sense helps to determine properties of the surface: is it cold or hot, dry or wet, soft or hard. Olfactory and flavor receptors are used to determine food source or territory range. In real biological world animals could easily analyze and combine information from different sense receptors in spatial navigation process. In artificial system this data we are getting from different type and characteristics sensors. The main aim of this project is to solve robotic task: goal directed navigation based on self -marking method. Created system simulates rodent’s spatial navigation process where in two sensors are used: artificial eyes and artificial nose. This project presents created system and implemented spatial navigation algorithms which are using video and odor tracking based information. The methodology and tools used in experiment are described in this paper.
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Li, Hongjun. "Mapping in uncertain environments for mobile robots." Doctoral thesis, Universidade de Évora, 2019. http://hdl.handle.net/10174/26154.
Full textAbstract:
Um dos problemas fundamentais em robótica móvel é o problema da localização e mapeamento, no qual
um robô se deve localizar ao mesmo tempo que constrói um mapa do ambiente. Existem diversas técnicas
para abordar este problema. Neste trabalho propõem-se abordagens novas para a construção do mapa em
ambientes estáticos e dinâmicos, assumindo pose conhecida.
As abordagens propostas baseiam-se em campos aleatórios de Markov (Markov random fields - MRF) e em
campos aleatórios Gaussianos (Gaussian random fields - GRF), seguindo um ponto de vista Bayesiano, onde
as distribuições de probabilidade a priori são usadas como regularizadores. Num ambiente estático, cada
ponto do espaço é descrito pela sua probabilidade de ocupação. O primeiro método proposto é um filtro
baseado nos MRF, que se centra no ruído das medidas e que pode ser implementado em linha (tempo real).
O segundo método é um método preditivo baseado nos MRF que permite também estimar a probabilidade
de ocupação do espaço não observado. Em ambos os métodos, os mapas são construídos numa grelha de
células. Outra abordagem baseia-se num espaço contínuo, baseado em GRF onde se propõe um método
recursivo de modo a reduzir a complexidade computacional.
No caso de ambientes dinâmicos, a probabilidade de ocupação é substituída pelas probabilidade de transição
duma cadeia de Markov para descrever o comportamento dinâmico de cada ponto. Nesta abordagem são
propostos dois métodos para os ambientes dinâmicos, igualmente baseados nos MRF e nos GRF. No método
com MRF todos os parâmetros são estimados em conjunto. Pelo contrário, com os GRF os parâmetros são
divididos em dois sub-conjuntos de modo a reduzir a complexidade computacional.
Todos os métodos propostos são testados e apresentam-se resultados em simulação nos respetivos capítulos.
Finalmente estes algoritmos são também validados em ambiente experimental. Nestas experiências, as
poses não podem ser medidas com precisão e é tida em consideração a incerteza na pose do robô.
Quando comparados com o estado da arte, os métodos propostos resolvem as inconsistências nos mapas
tendo em consideração a dependência entre pontos vizinhos. Este processo é realizado usando MRF e
GRF em vez de assumir independência. As simulações e os resultados experimentais demonstram que os
métodos propostos podem, não apenas lidar com as inconsistências nos mapas construídos, mas também
tirar proveito da correlação espacial para prever o espaço não observado; Abstract:
Mapping in Uncertain Environments for Mobile
Robots
One of the fundamental problems in robotics is the localization and mapping problem, where a robot has to
localize itself while building a map of the environment. Several techniques exist to tackle this problem. This
work proposes novel mapping approaches with known robot poses for static and dynamic environments.
The proposed techniques are based on Markov random fields (MRFs) and Gaussian random fields (GRFs),
following a Bayesian viewpoint where prior distributions are provided as regularizers. In static environments,
every point is described by its occupancy probability. The first proposed method is an MRF-based filter,
which focuses on the measurement noise and can be implemented online (realtime). The second one
is an MRF-based prediction method, which can also be used to estimate the occupancy probability for
unobserved space. In both methods, the maps are organized as a grid. Another approach, which works in
continuous space, is based on a GRF prediction method, and a recursive algorithm is proposed to reduce
the computational complexity.
In the case of dynamic environments, the occupancy probability is replaced by transition probabilities of a
Markov chain that describe the dynamic behaviour of each point. Two methods for dynamic environments
are proposed, also based on MRFs and GRFs. In the MRF-based method, all the parameters are jointly
estimated. In contrast, in the GRF-based method, the parameters are divided into two subsets to reduce
the computational complexity.
All the proposed methods are tested in simulations in the corresponding chapters. Finally, these algorithms
are also validated on an experimental platform. In the experimental environments, robot poses cannot be
measured precisely, and so the uncertainty of robot poses is also considered.
When compared with the state of the art for dynamic environments, the proposed methods tackle the
inconsistencies in the maps by considering dependence between neighbour points. This is done using MRFs
and GRFs instead of assuming independence. The simulations and the experimental results demonstrate
that the proposed methods can, not only deal with the inconsistency in the built maps, but also take
advantage of the spatial correlation to predict unobserved space.
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Maeda, Guilherme Jorge. "Learning and Reacting with Inaccurate Prediction: Applications to Autonomous Excavation." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9460.
Full textAbstract:
Motivated by autonomous excavation, this work investigates solutions to a class of problem where disturbance prediction is critical to overcoming poor performance of a feedback controller, but where the disturbance prediction is intrinsically inaccurate. Poor feedback controller performance is related to a fundamental control problem: there is only a limited amount of disturbance rejection that feedback compensation can provide. It is known, however, that predictive action can improve the disturbance rejection of a control system beyond the limitations of feedback. While prediction is desirable, the problem in excavation is that disturbance predictions are prone to error due to the variability and complexity of soil-tool interaction forces. This work proposes the use of iterative learning control to map the repetitive components of excavation forces into feedforward commands. Although feedforward action shows useful to improve excavation performance, the non-repetitive nature of soil-tool interaction forces is a source of inaccurate predictions. To explicitly address the use of imperfect predictive compensation, a disturbance observer is used to estimate the prediction error. To quantify inaccuracy in prediction, a feedforward model of excavation disturbances is interpreted as a communication channel that transmits corrupted disturbance previews, for which metrics based on the sensitivity function exist. During field trials the proposed method demonstrated the ability to iteratively achieve a desired dig geometry, independent of the initial feasibility of the excavation passes in relation to actuator saturation. Predictive commands adapted to different soil conditions and passes were repeated autonomously until a pre-specified finish quality of the trench was achieved. Evidence of improvement in disturbance rejection is presented as a comparison of sensitivity functions of systems with and without the use of predictive disturbance compensation.
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Bender, Asher. "Autonomous Exploration of Large-Scale Natural Environments." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9450.
Full textAbstract:
This thesis addresses issues which arise when using robotic platforms to explore large-scale, natural environments. Two main problems are identified: the volume of data collected by autonomous platforms and the complexity of planning surveys in large environments. Autonomous platforms are able to rapidly accumulate large data sets. The volume of data that must be processed is often too large for human experts to analyse exhaustively in a practical amount of time or in a cost-effective manner. This burden can create a bottleneck in the process of converting observations into scientifically relevant data. Although autonomous platforms can collect precisely navigated, high-resolution data, they are typically limited by finite battery capacities, data storage and computational resources. Deployments are also limited by project budgets and time frames. These constraints make it impractical to sample large environments exhaustively. To use the limited resources effectively, trajectories which maximise the amount of information gathered from the environment must be designed. This thesis addresses these problems. Three primary contributions are presented: a new classifier designed to accept probabilistic training targets rather than discrete training targets; a semi-autonomous pipeline for creating models of the environment; and an offline method for autonomously planning surveys. These contributions allow large data sets to be processed with minimal human intervention and promote efficient allocation of resources. In this thesis environmental models are established by learning the correlation between data extracted from a digital elevation model (DEM) of the seafloor and habitat categories derived from in-situ images. The DEM of the seafloor is collected using ship-borne multibeam sonar and the in-situ images are collected using an autonomous underwater vehicle (AUV). While the thesis specifically focuses on mapping and exploring marine habitats with an AUV, the research applies equally to other applications such as aerial and terrestrial environmental monitoring and planetary exploration.
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Campanholo, Guizilini Vitor. "Non-Parametric Learning for Monocular Visual Odometry." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9903.
Full textAbstract:
This thesis addresses the problem of incremental localization from visual information, a scenario commonly known as visual odometry. Current visual odometry algorithms are heavily dependent on camera calibration, using a pre-established geometric model to provide the transformation between input (optical flow estimates) and output (vehicle motion estimates) information. A novel approach to visual odometry is proposed in this thesis where the need for camera calibration, or even for a geometric model, is circumvented by the use of machine learning principles and techniques. A non-parametric Bayesian regression technique, the Gaussian Process (GP), is used to elect the most probable transformation function hypothesis from input to output, based on training data collected prior and during navigation. Other than eliminating the need for a geometric model and traditional camera calibration, this approach also allows for scale recovery even in a monocular configuration, and provides a natural treatment of uncertainties due to the probabilistic nature of GPs. Several extensions to the traditional GP framework are introduced and discussed in depth, and they constitute the core of the contributions of this thesis to the machine learning and robotics community. The proposed framework is tested in a wide variety of scenarios, ranging from urban and off-road ground vehicles to unconstrained 3D unmanned aircrafts. The results show a significant improvement over traditional visual odometry algorithms, and also surpass results obtained using other sensors, such as laser scanners and IMUs. The incorporation of these results to a SLAM scenario, using a Exact Sparse Information Filter (ESIF), is shown to decrease global uncertainty by exploiting revisited areas of the environment. Finally, a technique for the automatic segmentation of dynamic objects is presented, as a way to increase the robustness of image information and further improve visual odometry results.
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Gerardo-Castro, Marcos Paul. "Robust Multiple-Sensing-Modality Data Fusion for Reliable Perception in Outdoor Scenarios." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/18135.
Full textAbstract:
To be reliable in outdoor operations, perception systems should use a combination of sensing modalities, such as laser, radar, visual camera, or IR camera, since they respond differently to distinct environmental conditions. In this thesis, a novel multisensory data fusion framework designed to appropriately combine data acquired by multiple exteroceptive sensing modalities in field robotics scenarios is presented. Compared with the traditional data fusion methods, the proposed framework will account for the fact that sensors perceive the environment in different ways. To this end, it is proposed to automatically detect commonalities and discrepancies between the data sources before performing data fusion or combination. The consistency test evaluates dependencies between data from multiple-sensing-modalities or representations and chooses intelligently the data that most likely leads to synergy. The data fusion methods described in this thesis create high fidelity representations taking advantage of the full potential of each sensing modality leading to resilient perception system. This thesis introduces a data fusion with a parametric consistency test framework for multiple sensing modalities using Gaussian process data fusion. This approach avoids the data quality degradation inherent to fusing multiple types of sensor data by using a parametric consistency test. In addition, a non-parametric consistency test for multiple-sensing-modalities using Gaussian process data fusion is presented. This approach avoids local geometric threshold parameters and can be more discriminatory because it takes into account the global model. An experimental analysis was evaluated for simulations and for real-world implementation using laser and radar sensor in different environmental conditions. More specifically the performance of 3D surface reconstruction in the context of field robotics was tested in a variety of scenarios. It was demonstrated that by identifying discrepancies and exploiting data commonalities between the data of each sensing modality the framework creates a resilient perception system to adverse environmental conditions.
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Migliore, Shane Anthony. "Control of robotic joints using principles from the equilibrium point hypothesis of animal motor control." Thesis, Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5009.
Full textAbstract:
Biological systems are able to perform complex movements with high energy-efficiency and, in general, can adapt to environmental changes more elegantly than traditionally engineered mechanical
systems. The Equilibrium Point Hypothesis describes animal motor control as trajectories of
equilibrium joint angle and joint stiffness. Traditional approaches to robot design are unable to implement this control scheme because they lack joint actuation methods that can control mechanical stiffness, and, in general, they are unable to take advantage of energy introduced into the system by the environment. In this paper, we describe the development and implementation of an FPGA-controlled, servo-actuated robotic joint that incorporates series-elastic actuation with specially developed nonlinear springs. We show that the joint's equilibrium angle and stiffness are independently controllable and that their independence is not lost in the presence of external joint torques. This approach to joint control emulates the behavior of antagonistic muscles, and thus produces a mechanical system that demonstrates biological similarity both in its observable
output and in its method of control.
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Burke, Jennifer L. "Moonlight in Miami [electronic resource] : a field study of human-robot interaction in the context of an urban search and rescue disaster response training exercise / by Jennifer L. Burke." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000220.
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Sofman, Boris. "Online Learning Techniques for Improving Robot Navigation in Unfamiliar Domains." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/43.
Full textAbstract:
Many mobile robot applications require robots to act safely and intelligently in complex unfamiliarenvironments with little structure and limited or unavailable human supervision. As arobot is forced to operate in an environment that it was not engineered or trained for, various aspectsof its performance will inevitably degrade. Roboticists equip robots with powerful sensorsand data sources to deal with uncertainty, only to discover that the robots are able to make onlyminimal use of this data and still find themselves in trouble. Similarly, roboticists develop andtrain their robots in representative areas, only to discover that they encounter new situations thatare not in their experience base. Small problems resulting in mildly sub-optimal performance areoften tolerable, but major failures resulting in vehicle loss or compromised human safety are not.This thesis presents a series of online algorithms to enable a mobile robot to better deal withuncertainty in unfamiliar domains in order to improve its navigational abilities, better utilizeavailable data and resources and reduce risk to the vehicle. We validate these algorithms throughextensive testing onboard large mobile robot systems and argue how such approaches can increasethe reliability and robustness of mobile robots, bringing them closer to the capabilitiesrequired for many real-world applications.
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Kingry, Nathaniel. "Heuristic Optimization and Sensing Techniques for Mission Planning of Solar-Powered Unmanned Ground Vehicles." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523874767812408.
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Woodford, Oliver J. "Priors for new view synthesis." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:38962bda-6f0f-4158-89cf-8c641ebac486.
Full textAbstract:
New view synthesis (NVS) is the problem of generating a novel image of a scene, given a set of calibrated input images of the scene, i.e. their viewpoints, and also that of the output image, are known. The problem is generally ill-posed---a large number of scenes can generate a given set of images, therefore there may be many equally likely (given the input data) output views. Some of these views will look less natural to a human observer than others, so prior knowledge of natural scenes is required to ensure that the result is visually plausible. The aim of this thesis is to compare and improve upon the various Markov random field} and conditional random field prior models, and their associated maximum a posteriori optimization frameworks, that are currently the state of the art for NVS and stereo (itself a means to NVS). A hierarchical example-based image prior is introduced which, when combined with a multi-resolution framework, accelerates inference by an order of magnitude, whilst also improving the quality of rendering. A parametric image prior is tested using a number of novel discrete optimization algorithms. This general prior is found to be less well suited to the NVS problem than sequence-specific priors, generating two forms of undesirable artifact, which are discussed. A novel pairwise clique image prior is developed, allowing inference using powerful optimizers. The prior is shown to perform better than a range of other pairwise image priors, distinguishing as it does between natural and artificial texture discontinuities. A dense stereo algorithm with geometrical occlusion model is converted to the task of NVS. In doing so, a number of challenges are novelly addressed; in particular, the new pairwise image prior is employed to align depth discontinuities with genuine texture edges in the output image. The resulting joint prior over smoothness and texture is shown to produce cutting edge rendering performance. Finally, a powerful new inference framework for stereo that allows the tractable optimization of second order smoothness priors is introduced. The second order priors are shown to improve reconstruction over first order priors in a number of situations.
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Williams, Stephen Vincent. "Visual arctic navigation: techniques for autonomous agents in glacial environments." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41135.
Full textAbstract:
Arctic regions are thought to be more sensitive to climate change fluctuations, making weather data from these regions more valuable for climate modeling. Scientists have expressed an interest in deploying a robotic sensor network in these areas, minimizing the exposure of human researchers to the harsh environment, while allowing dense, targeted data collection to commence. For any such robotic system to be successful, a certain set of base navigational functionality must be developed. Further, these navigational algorithms must rely on the types of low-cost sensors that would be viable for use in a multi-agent system. A set of vision-based processing techniques have been proposed, which augment current robotic technologies for use in glacial terrains. Specifically, algorithms for estimating terrain traversability, robot localization, and terrain reconstruction have been developed which use data collected exclusively from a single camera and other low-cost robotic sensors. For traversability assessment, a custom algorithm was developed that uses local scale surface texture to estimate the terrain slope. Additionally, a horizon line estimation system has been proposed that is capable of coping with low-contrast, ambiguous horizons. For localization, a monocular simultaneous localization and mapping (SLAM) filter has been fused with consumer-grade GPS measurements to produce full robot pose estimates that do not drift over long traverses. Finally, a terrain reconstruction methodology has been proposed that uses a Gaussian process framework to incorporate sparse SLAM landmarks with dense slope estimates to produce a single, consistent terrain model. These algorithms have been tested within a custom glacial terrain computer simulation and against multiple data sets acquired during glacial field trials. The results of these tests indicate that vision is a viable sensing modality for autonomous glacial robotics, despite the obvious challenges presented by low-contrast glacial scenery. The findings of this work are discussed within the context of the larger arctic sensor network project, and a direction for future work is recommended.
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Eich, Markus [Verfasser], Frank [Akademischer Betreuer] Kirchner, and Bernd [Akademischer Betreuer] Krieg-Brückner. "Marine Vessel Inspection as a Novel Field for Service Robotics: A Contribution to Systems, Control Methods and Semantic Perception Algorithms / Markus Eich. Gutachter: Frank Kirchner ; Bernd Krieg-Brückner. Betreuer: Frank Kirchner." Bremen : Staats- und Universitätsbibliothek Bremen, 2013. http://d-nb.info/1072078546/34.
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Santos, Luciano Antonio Frezzatto 1986. "Planejamento de trajetórias e implementação de técnicas de posicionamento de eixos para dispositivo CNC com arquitetura de controle aberta." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265316.
Full textAbstract:
Orientador: João Maurício RosárioDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica
Made available in DSpace on 2018-08-18T19:32:48Z (GMT). No. of bitstreams: 1 Santos_LucianoAntonioFrezzatto_M.pdf: 5750912 bytes, checksum: c4a22f3fc8b9c19bf5c3cac9557903c4 (MD5) Previous issue date: 2011
Resumo: Máquinas-ferramenta CNC são dispositivos complexos que executam movimentos automáticos, precisos e consistentes. Com o propósito de aprimorar o desempenho destes dispositivos face às mudanças bruscas de parâmetros agregados a posição, velocidade e aceleração no decurso de determinada trajetória torna-se imprescindível o estudo de novas arquiteturas de controle. A automação flexível permite que dispositivos robóticos mostrem-se cada vez mais velozes, motivando o desenvolvimento de técnicas que possibilitem de modo eficiente a substituição de controladores clássicos do tipo PID. Este estudo propôs-se a implementar um método alternativo para acionamento de um dispositivo cartesiano CNC de alta precisão com ênfase no desenvolvimento de um ambiente computacional para planejamento de movimentos e síntese de controladores. A geração de movimentos foi realizada por meio de um método de interpolação por splines que garantiu a continuidade e a suavidade dos perfis de trajetória. Para garantir que tais perfis fossem corretamente executados pelo dispositivo cartesiano, dois métodos de controle baseados no modelo do sistema foram implementados de forma a estabelecer comparação entre seus desempenhos. Em seguida, concebeu-se um ambiente de simulação com arquitetura aberta e flexível, o qual permitiu validar as estratégias propostas além de analisar a influência que perturbações externas causavam sobre o comportamento do dispositivo cartesiano. Tendo as estratégias sido validadas, procedeu-se à implementação em dispositivo físico utilizando uma placa FPGA para embarcar os controladores sintetizados. Os resultados mostraram que as estratégias propostas apresentaram bom desempenho quando empregadas junto ao dispositivo cartesiano CNC. Outrossim, o ferramental desenvolvido no estudo é suficientemente genérico para ser estendido a outras aplicações utilizando máquinas-ferramenta CNC
Abstract: CNC machine tools are complex devices that execute automatic, precise and consistent movements. Aiming to improve the performance of these devices due to sudden changes on parameters associated to position, speed and acceleration while performing a given trajectory, it becomes indispensable the study of new control architectures. The flexible automation allows faster robotic devices, motivating the development of techniques that efficiently substitute classic PID controllers. This study aimed to implement an alternative method for driving a high-precision CNC cartesian device focusing on the development of a computing environment for movement planning and synthesis of controllers. The movement generation was performed by means of a spline interpolation method that ensured the continuity and smoothness of trajectory profiles. To ensure that these profiles were correctly executed by the cartesian device, two control techniques based on the model of the system were implemented in order to establish comparisons between their performances. Then, a simulation environment with open and flexible architecture was conceived, which allowed the validation of the proposed techniques besides analyzing the influence of external disturbances on the cartesian device behavior. Having the strategies been validated, the implementation in physical device was done using a FPGA board to embed the synthesized controllers. The results showed that the proposed strategies had good performance when applied to the CNC cartesian device. Furthermore, the tools developed in the study are general enough to be extended to other applications using CNC machine tools
Mestrado
Mecanica dos Sólidos e Projeto Mecanico
Mestre em Engenharia Mecânica
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Wigmore, Oliver Henry Wigmore. "Assessing Spatiotemporal Variability in Glacial Watershed Hydrology: Integrating Unmanned Aerial Vehicles and Field Hydrology, Cordillera Blanca, Peru." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471854919.
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Tsai, Dorian Yu Peng. "Light-field features for robotic vision in the presence of refractive objects." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/192102/1/Dorian%20Yu%20Peng_Tsai_Thesis.pdf.
Full textAbstract:
Curved transparent objects are difficult for robots to perceive and this makes it difficult for robots to work with them. This thesis shows that multi-aperture or light-field cameras overcome this problem since they capture a set of dense and uniformly sampled views to capture multiple views of the scene. The advances constitute a critical step towards enabling robots to work more safely and reliably with everyday refractive objects.
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Canelhas, Daniel Ricão. "Truncated Signed Distance Fields Applied To Robotics." Doctoral thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-59369.
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This thesis is concerned with topics related to dense mapping of large scale three-dimensional spaces. In particular, the motivating scenario of this work is one in which a mobile robot with limited computational resources explores an unknown environment using a depth-camera. To this end, low-level topics such as sensor noise, map representation, interpolation, bit-rates, compression are investigated, and their impacts on more complex tasks, such as feature detection and description, camera-tracking, and mapping are evaluated thoroughly. A central idea of this thesis is the use of truncated signed distance fields (TSDF) as a map representation and a comprehensive yet accessible treatise on this subject is the first major contribution of this dissertation. The TSDF is a voxel-based representation of 3D space that enables dense mapping with high surface quality and robustness to sensor noise, making it a good candidate for use in grasping, manipulation and collision avoidance scenarios. The second main contribution of this thesis deals with the way in which information can be efficiently encoded in TSDF maps. The redundant way in which voxels represent continuous surfaces and empty space is one of the main impediments to applying TSDF representations to large-scale mapping. This thesis proposes two algorithms for enabling large-scale 3D tracking and mapping: a fast on-the-fly compression method based on unsupervised learning, and a parallel algorithm for lifting a sparse scene-graph representation from the dense 3D map. The third major contribution of this work consists of thorough evaluations of the impacts of low-level choices on higher-level tasks. Examples of these are the relationships between gradient estimation methods and feature detector repeatability, voxel bit-rate, interpolation strategy and compression ratio on camera tracking performance. Each evaluation thus leads to a better understanding of the trade-offs involved, which translate to direct recommendations for future applications, depending on their particular resource constraints.
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Guerra, Matteo. "Le déploiement et l'évitement d'obstacles en temps fini pour robots mobiles à roues." Thesis, Ecole centrale de Lille, 2015. http://www.theses.fr/2015ECLI0024/document.
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Ce travail traite de l'évitement d'obstacles pour les robots mobiles à roues. D’abord, deux solutions sont proposées dans le cas d’un seul robot autonome. La première est une amélioration de la technique des champs de potentiel afin de contraster l’apparition de minima locaux. Le résultat se base sur l’application de la définition de l’ «Input-to-State Stability» pour des ensembles décomposables. Chaque fois que le robot mobile approche un minimum local l’introduction d’un contrôle dédié lui permet de l’éviter et de terminer la tâche. La deuxième solution se base sur l’utilisation de la technique du «Supervisory Control» qui permet de diviser la tâche principale en deux sous tâches : un algorithme de supervision gère deux signaux de commande, le premier en charge de faire atteindre la destination, le deuxième d’éviter les obstacles. Les deux signaux de commande permettent de compléter la mission en temps fini en assurant la robustesse par rapport aux perturbations représentant certaines dynamiques négligées. Les deux solutions ont été mises en service sur un robot mobile «Turtlebot 2». Pour contrôler une formation de type leader-follower qui puisse éviter collisions et obstacles, une modification de l’algorithme de supervision précédent a été proposée ; elle divise la tâche principale en trois sous-problèmes gérés par trois lois de commande. Le rôle du leader est adapté pour être la référence du groupe avec un rôle actif : ralentir la formation en cas de manœuvre d'évitement pour certains robots. La méthode proposée permet au groupe de se déplacer et à chaque agent d’éviter les obstacles, ou les collisions, de manière décentraliséeThis dissertation work addresses the obstacle avoidance for wheeled mobile robots. The supervisory control framework coupled with the output regulation technique allowed to solve the obstacle avoidance problem and to formally prove the existence of an effective solution: two outputs for two objectives, reaching the goal and avoiding the obstacles. To have fast, reliable and robust results the designed control laws are finite-time, a particular class very appropriate to the purpose. The novelty of the approach lies in the easiness of the geometric approach to avoid the obstacle and on the formal proof provided under some assumptions. The solution have been thus extended to control a leader follower formation which, sustained from the previous result, uses two outputs but three controls to nail the problem. The Leader role is redesigned to be the reference of the group and not just the most advanced agent, moreover it has a active role slowing down the formation in case of collision avoidance manoeuvre for some robots. The proposed method, formally proven, makes the group move together and allow each agent to avoid obstacles or collision in a decentralized way. In addition, a further contribution of this dissertation, it is represented by a modification of the well known potential field method to avoid one of the common drawback of the method: the appearance of local minima. Control theory tools helps again to propose a solution that can be formally proven: the application of the definition of Input-to-State Stability (ISS) for decomposable sets allows to treat separate obstacles adding a perturbation which is able to move the trajectory away from a critic point
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Wright, Darrel W. "Pressure losses experienced by liquid flow through straight PDMS microchannels of varying diameters." Honors in the Major Thesis, University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1527.
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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.Bachelors
Engineering and Computer Science
Mechanical Engineering
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Farritor, Shane M. (Shane Michael). "On modular design and planning for field robotic systems." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9894.
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Yu, Zhan Qing. "Detection de collision en robotique." Nantes, 1987. http://www.theses.fr/1987NANT2058.
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Modelisation du robot et de son environnement grace a des primitives simples. Utilisation d'une tabulation qui indique les zones ou le robot se trouve sans intersection avec les obstacles et les zones a risques pour lesquelles une collision est possible, ceci en vue d'accelerer le calcul de detection de collision
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Daya, Bassam. "Résolution numérique des équations du champ neural : étude de la coordination du mouvement par des modèles mathématiques du cervelet." Angers, 1996. http://www.theses.fr/1996ANGE0013.
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Nous abordons le problème de la coordination du mouvement par les deux approches continues et discrètes, afin de les comparer en vue du neurocontrôle en robotique. Dans le premier chapitre, le formalisme des champs et les équations du champ pour un tissu nerveux ont été rappelés. Ces équations incluent les mécanismes physiologiques du système nerveux pour mieux tenir compte de la réalité. Les hypothèses permettant de retrouver les modèles classiques ont été déterminées, prouvant ainsi la généralité de la théorie du champ envisagée. Dans le deuxième chapitre, la résolution numérique des équations du champ est réalisée au moyen de la technique des éléments finis et des différences finies. Il est supposé que l'équation au niveau neuronal est bidimensionnelle, alors que celle du niveau synaptique est à une dimension. A la fin de ce chapitre, nous proposons des simulations pour les réseaux multicouches classiques illustrant la propagation de l'activation. Dans le troisième chapitre, nous présentons les premiers résultats de l'application du formalisme des champs au cervelet. En particulier, les propriétés concernant l'effet de la localisation géométrique des neurones et l'effet de la hiérarchie sont déduites. Dans le quatrième chapitre, nous commençons par l'étude du modèle du cervelet en boucle fermée en analysant le rôle de la fibre grimpante dont on sait qu'elle véhicule le signal d'erreur. La méthode analytique utilisée, est fondée sur une technique classique d'optimisation. Nous généralisons ensuite notre méthode. Enfin nous abordons dans une dernière section un aspect de la robotique mobile. Nous avons considéré l'exemple simplifie d'un bipède dont il faut contrôler l'équilibre dynamique par l'accélération articulaire du tronc. On montre que notre modèle permet d'apprendre à contrôler l'équilibre dynamique du système pour les trajectoires apprises et d'anticiper celles qui sont non apprises.
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Rutman, Nathaniel. "Automated design of modular field robots." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/38077.
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Jin, Zhe Kun. "Système multi-agents appliqué à la navigation d'un robot mobile dans un environnement inconnu." Cachan, Ecole normale supérieure, 1997. http://www.theses.fr/1997DENS0008.
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Cette thèse présente une étude sur la navigation d'un robot mobile dans un environnement inconnu. Nous avons débuté ce travail par un état de l'art. Par l'analyse des architectures de commande, notre choix s'est porte sur l'architecture de commande réactive base sur un système multi-agents en raison de sa flexibilité et des diverses possibilités d'évolution. Nous avons place ce travail dans le projet de robot mobile Amara (architecture multi-agents pour un robot autonome), le système de commande multi-agents est lui-même un agent de navigation dans l'ensemble du système de commande Amara. Dans le système de commande multi-agents développé, chaque agent représente un des comportements élémentaires du robot. Le comportement du robot mobile est le comportement émergé de ses comportements élémentaires. Nous avons implémenté les agents de comportement élémentaire en utilisant les données sur l'environnement délivrées par un télémètre laser panoramique. L'intégration des agents au sein du système se fait par le moyen du champ potentiel dérivé. Chaque agent fournit un gradient partiel du champ potentiel. Le gradient global est la somme pondérée de tous les gradients partiels. Un logiciel de simulation smasim (système multi-agents simulateur) sous Windows a été développé pour visualiser le comportement du robot et pour valider le choix du système. Nous l'avons utilise pour étudier le comportement du système de commande multi-agents en simulation