Dissertations / Theses on the topic 'Collision avoidance; Global path planning'

Presented in this thesis is a collision avoidance algorithm designed around an arc path model. The algorithm was designed for use on Virginia Tech robots entered in the 2003 and 2004 Intelligent Ground Vehicle Competition (IGVC) and on our 2004 entry into the DARPA Grand Challenge. The arc path model was used because of the simplicity of the calculations and because it can accurately represent the base kinematics for Ackerman or differentially steered vehicles. Clothoid curves have been used in the past to create smooth paths with continuously varying curvature, but clothoids are computationally intensive. The circular arc algorithm proposed here is designed with simplicity and versatility in mind. It is readily adaptable to ground vehicles of any size and shape. The algorithm is also designed to run with minimal tuning. The algorithm can be used as a stand alone reactive collision avoidance algorithm in simple scenarios, but it can be better optimized for speed and safety when guided by a global path planner. A complete navigation architecture is presented as an example of how obstacle avoidance can be incorporated in the algorithm.
Master of Science

This paper describes development of an optimal 3D path planner with collision avoidance for a 9 DOF robot manipulator. The application of the robot manipulator will be on an unmanned oil platform where it will be used for inspection. Most of the time the robot manipulator will follow a pre-programmed collision-free path specified by an operator. Situations where it is desirable to move the end effector from the current position to a new position without specifying the path in advance might occur. To make this possible a 3D path planner with collision avoidance is needed. The path planner presented in this paper is based on the well known Probabilistic Roadmap method (PRM). One of the main challenges using the PRM is to make a roadmap covering the entire collision free Configuration space, Cfree, and connect it into one connected component. It is shown by empirical testing that using a combination of the Bridge Sampling technique and a simple Random sampling technique gives best Coverage of the Cfree space and highest Connectivity in the roadmap for the given environment. An algorithm that increases the Connectivity and sometimes provide Maximal Connection is also described. A backup procedure that can be executed on-line if a query fails is also presented. The backup procedure is slow, but it increases the chances of succeeding a query if the goal is in a difficult area. It is also investigated if the coverage and connectivity can be further improved by using the potential field planner when connecting the waypoints. Empirical testing showed that the improvements of Coverage and Connectivity were limited and the sampling and query time increased. The query time for a roadmap containing 400 nodes and one containing 1000 nodes was compared. It turned out that a large roadmap did not necessarily affect the query time negative because it made it easier to connect the start and goal nodes. Three existing path smoothing algorithms and a new algorithm, called Deterministic Shortcut, were implemented and tested. Empirical testing showed that the Deterministic Shortcut algorithm outperformed the others when it came to path smoothing versus time.

Efficient maritime navigation with the ability to avoid obstructions is an intensive research topic for autonomous navigation in ‘practical’ Unmanned Surface Vehicles (USVs). However, only few of the existing USVs have applied path planning in their navigation systems. Most studies present validation results at the simulation level and do not consider any environmental disturbances. The aim of this research project is to develop practical and efficient path planning algorithms that can generate and optimise the path based on known (or predicted) traffic and environment data with the ability to adapt to different criteria or missions. New risk assessment strategies together with three novel path planning algorithms have been developed to process and evaluate the real-time environmental conditions, to minimise the adverse effects caused by surface currents, and to improve the safety of the generated path for those circumstances where the reliability of the fused navigational data is uncertain. All these algorithms have been tested and verified in simulations with results proving the effectiveness of path generation and low-cost of energy consumption. Experiments using a practical USV have also been carried out to validate the capabilities of the algorithms.

Linear Temporal Logic (LTL), as one of the temporal logic, can generate a fully automated correct-by-design controller synthesis approach for single or multiple autonomous vehicles, under much more complex missions than the traditional point-to-point navigation.In this master thesis, a framework which combines model- checking-based robot motion planning with action planning is proposed based on LTL for-mulas. The specifications implicitly require both sequential regions for multi-agent to visit and the desired actions to perform at these regions while avoid-ing collision with each other and fixed obstacles. The high level motion and task planning and low level navigation function based collision avoidance controller are verified by nontrivial simulation and implementation on real quadcopter in Smart Mobility Lab.

Unmanned vehicle systems, specifically Unmanned Air Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have found potential use in both military and civilian applications. For many applications, unmanned vehicle systems are required to navigate in urban environments where obstacles with various types and sizes exist. The main contribution of this research is to offer vision-based path planning, collision avoidance, and target tracking strategies for Unmanned Air and Ground vehicles operating in urban environments. Two vision-based local-level frame mapping and planning techniques are first developed for Miniature Air Vehicles (MAVs). The techniques build maps and plan paths in the local-level frame of MAVs directly using the camera measurements without transforming to the inertial frame. Using a depth map of an environment obtained by computer vision methods, the first technique employs an extended Kalman Filter (EKF) to estimate the range, azimuth to, and height of obstacles, and constructs local spherical maps around MAVs. Based on the maps, the Rapidly-Exploring Random Tree (RRT) algorithm is used to plan collision-free Dubins paths. The second technique constructs local multi-resolution maps using an occupancy grid, which give higher resolution to the areas that are close to MAVs and give lower resolution to the areas that are far away. The maps are built using a log-polar representation. The two planning techniques are demonstrated in simulation and flight tests. Based on the observation that a camera does not provide accurate time-to-collision (TTC) measurements, two and three dimensional observability-based planning algorithms are explored. The techniques estimate both TTC and bearing using bearing-only measurements. A nonlinear observability analysis of state estimation process is conducted to obtain the conditions for complete observability of the system. Using the conditions, the observability-based planning algorithms are designed to minimize the estimation uncertainties while simultaneously avoiding collisions. The two dimensional planning algorithm parameterizes an obstacle using TTC and azimuth, and constructs local polar maps. The three dimensional planning algorithm parameterizes an obstacle using inverse TTC, azimuth, and elevation, and constructs local spherical maps. The algorithms are demonstrated in simulation. Lastly, a probabilistic path planning algorithm is developed for tracking a moving target in urban environments using UAVs and UGVs. The algorithm takes into account occlusions due to obstacles. It models the target using a dynamic occupancy grid and updates the target location using a Bayesian filter. Based on the target's current and probable future locations, a decentralized path planning algorithm is designed to generate suboptimal paths that maximize the sum of the joint probability of detection for all vehicles over a finite look-ahead horizon. Results demonstrate the planning algorithm is successful in solving the moving target tracking problem in urban environments.

By the year 2020 the number of aircraft will have increased substantially and will be in �Free Flight�(that is, ATC will be devolved to the aircraft rather than being ground based). As an aid to navigation a more advanced form of collision avoidance will be required. This thesis proposes a method of collision avoidance planning using Automatic Dependent Surveillance-Broadcast (ADS-B) and Dynamic Programming (DP). It in essence enables Air Traffic Control (ATC) from within the cockpit for remote or uncontrolled airspace and is a step toward Free Flight. Free Flight requires quite different strategies than those used in the present collision avoidance schemes. This thesis reviews the approaches to collision avoidance used in the Air traffic navigation and to similar problems in other industries. In particular it considers the extended problem of collision avoidance within the framework of path planning. This is a key departure from the approach to aircraft collision avoidance used in the industry to date. Path planning reflects the real goal of an aircraft, which is to reach a particular destination efficiently and safely. Dynamic Programming is one solution method used in other industries for the problem of path planning to avoid collisions with fixed obstacles. The solution proposed herein for the Aircraft case uses Dynamic Programming applied to the moving obstacle case. The problem is first simplified by assuming fixed (static) obstacles for the cost minimisation algorithms. These fixed obstacles are then moved with time and the minimisation process is repeated at each time increment. Although this method works well in most cases, situations can be constructed where this method fails, allowing a collision. A modified approach is then used, whereby the movement of obstacles is included more explicitly (by modifying the shapes of the obstacles to represent motion) in the cost minimisation algorithm and a safe manoeuvre distance for each aircraft is used (by expanding the object size), to allow space for aircraft to execute safe evasive manoeuvres in difficult cases. This modification allows solutions which are complete (with no known cases of failure � collision situations) and should be considered as an important extension to the current Aircraft and Collision Avoidance System (ACAS). The testing of these solutions is focussed on the most difficult cases, and includes aircraft movement in �real space� (that is simulations using real aircraft dynamics together with dynamic programming algorithms running in discrete time steps).

Thesis (PhD)--Stellenbosch University, 2012.
ENGLISH ABSTRACT: This research focuses on the development of new efficient algorithms for multi-path planning and multi-rigid body constrained attitude control. The work is motivated by current and future applications of these algorithms in: intelligent control of multiple autonomous aircraft and spacecraft systems; control of multiple mobile and industrial robot systems; control of intelligent highway vehicles and traffic; and air and sea traffic control. We shall collectively refer to the class of mobile autonomous systems as “agents”. One of the challenges in developing and applying such algorithms is that of complexity resulting from the nontrivial agent dynamics as agents interact with other agents, and their environment. In this work, some of the current approaches are studied with the intent of exposing the complexity issues associated them, and new algorithms with reduced computational complexity are developed, which can cope with interaction constraints and yet maintain stability and efficiency. To this end, this thesis contributes the following new developments to the field of multipath planning and multi-body constrained attitude control: • The introduction of a new LMI-based approach to collision avoidance in 2D and 3D spaces. • The introduction of a consensus theory of quaternions by applying quaternions directly with the consensus protocol for the first time. • A consensus and optimization based path planning algorithm for multiple autonomous vehicle systems navigating in 2D and 3D spaces. • A proof of the consensus protocol as a dynamic system with a stochastic plant matrix. • A consensus and optimization based algorithm for constrained attitude synchronization of multiple rigid bodies. • A consensus and optimization based algorithm for collective motion on a sphere.
AFRIKAANSE OPSOMMING: Hierdie navorsing fokus op die ontwikkeling van nuwe koste-effektiewe algoritmes, vir multipad-beplanning en veelvuldige starre-liggaam beperkte standbeheer. Die werk is gemotiveer deur huidige en toekomstige toepassing van hierdie algoritmes in: intelligente beheer van veelvuldige outonome vliegtuig- en ruimtevaartuigstelsels; beheer van veelvuldige mobiele en industrile robotstelsels; beheer van intelligente hoofwegvoertuie en verkeer; en in lug- en see-verkeersbeheer. Ons sal hier “agente” gebruik om gesamentlik te verwys na die klas van mobiele outonome stelsels. Een van die uitdagings in die ontwikkeling en toepassing van sulke algoritmes is die kompleksiteit wat spruit uit die nie-triviale agentdinamika as gevolg van die interaksie tussen agente onderling, en tussen agente en hul omgewing. In hierdie werk word sommige huidige benaderings bestudeer met die doel om die kompleksiteitskwessies wat met hulle geassosieer word, bloot te l^e. Verder word nuwe algoritmes met verminderde berekeningskompleksiteit ontwikkel. Hierdie algoritmes kan interaksie-beperkings hanteer, en tog stabiliteit en doeltreffendheid behou. Vir hierdie doel dra die proefskrif die volgende nuwe ontwikkelings by tot die gebied van multipad-beplanning van multi-liggaam beperkte standbeheer: • Die voorstel van ’n nuwe LMI-gebasseerde benadering tot botsingsvermyding in 2D en 3D ruimtes. • Die voorstel van ’n konsensus-teorie van “quaternions” deur “quaternions” vir die eerste keer met die konsensusprotokol toe te pas. • ’n Konsensus- en optimeringsgebaseerde padbeplanningsalgoritme vir veelvoudige outonome voertuigstelsels wat in 2D en 3D ruimtes navigeer. • Die bewys van ’n konsensusprotokol as ’n dinamiese stelsel met ’n stochastiese aanlegmatriks. • ’n Konsensus- en optimeringsgebaseerde algoritme vir beperkte stand sinchronisasie van veelvoudige starre liggame. • ’n Konsensus- en optimeringsgebaseerde algoritme vir kollektiewe beweging op ’n sfeer.

The increasing demand to integrate unmanned aircraft systems (UAS) into the national airspace is motivated by the rapid growth of the UAS industry, especially small UAS weighing less than 55 pounds. Their use however has been limited by the Federal Aviation Administration regulations due to collision risk they pose, safety and regulatory concerns. Therefore, before civil aviation authorities can approve routine UAS flight operations, UAS must be equipped with sense-and-avoid technology comparable to the see-and-avoid requirements for manned aircraft. The sense-and-avoid problem includes several important aspects including regulatory and system-level requirements, design specifications and performance standards, intruder detecting and tracking, collision risk assessment, and finally path planning and collision avoidance. In this dissertation, our primary focus is on developing an collision detection, risk assessment and avoidance framework that is computationally affordable and suitable to run on-board small UAS. To begin with, we address the minimum sensing range for the sense-and-avoid (SAA) system. We present an approximate close form analytical solution to compute the minimum sensing range to safely avoid an imminent collision. The approach is then demonstrated using a radar sensor prototype that achieves the required minimum sensing range. In the area of collision risk assessment and collision prediction, we present two approaches to estimate the collision risk of an encounter scenario. The first is a deterministic approach similar to those been developed for Traffic Alert and Collision Avoidance (TCAS) in manned aviation. We extend the approach to account for uncertainties of state estimates by deriving an analytic expression to propagate the error variance using Taylor series approximation. To address unanticipated intruders maneuvers, we propose an innovative probabilistic approach to quantify likely intruder trajectories and estimate the probability of collision risk using the uncorrelated encounter model (UEM) developed by MIT Lincoln Laboratory. We evaluate the proposed approach using Monte Carlo simulations and compare the performance with linearly extrapolated collision detection logic. For the path planning and collision avoidance part, we present multiple reactive path planning algorithms. We first propose a collision avoidance algorithm based on a simulated chain that responds to a virtual force field produced by encountering intruders. The key feature of the proposed approach is to model the future motion of both the intruder and the ownship using a chain of waypoints that are equally spaced in time. This timing information is used to continuously re-plan paths that minimize the probability of collision. Second, we present an innovative collision avoidance logic using an ownship centered coordinate system. The technique builds a graph in the local-level frame and uses the Dijkstra's algorithm to find the least cost path. An advantage of this approach is that collision avoidance is inherently a local phenomenon and can be more naturally represented in the local coordinates than the global coordinates. Finally, we propose a two step path planner for ground-based SAA systems. In the first step, an initial suboptimal path is generated using A* search. In the second step, using the A* solution as an initial condition, a chain of unit masses connected by springs and dampers evolves in a simulated force field. The chain is described by a set of ordinary differential equations that is driven by virtual forces to find the steady-state equilibrium. The simulation results show that the proposed approach produces collision-free plans while minimizing the path length. To move towards a deployable system, we apply collision detection and avoidance techniques to a variety of simulation and sensor modalities including camera, radar and ADS-B along with suitable tracking schemes.

Unmanned aircraft systems (UAS) represent the future of modern aviation. Over the past 10 years their use abroad by the military has become commonplace for surveillance and combat. Unfortunately, their use at home has been far more restrictive. Due to safety and regulatory concerns, UAS are prohibited from flying in the National Airspace System without special authorization from the FAA. One main reason for this is the lack of an on-board pilot to "see and avoid" other air traffic and thereby maintain the safety of the skies. Development of a comparable capability, known as "Sense and Avoid" (SAA), has therefore become a major area of focus. This research focuses on the SAA problem as it applies specifically to small UAS. Given the size, weight, and power constraints on these aircraft, current approaches fail to provide a viable option. To aid in the development of a SAA system for small UAS, various simulation and hardware tools are discussed. The modifications to the MAGICC Lab's simulation environment to provide support for multiple agents is outlined. The use of C-MEX s-Functions to improve simulation performance and code portability is also presented. For hardware tests, two RC airframes were constructed and retrofitted with autopilots to allow autonomous flight. The development of a program to interface with the ground control software and run the collision avoidance algorithms is discussed as well. Intruder sensing is accomplished using a low-power, low-resolution radar for detection and an Extended Kalman Filter (EKF) for tracking. The radar provides good measurements for range and closing speed, but bearing measurements are poor due to the low-resolution. A novel method for improving the bearing approximation using the raw radar returns is developed and tested. A four-state EKF used to track the intruder's position and trajectory is derived and used to provide estimates to the collision avoidance planner. Simulation results and results from flight tests using a simulated radar are both presented. To effectively plan collision avoidance paths a tree-branching path planner is developed. Techniques for predicting the intruder position and creating safe, collision-free paths using the estimates provided by the EKF are presented. A method for calculating the cost of flying each path is developed to allow the selection of the best candidate path. As multiple duplicate paths can be created using the branching planner, a strategy to remove these paths and greatly increase computation speed is discussed. Both simulation and hardware results are presented for validation.

For the past couple of years researchers around theworld have tried to develop fully autonomous vehicles. One of theproblems that they have to solve is how to navigate in a dynamicworld with ever-changing variables. This project was initiated tolook into one scenario of the path planning problem; overtakinga human driven vehicle. Model Predictive Control (MPC) hashistorically been used in systems with slower dynamics but withadvancements in computation it can now be used in systems withfaster dynamics. In this project autonomous vehicles controlledby MPC were simulated in Python based on the kinematic bicyclemodel. Constraints were posed on the overtaking vehicle suchthat the two vehicles would not collide. Results show that anovertake, that keeps a proper distance to the other vehicle andfollows common traffic laws, is possible in certain scenarios.
Under de senaste åren har forskare världen över försökt utveckla fullt autonoma fordon. Ett av problemen som behöver lösas är hur man navigerar i en dynamisk värld med ständigt förändrande variabler. Detta projekt startades för att titta närmare på en aspekt av att planera en rutt; att köra om ett mänskligt styrt fordon. Model Predictive Control (MPC) har historiskt sett blivit använt i system med långsammare dynamik, men med framsteg inom datorers beräkningskraft kan det nu användas i system med snabbare dynamik. I detta projekt simulerades självkörande fordon, styrda av MPC, i Python. Fordonsmodellen som används var kinematic bicycle model. Begränsningar sattes på det omkörande fordonet så att de två fordonen inte kolliderar. Resultaten visar att en omkörning, som håller avstånd till det andra fordonet samt följer trafikregler, är möjligt i vissa scenarion.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

For unmanned aircraft systems to gain full access to the National Airspace System (NAS), they must have the capability to detect and avoid other aircraft. This research focuses on the development of a detect-and-avoid (DAA) system for small unmanned aircraft systems. To safely avoid another aircraft, an unmanned aircraft must detect the intruder aircraft with ample time and distance. Two analytical methods for finding the minimum detection range needed are described. The first method, time-based geometric velocity vectors (TGVV), includes the bank-angle dynamics of the ownship while the second, geometric velocity vectors (GVV), assumes an instantaneous bank-angle maneuver. The solution using the first method must be found numerically, while the second has a closed-form analytical solution. These methods are compared to two existing methods. Results show the time-based geometric velocity vectors approach is precise, and the geometric velocity vectors approach is a good approximation under many conditions. The DAA problem requires the use of a robust target detection and tracking algorithm for tracking multiple maneuvering aircraft in the presence of noisy, cluttered, and missed measurements. Additionally these algorithms needs to be able to detect overtaking intruders, which has been resolved by using multiple radar sensors around the aircraft. To achieve these goals the formulation of a nonlinear extension to R-RANSAC has been performed, known as extended recursive-RANSAC (ER-RANSAC). The primary modifications needed for this ER-RANSAC implementation include the use of an EKF, nonlinear inlier functions, and the Gauss-Newton method for model hypothesis and generation. A fully functional DAA system includes target detection and tracking, collision detection, and collision avoidance. In this research we demonstrate the integration of each of the DAA-system subcomponents into fully functional simulation and hardware implementations using a ground-based radar setup. This integration resulted in various modifications of the radar DSP, collision detection, and collision avoidance algorithms, to improve the performance of the fully integrated DAA system. Using these subcomponents we present flight results of a complete ground-based radar DAA system, using actual radar hardware.

Dans un contexte industriel aéronautique où les problématiques de sécurité constituent un facteur différentiateur clé, l’objectif de cette thèse est de répondre à la problématique ambitieuse de la réduction des accidents de type opérationnel. Les travaux de recherche s’inscrivent dans le domaine des systèmes d’alarmes pour l’évitement de collision qui ne font pas une analyse approfondie des solutions d’évitement par rapport à la situation de danger. En effet, les situations d’urgence en vol ne bénéficient pas à ce jour d’une représentation et d’un guide des solutions associées formels. Bien que certains systèmes d’assistance existent et qu’une partie de la connaissance associée aux situations d’urgence ait pu être identifiée, la génération dynamique d’une séquence de manœuvres sous fortes contraintes de temps et dans un environnement non connu à l’avance représente une voie d’exploration nouvelle. Afin de répondre à cette question et de rendre objective la notion de danger, les travaux de recherche présentés dans cette thèse mettent en confrontation la capacité d’évolution d’un aéronef dans son environnement immédiat avec une enveloppe physique devenant contraignante. Afin de mesurer ce danger, les travaux de recherche ont conduit à construire un module de trajectoires capable d’explorer l’espace en 3D. Cela a permis de tirer des enseignements en terme de flexibilité des manœuvres d’évitement possibles à l’approche du sol. De plus l’elicitation des connaissances des pilotes et des experts d’Airbus Helicopters (ancien Eurocopter) mis en situation d’urgence dans le cas d’accidents reconstitués en simulation a conduit à un ensemble de paramètres pour l’utilisation de la méthode multicritère PROMETHEE II dans le processus de prise de décision relatif au choix de la meilleure trajectoire d’évitement et par conséquent à la génération d’alarmes anti-collision
In the aeronautics industrial context, the issues related to the safety constitute a highly differentiating factor. This PhD thesis addresses the challenge of operational type accident reduction. The research works are positioned and considered within the context of existing alerting equipments for collision avoidance, who don’t report a thorough analysis of the avoidance manoeuvres with respect to a possible threat. Indeed, in-flight emergency situations are various and do not all have a formal representation of escape procedures to fall back on. Much of operational accident scenarios are related to human mistakes. Even if systems providing assistance already exist, the dynamic generation of a sequence of manoeuvres under high constraints in an unknown environment remain a news research axis, and a key development perspective. In order to address this problematic and make the notion of danger objective, the research works presented in this thesis confront the capabilities of evolution of an aircraft in its immediate environment with possible physical constraints. For that purpose, the study has conducted to generate a module for trajectory generation in the 3D space frame, capable of partitioning and exploring the space ahead and around the aircraft. This has allowed to draw conclusions in terms of flexibility of escape manoeuvres on approach to the terrain. Besides, the elicitation of the Airbus Helicopters (former Eurocopter) experts knowledge put in emergency situations, for reconstituted accident scenarios in simulation, have permitted to derive a certain number of criteria and rules for parametrising the multicriteria method PROMETHEE II in the process for the relative decision-making of the best avoidance trajectory solution. This has given clues for the generation of new alerting rules to prevent the collisions

碩士
國立交通大學
電控工程研究所
104
In industry, six-degree-of-freedom (DOF) manipulators are most widely used, because the 3D position and orientation of the end-effector can be completely determined. However, if there exist obstacles in the work space, there may be no solutions for 6-DOF manipulators to avoid the obstacles. Seven-DOF manipulators can avoid the obstacle by exploiting the redundant DOF; therefore, they are worthy of further researches. The seven-DOF manipulator in this thesis has the similar structure to an anthropomorphic manipulator. The difference is that the manipulator in this thesis has an offset between the wrist and the elbow. To describe the relationship between the position of the end-effector of the manipulator and the angle of each joint, we built the manipulator's model by the D-H rules, and derived the kinematic model, and the dynamic model which includes motor models. The motors' torque is limited by the hardware. In such situation, trajectory commands are not always fulfilled by the manipulator because the motors cannot afford the torques required to follow the desired trajectory. In addition, it is an important issue in industry to reduce the moving time of manipulators, because moving faster means working more efficiently. Manipulators' self-collision-avoidance must be fulfilled by all the path planning methods. Once self-collision happens, manipulators will be damaged. In this thesis, we formulated two problems mentioned above as optimal control problems with the constraints of manipulators' self-collision-avoidance and maximum-motor-torque, and solved the path by using GPOPS software. However, dynamic equations of seven-DOF manipulators are very complicated, such that it spent a long time for GPOPS to solve the problem. To find a more efficient solver, we use dynamic programming, and compared the result with the solution derived by GPOPS software by means of simulations. Then we verify correctness and efficiency of both solutions.

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do Paraná
The new paradigm of Industry 4.0 demand the collaboration between robot and humans. They could help (human and robot) and collaborate each other without any additional security, unlike other conventional manipulators. For this, the robot should have the ability of acquire the environment and plan (or re-plan) on-the-fly the movement avoiding the obstacles and people. This work proposes a system that acquires the space of the environment, based on a Kinect sensor, verifies the free spaces generated by a Point Cloud and executes the trajectory of manipulators in these free spaces. The simulation system should perform the path planning of a UR5 manipulator for pick-and-place tasks, while avoiding the objects around it, based on the point cloud from Kinect. And due to the results obtained in the simulation, it was possible to apply this system in real situations. The basic structure of the system is the ROS software, which facilitates robotic applications with a powerful set of libraries and tools. The MoveIt! and Rviz are examples of these tools, with them it was possible to carry out simulations and obtain planning results. The results are reported through logs files, indicating whether the robot motion plain was successful and how many manipulator poses were needed to create the final movement. This last step, allows to validate the proposed system, through the use of the RRT and PRM algorithms. Which were chosen because they are most used in the field of robot path planning.
Os novos paradigmas da Indústria 4.0 exigem a colaboração entre robôs e seres humanos. Estes podem ajudar e colaborar entre si sem qualquer segurança adicional, ao contrário de outros manipuladores convencionais. Para isto, o robô deve ter a capacidade de adquirir o meio ambiente e planear (ou re-planear) on-the-fly o movimento evitando obstáculos e pessoas. Este trabalho propõe um sistema que adquire o espaço do ambiente através do sensor Kinect. O sistema deve executar o planeamento do caminho de manipuladores que possuem movimentos de um ponto a outro (ponto inicial e final), evitando os objetos ao seu redor, com base na nuvem de pontos gerada pelo Kinect. E devido aos resultados obtidos na simulação, foi possível aplicar este sistema em situações reais. A estrutura base do sistema é o software ROS, que facilita aplicações robóticas com um poderoso conjunto de bibliotecas e ferramentas. O MoveIt! e Rviz são exemplos destas ferramentas, com elas foi possível realizar simulações e conseguir os resultados de planeamento livre de colisões. Os resultados são informados por meio de arquivos logs, indicando se o movimento do UR5 foi realizado com sucesso e quantas poses do manipulador foram necessárias criar para atingir o movimento final. Este último passo, permite validar o sistema proposto, através do uso dos algoritmos RRT e PRM. Que foram escolhidos por serem mais utilizados no ramo de planeamento de trajetória para robôs.

碩士
國立臺北科技大學
自動化科技研究所
91
This study uses the software MATLAB to develop a Graphic-User-Interface (GUI) environment to conduct an off-line robot path planning. The computer simulation programs can search the Cartesian coordinates of a collision-avoidance path for a three-dimensional robot arm. Then, the joint-space coordinates of the robot arm corresponding to a successful path can be efficiently and reasonably obtained by the optimization model suggested in this paper to avoid the tedious work of solving the inverse kinematics problem. A commercial robot arm RV-E2 is studied in this paper. During the search process, simple geometric bodies instead of the real shapes of the robot arm and obstacles are used to check the collision between the robot links and obstacles. In addition, using Pro/Engineer software constructs the solid model of the robot arm and obstacle. After path planning being completed, the joint-space coordinates of the robot arm are presented in the GUI figure. These coordinates can be downloaded to Pro/Engineer software to simulate the robot motion or downloaded to a robot controller for real practice after being transformed and translated. The GUI can integrate the whole process of the path planning, including the construction of the robot arm and obstacle model, the computational work of collision inspection and path planning, the transformation between Cartesian and joint-space coordinates, as well as the collision-free motion simulation of the robot arm. However, using Pro/Engineer to simulate the robot motion with solid model provides a way of visually verifying the feasibility of collision-avoidance paths.

abstract: There has been a vast increase in applications of Unmanned Aerial Vehicles (UAVs) in civilian domains. To operate in the civilian airspace, a UAV must be able to sense and avoid both static and moving obstacles for flight safety. While indoor and low-altitude environments are mainly occupied by static obstacles, risks in space of higher altitude primarily come from moving obstacles such as other aircraft or flying vehicles in the airspace. Therefore, the ability to avoid moving obstacles becomes a necessity for Unmanned Aerial Vehicles. Towards enabling a UAV to autonomously sense and avoid moving obstacles, this thesis makes the following contributions. Initially, an image-based reactive motion planner is developed for a quadrotor to avoid a fast approaching obstacle. Furthermore, A Dubin’s curve based geometry method is developed as a global path planner for a fixed-wing UAV to avoid collisions with aircraft. The image-based method is unable to produce an optimal path and the geometry method uses a simplified UAV model. To compensate these two disadvantages, a series of algorithms built upon the Closed-Loop Rapid Exploratory Random Tree are developed as global path planners to generate collision avoidance paths in real time. The algorithms are validated in Software-In-the-Loop (SITL) and Hardware-In-the-Loop (HIL) simulations using a fixed-wing UAV model and in real flight experiments using quadrotors. It is observed that the algorithm enables a UAV to avoid moving obstacles approaching to it with different directions and speeds.
Dissertation/Thesis
Doctoral Dissertation Computer Science 2015