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Relevant bibliographies by topics / Collision avoidance; Global path planning

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Academic literature on the topic 'Collision avoidance; Global path planning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Collision avoidance; Global path planning"

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Chen, Gang, Dan Liu, Yifan Wang, Qingxuan Jia, and Xiaodong Zhang. "Path planning method with obstacle avoidance for manipulators in dynamic environment." International Journal of Advanced Robotic Systems 15, no. 6 (November 1, 2018): 172988141882022. http://dx.doi.org/10.1177/1729881418820223.

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Obstacle avoidance is of great importance for path planning of manipulators in dynamic environment. To help manipulators successfully perform tasks, a method of path planning with obstacle avoidance is proposed in this article. It consists of two consecutive phases, namely, collision detection and obstacle-avoidance path planning. The collision detection is realized by establishing point-cloud model and testing intersection of axis-aligned bounding boxes trees, while obstacle-avoidance path planning is achieved through preplanning a global path and adjusting it in real time. This article has the following contributions. The point-cloud model is of high resolution while the speed of collision detection is improved, and collision points can be found exactly. The preplanned global path is optimized based on the improved D-star algorithm, which reduces inflection points and decreases collision probability. The real-time path adjusting strategy satisfies the requirement of reachability and obstacle avoidance for manipulators in dynamic environment. Simulations and experiments are carried out to evaluate the validity of the proposed method, and the method is available to manipulators of any degree of freedom in dynamic environment.

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Xia, Guoqing, Zhiwei Han, Bo Zhao, and Xinwei Wang. "Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization." Complexity 2020 (April 13, 2020): 1–15. http://dx.doi.org/10.1155/2020/3095426.

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An unmanned surface vehicle (USV) plans its global path before the mission starts. When dynamic obstacles appear during sailing, the planned global path must be adjusted locally to avoid collision. This study proposes a local path planning algorithm based on the velocity obstacle (VO) method and modified quantum particle swarm optimization (MQPSO) for USV collision avoidance. The collision avoidance model based on VO not only considers the velocity and course of the USV but also handles the variable velocity and course of an obstacle. According to the collision avoidance model, the USV needs to adjust its velocity and course simultaneously to avoid collision. Due to the kinematic constraints of the USV, the velocity window and course window of the USV are determined by the dynamic window approach (DWA). In summary, local path planning is transformed into a multiobjective optimization problem with multiple constraints in a continuous search space. The optimization problem is to obtain the USV’s optimal velocity variation and course variation to avoid collision and minimize its energy consumption under the rules of the International Regulations for Preventing Collisions at Sea (COLREGs) and the kinematic constraints of the USV. Since USV local path planning is completed in a short time, it is essential that the optimization algorithm can quickly obtain the optimal value. MQPSO is primarily proposed to meet that requirement. In MQPSO, the efficiency of quantum encoding in quantum computing and the optimization ability of representing the motion states of the particles with wave functions to cover the whole feasible solution space are combined. Simulation results show that the proposed algorithm can obtain the optimal values of the benchmark functions and effectively plan a collision-free path for a USV.

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Maw, Aye Aye, Maxim Tyan, Tuan Anh Nguyen, and Jae-Woo Lee. "iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV." Applied Sciences 11, no. 9 (April 27, 2021): 3948. http://dx.doi.org/10.3390/app11093948.

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Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.

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Wang, Zhenfei, Chuchu Zhang, Junfeng Wang, Zhiyun Zheng, and Lun Li. "Research on Path Planning Algorithm for Crowd Evacuation." Symmetry 13, no. 8 (July 24, 2021): 1339. http://dx.doi.org/10.3390/sym13081339.

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In recent years, crowded stampede incidents have occurred frequently, resulting in more and more serious losses. The common cause of such incidents is that when large-scale populations gather in a limited area, the population is highly unstable. In emergency situations, only when the crowd reaches the safe exit as soon as possible within a limited evacuation time to complete evacuation can the loss and casualties be effectively reduced. Therefore, the safety evacuation management of people in public places in emergencies has become a hot topic in the field of public security. Based on the analysis of the factors affecting the crowd path selection, this paper proposes an improved path-planning algorithm based on BEME (Balanced Evacuation for Multiple Exits). And pedestrian evacuation simulation is carried out in multi-exit symmetrical facilities. First, this paper optimizes the update method of the GSDL list in the BEME algorithm as the basis for evacuating pedestrians to choose an exit. Second, the collision between pedestrians is solved by defining the movement rule and collision avoidance strategy. Finally, the algorithm is compared with BEME and traditional path-planning algorithms. The results show that the algorithm can further shorten the global evacuation distance of the symmetrical evacuation scene, effectively balance the number of pedestrians at each exit and reduce the evacuation time. In addition, this improved algorithm uses a collision avoidance strategy to solve the collision and congestion problems in path planning, which helps to maximize evacuation efficiency. Whether the setting of the scene or the setting of the exit, all studies are based on symmetric implementation. This is more in line with the crowd evacuation in the real scene, making the experimental results more meaningful.

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Zhu, Shinan, Weiyi Zhu, Xueqin Zhang, and Tao Cao. "Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988141989897. http://dx.doi.org/10.1177/1729881419898979.

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Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

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Polvara, Riccardo, Sanjay Sharma, Jian Wan, Andrew Manning, and Robert Sutton. "Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles." Journal of Navigation 71, no. 1 (October 10, 2017): 241–56. http://dx.doi.org/10.1017/s0373463317000753.

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The adoption of a robust collision avoidance module is required to realise fully autonomous Unmanned Surface Vehicles (USVs). In this work, collision detection and path planning methods for USVs are presented. Attention is focused on the difference between local and global path planners, describing the most common techniques derived from classical graph search theory. In addition, a dedicated section is reserved for intelligent methods, such as artificial neural networks and evolutionary algorithms. Born as optimisation methods, they can learn a close-to-optimal solution without requiring large computation effort under certain constraints. Finally, the deficiencies of the existing methods are highlighted and discussed. It has been concluded that almost all the existing method do not address sea or weather conditions, or do not involve the dynamics of the vessel while defining the path. Therefore, this research area is still far from being considered fully explored.

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de Oliveira, Guilherme, Kevin de Carvalho, and Alexandre Brandão. "A Hybrid Path-Planning Strategy for Mobile Robots with Limited Sensor Capabilities." Sensors 19, no. 5 (March 1, 2019): 1049. http://dx.doi.org/10.3390/s19051049.

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This paper introduces a strategy for the path planning problem for platforms with limited sensor and processing capabilities. The proposed algorithm does not require any prior information but assumes that a mapping algorithm is used. If enough information is available, a global path planner finds sub-optimal collision-free paths within the known map. For the real time obstacle avoidance task, a simple and cost-efficient local planner is used, making the algorithm a hybrid global and local planning solution. The strategy was tested in a real, cluttered environment experiment using the Pioneer P3-DX and the Xbox 360 kinect sensor, to validate and evaluate the algorithm efficiency.

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Li, Xingyu, Bo Tang, John Ball, Matthew Doude, and Daniel W. Carruth. "Rollover-Free Path Planning for Off-Road Autonomous Driving." Electronics 8, no. 6 (May 31, 2019): 614. http://dx.doi.org/10.3390/electronics8060614.

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Perception, planning, and control are three enabling technologies to achieve autonomy in autonomous driving. In particular, planning provides vehicles with a safe and collision-free path towards their destinations, accounting for vehicle dynamics, maneuvering capabilities in the presence of obstacles, traffic rules, and road boundaries. Existing path planning algorithms can be divided into two stages: global planning and local planning. In the global planning stage, global routes and the vehicle states are determined from a digital map and the localization system. In the local planning stage, a local path can be achieved based on a global route and surrounding information obtained from sensors such as cameras and LiDARs. In this paper, we present a new local path planning method, which incorporates a vehicle’s time-to-rollover model for off-road autonomous driving on different road profiles for a given predefined global route. The proposed local path planning algorithm uses a 3D occupancy grid and generates a series of 3D path candidates in the s-p coordinate system. The optimal path is then selected considering the total cost of safety, including obstacle avoidance, vehicle rollover prevention, and comfortability in terms of path smoothness and continuity with road unevenness. The simulation results demonstrate the effectiveness of the proposed path planning method for various types of roads, indicating its wide practical applications to off-road autonomous driving.

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Hee, Lee Gim, and Marcelo H. Ang Jr. "An Integrated Algorithm for Autonomous Navigation of a Mobile Robot in an Unknown Environment." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 4 (July 20, 2008): 328–35. http://dx.doi.org/10.20965/jaciii.2008.p0328.

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Global path planning algorithms are good in planning an optimal path in a known environment, but would fail in an unknown environment and when reacting to dynamic and unforeseen obstacles. Conversely, local navigation algorithms perform well in reacting to dynamic and unforeseen obstacles but are susceptible to local minima failures. A hybrid integration of both the global path planning and local navigation algorithms would allow a mobile robot to find an optimal path and react to any dynamic and unforeseen obstacles during an operation. However, the hybrid method requires the robot to possess full or partial prior information of the environment for path planning and would fail in a totally unknown environment. The integrated algorithm proposed and implemented in this paper incorporates an autonomous exploration technique into the hybrid method. The algorithm gives a mobile robot the ability to plan an optimal path and does online collision avoidance in a totally unknown environment.

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Lazarowska, Agnieszka. "Decision support system for collision avoidance at sea." Polish Maritime Research 19, Special (October 1, 2012): 19–24. http://dx.doi.org/10.2478/v10012-012-0018-2.

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ABSTRACT The paper presents design and realization of computer decision support system in collision situations of passage with greater quantity of met objects. The system was implemented into the real ship electro-navigational system onboard research and training ship m/v HORYZONT II. The radar system with Automatic Radar Plotting Aid constitutes a source of input data for algorithm determining safe trajectory of a ship. The article introduces radar data transmission details. The dynamic programming algorithm is used for the determination of safe optimal trajectory of own ship. The system enables navigational data transmission from radar system and automatic determining of safe manoeuvre or safe trajectory of a ship. Further development of navigator’s decision support system is also presented. Path Planning Subsystem is proposed for the determination of global optimal route between harbours with the use of Ant Colony Optimization algorithms.

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

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McLean, Alistair William. "Path planning for redundant manipulators." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296938.

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Naik, Ankur. "Arc Path Collision Avoidance Algorithm for Autonomous Ground Vehicles." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/30969.

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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

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Aasland, Kristoffer. "Optimal 3D Path Planning for a 9 DOF Robot Manipulator with Collision Avoidance." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8886.

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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.

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Song, Rui. "Path planning and collision avoidance of unmanned surface vehicles in the marine environment." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10042821/.

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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.

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Xu, Ziwei. "LTL Motion Planning with Collision Avoidance for A Team of Quadrotors." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196904.

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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.

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Blaich, Michael [Verfasser]. "Path Planning and Collision Avoidance for Safe Autonomous Vessel Navigation in Dynamic Environments / Michael Blaich." Aachen : Shaker, 2017. http://d-nb.info/1138177245/34.

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Yu, Huili. "Vision-based Path Planning, Collision Avoidance, and Target Tracking for Unmanned Air and Ground Vehicles in Urban Environments." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/3081.

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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.

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Holdsworth, Robert, and roberth@gil com au. "Autonomous in-flight path planning to replace pure collision avoidance for free flight aircraft using automatic depedent surveillance broadcast." Swinburne University of Technology, 2003. http://adt.lib.swin.edu.au./public/adt-VSWT20060608.141036.

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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).

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Okoloko, Innocent. "Multi-path planning and multi-body constrained attitude control." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71905.

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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.

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Ananthanarayanan, Hariharan Sankara. "Complete Path Planning of Higher DOF Manipulators in Human Like Environments." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1445972852.

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Book chapters on the topic "Collision avoidance; Global path planning"

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Gattringer, Hubert, Andreas Müller, and Klemens Springer. "Path Planning and Collision Avoidance." In Mechatronics and Robotics, 133–60. Boca Raton : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429347474-7.

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Landry, Chantal, Matthias Gerdts, René Henrion, and Dietmar Hömberg. "Path-Planning with Collision Avoidance in Automotive Industry." In IFIP Advances in Information and Communication Technology, 102–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36062-6_11.

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Kim, Hyeok, Jeonghoon Kwak, Guichang Sim, and Yunsick Sung. "Path Planning Method for Collision Avoidance of Multiple UAVs." In Lecture Notes in Electrical Engineering, 49–55. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5041-1_9.

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Simas, Henrique, Daniel Martins, and Raffaele Di Gregorio. "Smooth path planning for redundant robots on collision avoidance." In Advances in Mechanism and Machine Science, 1869–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20131-9_185.

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Valero, F., J. I. Cuadrado, V. Mata, and M. Ceccarelli. "Collision-Avoidance Robot Path Planning Using Fully Cartesian Coordinates." In Advances in Robot Kinematics and Computational Geometry, 485–94. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-015-8348-0_49.

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Li-Jia, Chen, and Huang Li-Wen. "Ship Collision Avoidance Path Planning by PSO Based on Maneuvering Equation." In Future Computing, Communication, Control and Management, 675–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27326-1_87.

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Kim, Dong-Eon, Dong-Ju Park, Jin-Hyun Park, and Jang-Myung Lee. "Collision and Singularity Avoidance Path Planning of 6-DOF Dual-Arm Manipulator." In Intelligent Robotics and Applications, 195–207. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97589-4_17.

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Kivelä, Tuomo, Jouni Mattila, Jussi Puura, and Sirpa Launis. "Redundant Robotic Manipulator Path Planning for Real-Time Obstacle and Self-Collision Avoidance." In Advances in Service and Industrial Robotics, 208–16. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61276-8_24.

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Benevides, João R. S., and Valdir Grassi. "Path Planning with Collision Avoidance for Free-Floating Manipulators: A RRT-Based Approach." In Communications in Computer and Information Science, 103–19. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47247-8_7.

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Nakrani, Naitik M., and Maulin M. Joshi. "An Online Path Planning with Modified Autonomous Parallel Parking Controller for Collision Avoidance." In Smart Computing Techniques and Applications, 403–13. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1502-3_41.

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Conference papers on the topic "Collision avoidance; Global path planning"

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Fernando, Jeffry Bonar, Toru Tanigawa, Eiichi Naito, Katsuyoshi Yamagami, and Jun Ozawa. "Collision avoidance path planning for hospital robot with consideration of disabled person's movement characteristic." In 2012 IEEE 1st Global Conference on Consumer Electronics (GCCE). IEEE, 2012. http://dx.doi.org/10.1109/gcce.2012.6379636.

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Moshchuk, Nikolai, Shih-Ken Chen, Chad Zagorski, and Amy Chatterjee. "Path Planning for Collision Avoidance Maneuver." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-63893.

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This paper summarizes the development of an optimal path planning algorithm for collision avoidance maneuver. The goal of the optimal path is to minimize distance to the target vehicle ahead of the host vehicle subject to vehicle and environment constraints. Such path constrained by allowable lateral (centripetal) acceleration and lateral acceleration rate (jerk). Two algorithms with and without lateral jerk limitation, are presented. The algorithms were implemented in Simulink and verified in CarSim. The results indicate that the lateral jerk limitation increases time-to-collision threshold and leads to a larger distance to the target required for emergency lane change. Collision avoidance path without lateral jerk limitation minimizes the distance to the target vehicle and is suitable for path tracking control in real-time application; however tracking such a path requires very aggressive control.

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G. Batista, Josias, Felipe J. S. Vasconcelos, Kaio M. Ramos, Darielson A. Souza, and José L. N. Silva. "Path Planning Collision Avoidance using Reinforcement Learning." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1597.

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Industrial robots have grown over the years making production systems more and more efficient, requiring the need for efficient trajectory generation algorithms that optimize and, if possible, generate collision-free trajectories without interrupting the production process. In this work is presented the use of Reinforcement Learning (RL), based on the Q-Learning algorithm, in the trajectory generation of a robotic manipulator and also a comparison of its use with and without constraints of the manipulator kinematics, in order to generate collisionfree trajectories. The results of the simulations are presented with respect to the efficiency of the algorithm and its use in trajectory generation, a comparison of the computational cost for the use of constraints is also presented.

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Burhanuddin, Liyana Adilla, Md Nazrul Islam, and Suhaila Mohd Yusof. "Evaluation of Collision Avoidance path planning Algorithm." In 2013 International Conference on Research and Innovation in Information Systems (ICRIIS). IEEE, 2013. http://dx.doi.org/10.1109/icriis.2013.6716736.

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Madas, David, Mohsen Nosratinia, Mansour Keshavarz, Peter Sundstrom, Rolland Philippsen, Andreas Eidehall, and Karl-Magnus Dahlen. "On path planning methods for automotive collision avoidance." In 2013 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2013. http://dx.doi.org/10.1109/ivs.2013.6629586.

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Patsikatheodorou, Stratos, Cees Bil, and Kevin Massey. "UAV Collision Avoidance and Path Planning in Commercial Airspace." In 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-5426.

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Wang, Han, Muqing Cao, Hao Jiang, and Lihua Xie. "Feasible Computationally Efficient Path Planning for UAV Collision Avoidance." In 2018 IEEE 14th International Conference on Control and Automation (ICCA). IEEE, 2018. http://dx.doi.org/10.1109/icca.2018.8444284.

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Shah, M. A., and Nabil Aouf. "Dynamic cooperative perception and path planning for collision avoidance." In 2009 6th International Symposium on Mechatronics and its Applications (ISMA). IEEE, 2009. http://dx.doi.org/10.1109/isma.2009.5164802.

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Berntorp, Karl. "Path planning and integrated collision avoidance for autonomous vehicles." In 2017 American Control Conference (ACC). IEEE, 2017. http://dx.doi.org/10.23919/acc.2017.7963572.

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Jain, Piyush K., and Souran Manoochehri. "Optimal Three Dimensional Robot Path Planning With Collision Avoidance." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0155.

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Abstract The study reported in this paper deals with path planning in three-dimensional space using network optimization for robotic manipulators working in the presence of obstacles and workspace singularities. To execute the algorithm, the robot design parameters, the geometry and location of the obstacles, and the initial and goal positions of the desired task are required. As a first step, the manipulator workspace is discretized and points inside forbidden regions formed by obstacles and singularities are excluded. An ellipsoidal searchspace is then selected as a part of the workspace to make the network enumeration and path synthesis more efficient. Based on an allowable deviation angle, path segments axe created to form the connectivity network. A path which is optimal with respect to the manipulator kinematic and dynamic properties is generated as a sequence of intermediate points connecting the initial and goal states using Dijkstra’s minimum cost search technique. A computer program has been developed to implement this methodology for three-axis manipulators, and results of the application of this algorithm to some industrial robots are presented.

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