Relevant bibliographies by topics / Global path planning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Global path planning'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Global path planning"

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Zelek, J. S., and M. D. Levine. "Local-global concurrent path planning and execution." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 30, no. 6 (2000): 865–70. http://dx.doi.org/10.1109/3468.895924.

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Sánchez Miralles, Álvaro, and Miguel Ángel Sanz Bobi. "Global Path Planning in Gaussian Probabilistic Maps." Journal of Intelligent and Robotic Systems 40, no. 1 (May 2004): 89–102. http://dx.doi.org/10.1023/b:jint.0000034339.13257.e6.

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Bostrom-Rost, Per, Daniel Axehill, and Gustaf Hendeby. "On Global Optimization for Informative Path Planning." IEEE Control Systems Letters 2, no. 4 (October 2018): 833–38. http://dx.doi.org/10.1109/lcsys.2018.2849559.

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Panov, Stojanche, and Saso Koceski. "Metaheuristic global path planning algorithm for mobile robots." International Journal of Reasoning-based Intelligent Systems 7, no. 1/2 (2015): 35. http://dx.doi.org/10.1504/ijris.2015.070910.

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Xie, Shaorong, Peng Wu, Hengli Liu, Peng Yan, Xiaomao Li, Jun Luo, and Qingmei Li. "A novel method of unmanned surface vehicle autonomous cruise." Industrial Robot: An International Journal 43, no. 1 (January 18, 2016): 121–30. http://dx.doi.org/10.1108/ir-05-2015-0097.

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Purpose – This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning. Design/methodology/approach – A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment. Findings – The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment. Originality/value – This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.
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Lv, Taizhi, Chunxia Zhao, and Jiancheng Bao. "A Global Path Planning Algorithm Based on Bidirectional SVGA." Journal of Robotics 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/8796531.

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For path planning algorithms based on visibility graph, constructing a visibility graph is very time-consuming. To reduce the computing time of visibility graph construction, this paper proposes a novel global path planning algorithm, bidirectional SVGA (simultaneous visibility graph construction and path optimization by A⁎). This algorithm does not construct a visibility graph before the path optimization. However it constructs a visibility graph and searches for an optimal path at the same time. At each step, a node with the lowest estimation cost is selected to be expanded. According to the status of this node, different through lines are drawn. If this line is free-collision, it is added to the visibility graph. If not, some vertices of obstacles which are passed through by this line are added to the OPEN list for expansion. In the SVGA process, only a few visible edges which are in relation to the optimal path are drawn and the most visible edges are ignored. For taking advantage of multicore processors, this algorithm performs SVGA in parallel from both directions. By SVGA and parallel performance, this algorithm reduces the computing time and space. Simulation experiment results in different environments show that the proposed algorithm improves the time and space efficiency of path planning.
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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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Huang, Chen, and Jiyou Fei. "UAV Path Planning Based on Particle Swarm Optimization with Global Best Path Competition." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 06 (February 21, 2018): 1859008. http://dx.doi.org/10.1142/s0218001418590085.

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Path planning is the essential aspect of autonomous flight system for unmanned aerial vehicles (UAVs). An improved particle swarm optimization (PSO) algorithm, named GBPSO, is proposed to enhance the performance of three-dimensional path planning for fixed-wing UAVs in this paper. In order to improve the convergence speed and the search ability of the particles, the competition strategy is introduced into the standard PSO to optimize the global best solution during the process of particle evolution. More specifically, according to a set of segment evaluation functions, the optimal path found by single waypoint selection way is adopted as one of the candidate global best paths. Meanwhile, based on the particle as an integrated individual, an optimal trajectory from the start point to the flight target is generated as another global best candidate path. Subsequently, the global best path is determined by considering the pre-specified elevation function values of two candidate paths. Finally, to verify the performance of the proposed method, GBPSO is compared with some existing path-planning methods in two simulation scenarios with different obstacles. The results demonstrate that GBPSO is more effective, robust and feasible for UAV path planning.
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Song, Xiaoru, Song Gao, C. B. Chen, Kai Cao, and Jiaoru Huang. "A New Hybrid Method in Global Dynamic Path Planning of Mobile Robot." International Journal of Computers Communications & Control 13, no. 6 (November 29, 2018): 1032–46. http://dx.doi.org/10.15837/ijccc.2018.6.3153.

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Path planning and real-time obstacle avoidance is the key technologies of mobile robot intelligence. But the efficiency of the global path planning is not very high. It is not easy to avoid obstacles in real time. Aiming at these shortcomings it is proposed that a global dynamic path planning method based on improved A* algorithm and dynamic window method. At first the improved A* algorithm is put forward based on the traditional A* algorithm in the paper. Its optimized heuristic search function is designed. They can be eliminated that the redundant path points and unnecessary turning points. Simulation experiment 1 results show that the planned path length is reduced greatly. And the path transition points are less, too. And then it is focused on the global dynamic path planning of fusion improved A* Algorithm and Dynamic Window Method. The evaluation function is constructed taking into account the global optimal path. The real time dynamic path is planning. On the basis of ensuring the optimal global optimization of the planning path, it is improved that the smoothness of the planning path and the local real-time obstacle avoidance ability. The simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional path planning algorithms with the fusion algorithm in the paper. It is more fit to the dynamics of the robot control. And when a dynamic obstacle is added, the new path can be gained. The barrier can be bypass and the robot is to reach the target point. It can be guaranteed the global optimality of the path. Finally the Turtlebot mobile robot was used to experiment. The experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. And the planned global path is smoother. When the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. It can re-plan the path. It can bypass the random obstacle to reach the original target point. The outputting control parameters are more conducive to the robot’s automatic control. The fusion method is used for global dynamic path planning of mobile robots in this paper. In summary the experimental results show that the method is good efficiency and real-time performance. It has great reference value for the dynamic path planning application of mobile robot.
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Niu, Chuanhu, Aijuan Li, Xin Huang, Wei Li, and Chuanyan Xu. "Research on Global Dynamic Path Planning Method Based on Improved A ∗ Algorithm." Mathematical Problems in Engineering 2021 (August 3, 2021): 1–13. http://dx.doi.org/10.1155/2021/4977041.

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Aiming at the optimal path and planning efficiency of global path planning for intelligent driving, this paper proposes a global dynamic path planning method based on improved A ∗ algorithm. First, this method improves the heuristic function of the traditional A ∗ algorithm to improve the efficiency of global path planning. Second, this method uses a path optimization strategy to make the global path smoother. Third, this method is combined with the dynamic window method to improve the real-time performance of the dynamic obstacle avoidance of the intelligent vehicle. Finally, the global dynamic path planning method of the proposed improved A ∗ algorithm is verified through simulation experiments and real vehicle tests. In the simulation analysis, compared with the modified A ∗ algorithm and the traditional A ∗ algorithm, the method in this paper shortens the path distance by 2.5%∼3.0%, increases the efficiency by 10.3%∼13.6% and generates a smoother path. In the actual vehicle test, the vehicle can avoid dynamic obstacles in real time. Therefore, the method proposed in this paper can be applied on the intelligent vehicle platform. The path planning efficiency is high, and the dynamic obstacle avoidance is good in real time.
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Dissertations / Theses on the topic "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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Muhandiramge, Ranga. "Maritime manoeuvring optimization : path planning in minefield threat environments." University of Western Australia. School of Mathematics and Statistics, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0015.

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The aim of the research project that is the subject of this thesis is to apply mathematical techniques, especially those in the area of operations research, to the problem of maritime minefield transit. We develop several minefield models applicable to different aspects of the minefield problem. These include optimal mine clearance, shortest time traversal and time constrained traversal. We hope the suite of models and tools developed will help make mine field clearance and traversal both safer and more efficient and that exposition of the models will bring a clearer understanding of the mine problem from a mathematical perspective. In developing the solutions to mine field models, extensive use is made of network path planning algorithms, particularly the Weight Constrained Shortest Path Problem (WCSPP) for which the current state-of-the-art algorithm is extended. This is done by closer integration of Lagrangean relaxation and preprocessing to reduce the size of the network. This is then integrated with gap-closing algorithms based on enumeration to provide optimal or near optimal solutions to the path planning problem. We provide extensive computational evidence on the performance of our algorithm and compare it to other algorithms found in the literature. This tool then became fundamental in solving various separate minefield models. Our models can be broadly separated into obstacle models in which mine affected regions are treated as obstacles to be avoided and continuous threat in which each point of space has an associated risk. In the later case, we wish to find a path that minimizes the integral of the risk along the path while constraining the length of the path. We call this the Continuous Euclidean Length Constrained Minimum Cost Path Problem (C-LCMCPP), for which we present a novel network approach to solving this continuous problem. This approach results in being able to calculate a global lower bound on a non-convex optimization problem.
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Ferreira, Ivan dos Santos. "Integration of the planning, global localization and path execution in autonomous cars." Master's thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/16423.

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Mestrado em Engenharia de Automação Industrial
Atlascar is a project developed in the Automation and Robotics laboratory at the Mechanical Department of the University of Aveiro, Portugal. It is used for research in autonomous navigation and for advanced driver assistance systems. The aim of this thesis is to develop a navigation system for path planning, which includes a graphical interface that allows the user to input a nal destination, with possible via points if needed. The application should be able to track the vehicle in real-time and output sequences of high level maneuvers along the mission. New hardware is also to be installed and con gured, namely a new server and a new GPS receiver, which requires an intervention on the previous setup of the Atlascar. The whole system is divided into three parts: Planner, Navigator and User interface. The planner module will be responsible for calculating the path and the instructions. The navigator module will be responsible for processing the data from the planner module ; communicating with a database, read and write values and acquiring data from the GPS. The user interface allows the user to input the desired destination by clicking on a map, this module also allows the user to: monitor the current location of the vehicle; view the path to the destination and it shows the instruction that needs to be done. This module also establishes a communication with the database. This module is optional because the system also works without a user interface, it simply allows the user to do it interactively and it allows motorization. If a wrong decision is made and the user follows a wrong path, the system will recalculate the mission and show a new path.
O Atlascar é um projeto desenvolvido no laboratório de automação e robótica, no departamento de Engenharia Mecânica da Universidade de Aveiro. Este veículo é utilizado para investigação em condução autónoma e sistemas avançados de apoio ao condutor. O objetivo desta dissertação é desenvolver um sistema de navegação para planeamento de rotas. Este sistema inclui uma interface gráfica que permite ao utilizador selecionar um destino, com pontos de passagem intermédios se assim for pretendido. A aplicação deve permitir monitorizar em tempo real o veículo e indicar as manobras que devem ser realizadas ao longo do percurso. Novo hardware ter a de ser instalado e configurado, nomeadamente um novo servidor e recetor GPS. Isto requer uma intervenção na configuração atual do Atlascar. O sistema está dividido em três partes: Planeador, Navegador e Interface Gráfica. O módulo do planeador é responsável por calcular os percursos e as manobras a realizar. O navegador é responsável pelo processamento dos dados vindos do planeador. Estabelece uma comunicação com uma base de dados de onde vai ler e escrever valores. Este módulo também será responsável por ler os valores vindos do GPS. A interface gráfica permite ao utilizador selecionar o destino pretendido, o que é feito clicando num mapa. Este módulo também permite monitorizar a localização do veículo, ver o percurso que irá ser realizado e apresenta a instrução a realizar. Este módulo também estabelece uma comunicação com uma base de dados. Este módulo é opcional porque o sistema também funciona sem uma interface visual, esta simplesmente permite ao utilizador fazê-lo de forma interactiva e também permite monitorização. Caso o utilizador escolha um percurso errado durante a viagem, o sistema recalcula a missão e apresenta um novo caminho.
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Toupet, Olivier. "Real-time path-planning using mixed-integer linear programming and global cost-to-go maps." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35290.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (leaves 93-95).
With the advance in the fields of computer science, control and optimization, it is now possible to build aerial vehicles which do not need pilots. An important capability for such autonomous vehicles is to be able to generate their own path to navigate in a constrained environment and accomplish mission objectives, such as reaching waypoints in minimal time. To account for dynamic changes in the environment, perturbations, modeling errors and modifications in the mission scenario, the trajectory needs to be continuously re-optimized online based on the latest available updates. However, to allow for high update rates, the trajectory optimization problem needs to be simple enough to be solved quickly. Optimizing for a continuous trajectory of a dynamically-constrained vehicle in the presence of obstacles is an infinite-dimension nonlinear optimal control problem. Such a problem is intractable in real-time and simplifications need to be made. In this thesis, the author presents the mechanisms used to design a path-planner with real-time and long-range capabilities. The approach relies on converting the optimal control problem into a parameter optimization one whose horizon can be reduced by using a global cost-to-go function to provide an approximate cost for the tail of the trajectory.
(cont.) Thus only the short-term trajectory is being constantly optimized online based on a mixed integer linear programming formulation that accounts for the vehicle's performance. The cost-to-go function presented in this thesis has the feature to be tailored to both the environment and the vehicle's maneuvering capabilities. The author then implements and demonstrates a path-planner software based on the presented approach for a real unmanned helicopter, the Renegade, that flew within the DARPA SEC program. A full description of the capabilities and functions supported by the planner software are provided. Hardware-in-the-loop simulation results are provided to illustrate the performance of the system.
by Olivier Toupet.
S.M.
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Fayad, Carole. "Development of techniques for global/local path planning of an autonomous mobile robot in dynamic environments." Thesis, University of Nottingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368954.

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Skinner, J. Branson. "Fashioning Waste: Considering the Global and Local Impacts of the Secondhand Clothing Trade in Accra, Ghana and Charting an Inclusive Path Forward." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1553613566277155.

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Warpe, Hrusheekesh Sunil. "An Analysis of EcoRouting Using a Variable Acceleration Rate Synthesis Model." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78678.

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Automotive manufacturers are facing increasing pressure from legislative bodies and consumers to reduce fuel consumption and greenhouse gas emissions of vehicles. This has led to many automotive manufacturers starting production of Plug-in Hybrid Electric Vehicles (PHEV's) and Battery Electric Vehicles (BEV's). Another method that helps to reduce the environmental effect of transportation is EcoRouting. The standard Global Positioning System (GPS) navigation offers route alternatives between user specified origin and destination. This technology provides multiple routes to the user and focuses on reducing the travel time to reach to the destination. EcoRouting is the method to determine a route that minimizes vehicle energy consumption, unlike traditional routing methods that minimize travel time. An EcoRouting system has been developed as a part of this thesis that takes in information such as speed limits, the number of stop lights, and the road grade to calculate the energy consumption of a vehicle along a route. A synthesis methodology is introduced that takes into consideration the distance between the origin and destination, the acceleration rate of the vehicle, cruise speed and jerk rate as inputs to simulate driver behavior on a given route. A new approach is presented in this thesis that weighs the energy consumption for different routes and chooses the route with the least energy consumption, subject to a constraint on travel time. A cost function for quantifying the effect of travel time is introduced that assists in choosing the EcoRoute with an acceptable limit on the travel time required to reach the destination. The analysis of the EcoRouting system with minimum number of conditional stops and maximum number of conditional stops is done in this thesis. The effect on energy consumption with the presence and absence of road-grade information along a route is also studied. A sensitivity study is performed to observe the change in energy consumption of the vehicle with a change in acceleration rates and road grade. Three routing scenarios are presented in this thesis to demonstrate the functionality of EcoRouting. The EcoRouting model presented in this thesis is also validated against an external EcoRouting research paper and the energy consumption along three routes is calculated. The EcoRoute solution is found to vary with the information given to the variable acceleration rate model. The synthesis and the results that are obtained show that parameters such as acceleration, deceleration, and road grade affect the overall energy consumption of a vehicle and are helpful in determining the EcoRoute.
Master of Science
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Chen, Chia-Hsien, and 陳家賢. "Dynamic Global Path Planning Based on Kalman/Particle Filter." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/40241663387068766323.

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碩士
國立臺灣科技大學
自動化及控制研究所
100
Autonomous robots are widely used in various applications such as industrial, military, exhibition and home care. Dynamic path planning including obstacle avoidance in real-time is a critical issue for the success of mobile robots in the above dynamic environments. In recent years, most of researches focus on using local sensors to avoid dynamic obstacles. It performances well in obstacle avoidance, but is not an efficient way of goal-oriented system navigation. This thesis proposes a dynamic path planning system only using omni-directional camera on the ceiling. Based on the global view, background subtraction method is applied to detect a moving object and Kalman/Particle filter based on HSV (hue, saturation, and value) color space is used to track the moving object. Furthermore, this system integrates reduced visibility graph algorithm and parallel processing in path planning to reduce the computation time from 4115 ms to less than 200 ms in 457x357 cm2 work space. Finally, it navigates the mobile robot with closed loop control through the server-client model. Our experimental results show that the Particle Filter is not only more robust than Kalman Filter to the speed change of moving object but also has better performance during object occlusion and illumination changes. The results also demonstrate the effectiveness and efficiency of the proposed visual servoing system. The system successfully controls the motion of a robot to avoid dynamic obstacle of speed 200 mm/s and re-plans a new path in real-time when necessary. It also maintains the trajectory following error of less than 30 mm with the closed loop control.
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Weng, Chung-tai, and 翁崇泰. "Global Path Planning and Remote Control Design for Robotic Mobility Platform." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/19463674735634962832.

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碩士
淡江大學
機械與機電工程學系碩士班
96
The objective of this research is to develop a robotic mobility platform, which using Segway RMP 50 to demonstrate the optimum path planning under A* algorithm and a developed map. Segway RMP 50 is the principal platform in this research. The self-designed mechanical loading structured is installed on the RMP 50 to set up the notebook computer/webcam. The robot system is capable to install more sensors to detect the environmental information beside this robot. A client control system is developed to be a server by the webcam and laptop computer. The other users are able to watch the pc screen to find out the related environmental images by the internet from the developed server. The A* algorithm and a developed grid map are applied to let this robot be able to avoid obstacle and make an optimum path planning. The simulation results show that the Segway RMP 50 system is capable to make a simple movement planning and to calculate the length of the moving path. Finally, the robot system is demonstrated its ability of obstacle–avoidance and the optimum path planning by the A* algorithm.
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Ricarte, Gustavo, and 祁谷風. "Hybrid of Visual Global Path Planning and Intelligent Local Navigation for Autonomous Mobile Robot." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/02723413948678692818.

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碩士
國立臺灣科技大學
電機工程系
100
In recent years, an autonomous mobile robot is required for performing several different tasks, such as removal of hazardous mines, exploration of volcano, monitoring nuclear power plants, etc. In general, these tasks are in hazardous and populated environments. A mobile robot must be able to deal with unexpected changes in the environment and to perform complex assignments in a short period of time. In this work, the focus is to provide a method for a mobile robot to navigate safely in an unknown and dynamic environment. The proposed scenario is "Assistance and Rescue Disaster", where a mobile robot is used to steer through a hazardous site in search of victims, in order to provide them rescue or emergency assistance. The approach used is a hybrid of visual global path plan and intelligent local navigation. The path plan is obtained by using visibility graph and Dijkstra’s algorithm, and the local navigation uses fuzzy logic to control the robot's behavior. Experiments were done in two different sizes of mobile robots and the initial objectives were achieved. The results show that a shorter travelled distance were obtained, as well as on-time reaction to dynamic obstacles in the robot's path. The outcome is that the hybrid method provides a better solution than any of the methods alone, combining the efficiency of the path planning with the dynamic response of the local navigation technique.
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Books on the topic "Global path planning"

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Richbourg, R. F. Exploiting capability constraints to solve global, two dimensional path planning problems. Monterey, California: Naval Postgraduate School, 1986.

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Guthrie, Brian. Picking a path to prosperity: A strategy for global-best commerce. Ottawa: Conference Board of Canada, 2006.

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Han'guk chungso kiŏp ŭi kung, chŭk, t'ong chŏllyak: 9988 kangso kiŏp [SGs] 36-kye = 36 paths to global small giants. Sŏul-si: Kŭllobŏl Kangso Kiŏp Yŏn'guso, 2011.

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J, Wen, and United States. National Aeronautics and Space Administration., eds. A global path planning approach for redundant manipulators. Troy, N.Y: Rensselaer Polytechnic Institute, Electrical, Computer, and Systems Engineering Dept., 1993.

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Book chapters on the topic "Global path planning"

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Lam, Tin Lun, and Yangsheng Xu. "Global Path and Motion Planning." In Springer Tracts in Advanced Robotics, 117–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28311-6_8.

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Koubaa, Anis, Hachemi Bennaceur, Imen Chaari, Sahar Trigui, Adel Ammar, Mohamed-Foued Sriti, Maram Alajlan, Omar Cheikhrouhou, and Yasir Javed. "Integration of Global Path Planners in ROS." In Robot Path Planning and Cooperation, 83–102. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77042-0_4.

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Koubaa, Anis, Hachemi Bennaceur, Imen Chaari, Sahar Trigui, Adel Ammar, Mohamed-Foued Sriti, Maram Alajlan, Omar Cheikhrouhou, and Yasir Javed. "Background on Artificial Intelligence Algorithms for Global Path Planning." In Robot Path Planning and Cooperation, 13–51. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77042-0_2.

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Koubaa, Anis, Hachemi Bennaceur, Imen Chaari, Sahar Trigui, Adel Ammar, Mohamed-Foued Sriti, Maram Alajlan, Omar Cheikhrouhou, and Yasir Javed. "Design and Evaluation of Intelligent Global Path Planning Algorithms." In Robot Path Planning and Cooperation, 53–81. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77042-0_3.

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Latombe, Jean-Claude. "Global Path Planning Approaches for Material Movements in a Worksite." In Advanced Information Technologies for Industrial Material Flow Systems, 223–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74575-1_13.

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Sun, Yu, and Rubo Zhang. "Research on Global Path Planning for AUV Based on GA." In Advances in Intelligent and Soft Computing, 311–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27329-2_43.

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Lu, Ming-Chih, Chen-Chien Hsu, Yuan-Jun Chen, and Shih-An Li. "Hybrid Path Planning Incorporating Global and Local Search for Mobile Robot." In Advances in Autonomous Robotics, 441–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32527-4_50.

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Panov, Stojanche, and Natasa Koceska. "Global Path Planning in Grid-Based Environments Using Novel Metaheuristic Algorithm." In ICT Innovations 2013, 121–30. Heidelberg: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01466-1_11.

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Halder, Ritesh Kumar. "Particle Swarm Optimization in Global Path Planning for Swarm of Robots." In Applying Particle Swarm Optimization, 209–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_12.

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Feng, Desheng, Lixia Deng, Tao Sun, HaiYing Liu, Hui Zhang, and Yang Zhao. "Global Path Planning Based on an Improved A* Algorithm in ROS." In Lecture Notes in Electrical Engineering, 1134–44. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5912-6_84.

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Conference papers on the topic "Global path planning"

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Wang, Eric, Il-Kyu Hwang, and Yong Se Kim. "Integrated Local and Global Tool Path Planning for Feature-Based Machining." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/cie-14646.

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Abstract:
Abstract We describe an automatic machining tool path generation method that combines local tool path planning for machining features with global tool path planning. From the solid model and the tolerance specifications of the part, machining features are automatically recognized, and geometry-based precedence relations are obtained between these features. From this information, the machining sequence, tool selections, and machining conditions are determined. Machining tool paths are then generated automatically for each setup, combining local and global tool paths. Local tool paths to machine each feature are generated using successive offsetting operations. Global tool paths between features are generated incrementally by searching the adjacency graph of feature free spaces, which represents the current free space of the part. Feature free spaces are obtained by expanding the machining features through their fictitious faces. The start and end positions for the local tool paths of each feature are selected based on a heuristic method to minimize the cost of each segment of the global tool path. This automatic tool path generation method is currently being developed as part of a comprehensive machining process planning system.
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Fridman, Alex, Steven Weber, Charles Graff, and Moshe Kam. "Path Planning for Network Performance." In IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference. IEEE, 2007. http://dx.doi.org/10.1109/glocom.2007.242.

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Yu, Xiaoqiang, Qinglong Huang, Ping Wang, and Jifeng Guo. "Comprehensive Global Path Planning for Lunar Rovers." In 2020 3rd International Conference on Unmanned Systems (ICUS). IEEE, 2020. http://dx.doi.org/10.1109/icus50048.2020.9274967.

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Chesi, G., and Y. S. Hung. "Visual servoing: a global path-planning approach." In 2007 IEEE International Conference on Robotics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/robot.2007.363629.

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Park, Jong-Hun, and Uk-Youl Huh. "Potential risk-based Safe Global Path Planning." In 2012 First International Conference on Innovative Engineering Systems (ICIES). IEEE, 2012. http://dx.doi.org/10.1109/icies.2012.6530865.

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Choi, Sunglok, Jaehyun Park, Eulgyoon Lim, and Wonpil Yu. "Global path planning on uneven elevation maps." In 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, 2012. http://dx.doi.org/10.1109/urai.2012.6462928.

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Rizano, Tizar, Daniele Fontanelli, Luigi Palopoli, Lucia Pallottino, and Paolo Salaris. "Global path planning for competitive robotic cars." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760584.

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Chen, Xiaochao, Ming Zhou, Jian Huang, and Zhiwei Luo. "Global path planning using modified firefly algorithm." In 2017 International Symposium on Micro-NanoMechatronics and Human Science (MHS). IEEE, 2017. http://dx.doi.org/10.1109/mhs.2017.8305195.

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Whitty, Mark, and Jose Guivant. "Efficient global path planning during dense map deformation." In 2011 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2011. http://dx.doi.org/10.1109/icra.2011.5980113.

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Ali, Mohammed A. H., Wan Azhar B. W. Yusoff, Zamzuri B. Hamedon, Zulkifli B. M. Yusssof, and Musa Mailah. "Global mobile robot path planning using laser simulator." In 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA). IEEE, 2016. http://dx.doi.org/10.1109/roma.2016.7847828.

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