Failure Robot Path Complementation for Robot Swarm Mission Planning

碩士 === 國立虎尾科技大學 === 自動化工程系碩士班 === 105 === Unmanned vehicle is applied widely in environmental explorations nowadays, especially for the tasks where human beings are not able to reach. Due to limited capacities like power supply, it is almost impossible for any single one unmanned vehicle to complete...

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Main Authors: Bo-Yu Chen, 陳柏宇
Other Authors: 李孟澤
Format: Others
Language:zh-TW
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/856bdw
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spelling ndltd-TW-105NYPI51460032019-09-21T03:32:43Z http://ndltd.ncl.edu.tw/handle/856bdw Failure Robot Path Complementation for Robot Swarm Mission Planning 群組機器人路徑規劃及失效後之路徑補償研究 Bo-Yu Chen 陳柏宇 碩士 國立虎尾科技大學 自動化工程系碩士班 105 Unmanned vehicle is applied widely in environmental explorations nowadays, especially for the tasks where human beings are not able to reach. Due to limited capacities like power supply, it is almost impossible for any single one unmanned vehicle to complete a large assignment with multiple designated location to visit. As well as failure robot task won’t complemented when each car failing. Therefore, multiple unmanned vehicles (Multi-Agent System, MAS) are required as well as the well-planned routes to minimize unnecessary consumption and waste on time, distance and energy/fuels needed. In addition, robot cars are able to avoid no-travel zone and the task of failure robot was complemented by the other. Same as other large mathematic model, heuristic algorithm was used to obtain an approximate solution within a reasonable timeline for this research. First, establish distance array. If any two points through the no-travel zone, use A^* algorithm to find the alternative path to avoid no-travel zone. Then, a two-phase architecture was applied. In the 1st phase, Tabu search and 2-Opt exchange method were used to figure out the optimal path for visiting all target nodes, and then the initial solution by splitting it into multiple clusters. In the 2nd phase, the algorithm was used with 2-Opt path exchange were used to improve the in-route and cross-route solutions. Diversification strategy was adopted to approach the global optimal solution rather than a regional one. Once the objectives mentioned above were accomplished, we dispatched several robot cars to operate simultaneously on the routes we planned ahead. If one of the autonomous cars failing, new path will be programmed and reassigned to autonomous car of remaining by the ground station. until all the target points are visited. In the end, computer simulations and real vehicle had been accomplished in this research. 李孟澤 2017 學位論文 ; thesis 80 zh-TW
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description 碩士 === 國立虎尾科技大學 === 自動化工程系碩士班 === 105 === Unmanned vehicle is applied widely in environmental explorations nowadays, especially for the tasks where human beings are not able to reach. Due to limited capacities like power supply, it is almost impossible for any single one unmanned vehicle to complete a large assignment with multiple designated location to visit. As well as failure robot task won’t complemented when each car failing. Therefore, multiple unmanned vehicles (Multi-Agent System, MAS) are required as well as the well-planned routes to minimize unnecessary consumption and waste on time, distance and energy/fuels needed. In addition, robot cars are able to avoid no-travel zone and the task of failure robot was complemented by the other. Same as other large mathematic model, heuristic algorithm was used to obtain an approximate solution within a reasonable timeline for this research. First, establish distance array. If any two points through the no-travel zone, use A^* algorithm to find the alternative path to avoid no-travel zone. Then, a two-phase architecture was applied. In the 1st phase, Tabu search and 2-Opt exchange method were used to figure out the optimal path for visiting all target nodes, and then the initial solution by splitting it into multiple clusters. In the 2nd phase, the algorithm was used with 2-Opt path exchange were used to improve the in-route and cross-route solutions. Diversification strategy was adopted to approach the global optimal solution rather than a regional one. Once the objectives mentioned above were accomplished, we dispatched several robot cars to operate simultaneously on the routes we planned ahead. If one of the autonomous cars failing, new path will be programmed and reassigned to autonomous car of remaining by the ground station. until all the target points are visited. In the end, computer simulations and real vehicle had been accomplished in this research.
author2 李孟澤
author_facet 李孟澤
Bo-Yu Chen
陳柏宇
author Bo-Yu Chen
陳柏宇
spellingShingle Bo-Yu Chen
陳柏宇
Failure Robot Path Complementation for Robot Swarm Mission Planning
author_sort Bo-Yu Chen
title Failure Robot Path Complementation for Robot Swarm Mission Planning
title_short Failure Robot Path Complementation for Robot Swarm Mission Planning
title_full Failure Robot Path Complementation for Robot Swarm Mission Planning
title_fullStr Failure Robot Path Complementation for Robot Swarm Mission Planning
title_full_unstemmed Failure Robot Path Complementation for Robot Swarm Mission Planning
title_sort failure robot path complementation for robot swarm mission planning
publishDate 2017
url http://ndltd.ncl.edu.tw/handle/856bdw
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