| Summary: | 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.
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