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Solving Travelling Salesman Problem and Mapping to Solve Robot Motion Planning through Genetic Algorithm Principle

机译:用遗传算法原理求解旅行商问题并映射以解决机器人运动计划

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Travelling Salesman Problem (TSP) is a very old problem which has been solved in so many methodologies. The solution for the Robot Path Planning (RPP) can be derived using the methodologies used for the TSP. In this paper, Genetic Algorithm (GA) principle is employed to solve the TSP and is mapped to solve the RPP with the same principle. The both problems are defined to observe similarity between these problems and enumerated the conversion phases. In these two cases how the solutions are to be derived to implement the GA technique to accomplish the optimal path in both cases and tested for different number of cities, population space and generations. The minimum cost and mean cost of the solution space proves its giving the optimal result.
机译:旅行推销员问题(TSP)是一个非常古老的问题,已经用许多方法论解决了。机器人路径规划(RPP)的解决方案可以使用用于TSP的方法来得出。本文采用遗传算法(GA)原理求解TSP,并以相同的原理映射到求解RPP。定义这两个问题是为了观察这些问题之间的相似性,并列举了转换阶段。在这两种情况下,如何导出解决方案以实施GA技术以在两种情况下均实现最佳路径,并针对不同数量的城市,人口空间和世代进行测试。解决方案空间的最小成本和平均成本证明了它给出了最佳结果。

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