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Dynamic Distributed PSO joints elites in Multiple Robot Path Planning Systems: theoretical and practical review of new ideas

机译:多机器人路径规划系统中的动态分布式PSO联合精英:新思想的理论和实践回顾

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Path planning problem for large number of robots is a quite challenging problem in mobile robotics since their control and coordination becomes unreliable and sometimes unfeasible. Particle Swarm Optimization (PSO) has been demonstrated to be a useful technique in the field of robotic research. This paper discusses an optimal path planning algorithm based on a Dynamic Distributed Particle Swarm Optimization Algorithm (D 2 PSO). The purpose of this approach is to find collision free optimal paths using two local optima detectors. This would add diversity to the population and hence avoid stagnation problem. The results show that the D 2 PSO has a better ability to get away from local optimums than the distributed PSO (dPSO). Simulations prove that this methodology is effective for every robot in multi-robot framework to discover its own proper path from the start to the destination position with minimum distance and no collision with obstacles.
机译:在移动机器人中,大量机器人的路径规划问题是一个极具挑战性的问题,因为它们的控制和协调变得不可靠,有时甚至不可行。粒子群优化(PSO)已被证明是机器人研究领域中的一种有用技术。本文讨论了基于动态分布式粒子群优化算法(D 2 PSO)的最优路径规划算法。这种方法的目的是使用两个局部最优检测器找到无碰撞的最佳路径。这将增加人口的多样性,从而避免停滞问题。结果表明,与分布式PSO(dPSO)相比,D 2 PSO具有更好的摆脱局部最优的能力。仿真证明,该方法对于多机器人框架中的每个机器人都能有效地发现自己从起点到目的地位置的正确路径,并且距离最短且不会与障碍物碰撞。

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