ABSTRACT

Recent trends in path planning have led to a proliferation of studies that find solutions to the path planning problems in an unknown cluster environment. This study aims to find an optimum impact-free path length for a mobile robot with a multi-objective optimization approach. The multi-objective optimization problem is formulated by using path length and a safety aspect as the two objectives. A hybrid population-based optimization algorithm, i.e. the hybrid particle swarm and chemical reaction optimization (HPCRO) algorithm, has been used to obtain a smooth path for the robot in an unknown environment with circular and/or polygonal obstacles. The results of the HPCRO algorithm are then compared with those of genetic algorithms, chemical reaction optimization and particle swarm optimization. Some statistical tests are performed to illustrate the superiority and potential applicability of the hybrid algorithm. The results of the hybrid algorithm are encouraging in terms of cost function value and computational cost.

摘要

路径规划的最新趋势已导致大量研究，这些研究为未知集群环境中的路径规划问题寻找解决方案。这项研究旨在为采用多目标优化方法的移动机器人找到最佳的无冲击路径长度。以路径长度和安全性为两个目标，提出了多目标优化问题。基于混合总体的优化算法，即混合粒子群和化学反应优化（HPCRO）算法已用于在未知环境中为具有圆形和/或多边形障碍的机器人获取平滑路径。然后将HPCRO算法的结果与遗传算法，化学反应优化和粒子群优化的结果进行比较。进行一些统计测试以说明混合算法的优越性和潜在适用性。混合算法的结果在成本函数值和计算成本方面令人鼓舞。