As path planning is an NP-hard problem it can be solved by multi-objective algorithms. In this article, we propose a multi-objective path planning algorithm which consists of three steps: (1) the first step consists of optimizing a path by the hybridization of the Grey Wolf optimizer-particle swarm optimization algorithm, it minimizes the path distance and smooths the path. (2) the second step, all optimal and feasible points generated by PSO–GWO algorithm are integrated with Local Search technique to convert any infeasible point into feasible point solution, the last step (3) depends on collision avoidance and detection algorithm, where mobile robot detects the presence of an obstacle in its sensing circle and then avoid them using collision avoidance algorithm. The proposed method is further improved by adding the mutation operators by evolutionary, it further solves path safety, length, and smooths it further for a mobile robot. Different simulations have been performed under numerous environments to test the feasibility of the proposed algorithm and it is shown the algorithm produces a more feasible path with a short distance and thus proves that it overcomes the shortcomings of other conventional techniques.

由于路径规划是一个NP难题，因此可以通过多目标算法解决。在本文中，我们提出了一种多目标路径规划算法，该算法包括三个步骤：（1）第一步包括通过使用Gray Wolf优化器-粒子群优化算法进行混合来优化路径，从而最小化路径距离和平滑路径。（2）第二步，将PSO-GWO算法生成的所有最佳和可行点与本地搜索技术集成，以将任何不可行点转换为可行点解，最后一步（3）取决于碰撞避免和检测算法，其中移动机器人检测到其感知圈中是否存在障碍物，然后使用避免碰撞算法将其避开。通过进化方法添加变异算子，对提出的方法进行了进一步的改进，进一步解决了路径安全性，长度问题，并对移动机器人进行了平滑处理。在多种环境下进行了不同的仿真，以验证该算法的可行性。结果表明，该算法在短距离内产生了一条更可行的路径，从而证明了它克服了其他常规技术的不足。