Multi-objective optimisation path planning with moving target



Path planning, or finding a collision-free path for mobile robots between starting position and its destination is a critical problem in robotics. This study is concerned with the multiobjective optimization path planning problem of autonomous mobile robots with moving targets in unknown dynamic environment, with three objectives considered; path security, length and smoothness. Three actions are presented in the study. The first step is to combine the Bat algorithm (BA) with Particle Swarm Optimization (PSO) algorithms. The purpose of PSO is to optimize two important parameters of BA algorithm to minimize distance and smooth the path. The second step is to convert the generated infeasible points into feasible ones using a new Local Search technique (LS). The third component of this study focuses on the development of the detection of obstacles using sensors and the development of an obstacle avoidance strategy based on simulating human walking in a dark room. Several simulations with varying scenarios are run to test the validity of the proposed solution. The results show that the mobile robots are able to travel clearly and completely safe with short path, proving the effectiveness of this method.



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