Multi-Objective Path Planning of an Autonomous Mobile Robot in Static and Dynamic Environments using a Hybrid PSO-MFB Optimisation Algorithm
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The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed path planning algorithm consists of three modules: in the first module, the path planning algorithm forms an optimised path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimises distance and follows path smoothness criteria; in the second module, any infeasible points generated by the proposed PSO-MFB Algorithm are detected by a novel Local Search (LS) algorithm and integrated with the PSO-MFB algorithm to be converted into feasible solutions; the third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. Simulations have been carried out that indicated that this method generates a feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Comparisons with previous examples in the literature are also included in the results.
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