Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

TL;DR

This paper introduces a hybrid PSO-MFB optimization algorithm for multi-objective path planning of autonomous mobile robots, effectively handling static and dynamic environments to generate optimal, collision-free, and smooth paths.

Contribution

It presents a novel hybrid PSO-MFB algorithm combined with local search and obstacle detection modules for improved path planning in complex environments.

Findings

Generates optimal feasible paths in dynamic environments

Outperforms conventional grid-based methods in path optimality

Effective obstacle detection and avoidance capabilities

Abstract

The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments. The problem is solved by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The proposed path planning 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 algorithm consists of three modules. The first module forms an optimized path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimizes distance and follows path smoothness criteria. The second module detects any infeasible points generated by the proposed hybrid PSO-MFB Algorithm by a novel Local Search (LS) algorithm integrated with the hybrid 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. The simulation results indicate that this method generates an optimal feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Moreover, compared to recent path planning techniques, simulation results show that the proposed hybrid PSO-MFB algorithm is highly competitive in terms of path optimality.