Path Planning of an Autonomous Mobile Robot in a Dynamic Environment using Modified Bat Swarm Optimization

TL;DR

This paper introduces a modified Bat Algorithm (MFBA) for mobile robot path planning in dynamic environments, achieving collision-free, shorter, and safer paths more efficiently than the standard BA.

Contribution

The paper proposes a novel modification to the Bat Algorithm's frequency parameter, enhancing path planning performance in dynamic environments.

Findings

MFBA outperforms standard BA in collision avoidance

Paths generated by MFBA are shorter and safer

Simulation confirms efficiency of the proposed method

Abstract

This paper outlines a modification on the Bat Algorithm (BA), a kind of swarm optimization algorithms with for the mobile robot navigation problem in a dynamic environment. The main objectives of this work are to obtain the collision-free, shortest, and safest path between starting point and end point assuming a dynamic environment with moving obstacles. A New modification on the frequency parameter of the standard BA has been proposed in this work, namely, the Modified Frequency Bat Algorithm (MFBA). The path planning problem for the mobile robot in a dynamic environment is carried out using the proposed MFBA. The path planning is achieved in two modes; the first mode is called path generation and is implemented using the MFBA, this mode is enabled when no obstacles near the mobile robot exist. When an obstacle close to the mobile robot is detected, the second mode, i.e., the obstacle avoidance (OA) is initiated. Simulation experiments have been conducted to check the validity and the efficiency of the suggested MFBA based path planning algorithm by comparing its performance with that of the standard BA. The simulation results showed that the MFBA outperforms the standard BA by planning a collision-free path with shorter, safer, and smoother than the path obtained by its BA counterpart.