Open Access

ARTICLE

Obstacle Avoidance Capability for Multi-Target Path Planning in Different Styles of Search

by Mustafa Mohammed Alhassow^1,*, Oguz Ata², Dogu Cagdas Atilla¹

1 Department of Electrical and Computer Engineering, Altinbas University, Istanbul, 34217, Turkey
2 Department of Software Engineering, Altinbas University, Istanbul, 34217, Turkey

* Corresponding Author: Mustafa Mohammed Alhassow. Email:

(This article belongs to the Special Issue: Intelligent Manufacturing, Robotics and Control Engineering)

Computers, Materials & Continua 2024, 81(1), 749-771. https://doi.org/10.32604/cmc.2024.055592

Received 02 July 2024; Accepted 20 August 2024; Issue published 15 October 2024

Abstract

This study investigates robot path planning for multiple agents, focusing on the critical requirement that agents can pursue concurrent pathways without collisions. Each agent is assigned a task within the environment to reach a designated destination. When the map or goal changes unexpectedly, particularly in dynamic and unknown environments, it can lead to potential failures or performance degradation in various ways. Additionally, priority inheritance plays a significant role in path planning and can impact performance. This study proposes a Conflict-Based Search (CBS) approach, introducing a unique hierarchical search mechanism for planning paths for multiple robots. The study aims to enhance flexibility in adapting to different environments. Three scenarios were tested, and the accuracy of the proposed algorithm was validated. In the first scenario, path planning was applied in unknown environments, both stationary and mobile, yielding excellent results in terms of time to arrival and path length, with a time of 2.3 s. In the second scenario, the algorithm was applied to complex environments containing sharp corners and unknown obstacles, resulting in a time of 2.6 s, with the algorithm also performing well in terms of path length. In the final scenario, the multi-objective algorithm was tested in a warehouse environment containing fixed, mobile, and multi-targeted obstacles, achieving a result of up to 100.4 s. Based on the results and comparisons with previous work, the proposed method was found to be highly effective, efficient, and suitable for various environments.

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Keywords

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