A comparative review on mobile robot path planning : classical or meta-heuristic methods?

Wahab, MNA; Nefti-Meziani, S; Atyabi, A

Abstract

The involvement of Meta-heuristic algorithms in robot motion planning has attracted the attention of researchers in the robotics community due to the simplicity of the approaches and their effectiveness in the coordination of the agents. This study explores the implementation of many meta-heuristic algorithms, e.g. Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) in multiple motion planning scenarios. The study provides comparison between multiple meta-heuristic approaches against a set of well-known conventional motion planning and navigation techniques such as Dijkstra’s Algorithm (DA), Probabilistic Road Map (PRM), Rapidly Random Tree (RRT) and Potential Field (PF). Two experimental environments with difficult to manipulate layouts are used to examine the feasibility of the methods listed. several performance measures such as total travel time, number of collisions, travel distances, energy consumption and displacement errors are considered for assessing feasibility of the motion planning algorithms considered in the study. The results show the competitiveness of meta-heuristic approaches against conventional methods. Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.

Citation

Wahab, M., Nefti-Meziani, S., & Atyabi, A. (2020). A comparative review on mobile robot path planning : classical or meta-heuristic methods?. Annual Reviews in Control, 50, 233-252. https://doi.org/10.1016/j.arcontrol.2020.10.001

• Journal Article Type: Article
• Acceptance Date: Oct 4, 2020
• Online Publication Date: Oct 16, 2020
• Publication Date: Oct 16, 2020
• Deposit Date: Oct 19, 2020
• Publicly Available Date: Oct 16, 2021
• Journal: Annual Reviews in Control
• Print ISSN: 1367-5788
• Publisher: Elsevier
• Volume: 50
• Pages: 233-252
• DOI: https://doi.org/10.1016/j.arcontrol.2020.10.001
• Publisher URL: https://doi.org/10.1016/j.arcontrol.2020.10.001
• Related Public URLs: http://www.journals.elsevier.com/annual-reviews-in-control/
• Additional Information: Funders : Universiti Sains Malaysia; Projects : PKOMP/6315262; Grant Number: PKOMP/6315262