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

Wahab, MNA, Nefti-Meziani, S and Atyabi, A 2020, 'A comparative review on mobile robot path planning : classical or meta-heuristic methods?' , Annual Reviews in Control, 50 , pp. 233-252.

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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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Annual Reviews in Control
Publisher: Elsevier
ISSN: 1367-5788
Related URLs:
Funders: Universiti Sains Malaysia
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 19 Oct 2020 10:23
Last Modified: 15 Feb 2022 17:19
URI: https://usir.salford.ac.uk/id/eprint/58591

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