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Path Planning and Energy Efficiency of Heterogeneous Mobile Robots Using Cuckoo–Beetle Swarm Search Algorithms with Applications in UGV Obstacle Avoidance

Dechao Chen Orcid Logo, Zhixiong Wang, Guanchen Zhou, Shuai Li Orcid Logo

Sustainability, Volume: 14, Issue: 22, Start page: 15137

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.3390/su142215137

Abstract

In this paper, a new meta-heuristic path planning algorithm, the cuckoo–beetle swarm search (CBSS) algorithm, is introduced to solve the path planning problems of heterogeneous mobile robots. Traditional meta-heuristic algorithms, e.g., genetic algorithms (GA), particle swarm search (PSO), beetle sw...

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Published in: Sustainability
ISSN: 2071-1050
Published: MDPI AG 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa62116
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Abstract: In this paper, a new meta-heuristic path planning algorithm, the cuckoo–beetle swarm search (CBSS) algorithm, is introduced to solve the path planning problems of heterogeneous mobile robots. Traditional meta-heuristic algorithms, e.g., genetic algorithms (GA), particle swarm search (PSO), beetle swarm optimization (BSO), and cuckoo search (CS), have problems such as the tenancy to become trapped in local minima because of premature convergence and a weakness in global search capability in path planning. Note that the CBSS algorithm imitates the biological habits of cuckoo and beetle herds and thus has good robustness and global optimization ability. In addition, computer simulations verify the accuracy, search speed, energy efficiency and stability of the CBSS algorithm. The results of the real-world experiment prove that the proposed CBSS algorithm is much better than its counterparts. Finally, the CBSS algorithm is applied to 2D path planning and 3D path planning in heterogeneous mobile robots. In contrast to its counterparts, the CBSS algorithm is guaranteed to find the shortest global optimal path in different sizes and types of maps.
Keywords: path planning and energy efficiency; meta-heuristic algorithm; levy flight; heterogeneous mobile robots; search orientation
College: Faculty of Science and Engineering
Funders: This work was supported in part by the National Natural Science Foundation of China under Grant 62276085 and Grant 61906054, and in part by the Natural Science Foundation of Zhejiang Province under Grant LY21-F030006.
Issue: 22
Start Page: 15137

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