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

Dechao Chen (), Zhixiong Wang, Guanchen Zhou and Shuai Li
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Dechao Chen: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Zhixiong Wang: The HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China
Guanchen Zhou: The HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China
Shuai Li: The College of Engineering, Swansea University, Swansea SA1 7EN, UK

Sustainability, 2022, vol. 14, issue 22, 1-23

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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