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

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.

A review on energy efficiency in autonomous mobile robots

“…The path planning algorithm aims to generate the shortest path for AMR navigation while considering various constraints such as obstacle avoidance, task requirements and energy consumption. The path planning algorithm reduces AMR energy consumption by minimizing the distance traveled (Alajlan et al , 2017; Yuan et al , 2017; Dechao et al , 2022; Shukla and Kumar, 2022) and avoiding unnecessary movements (Singh et al , 2015; Inderjeet Singh et al , 2020; Shukla and Kumar, 2022). In addition, some algorithms can consider the energy characteristics of different paths and choose the path with the lowest energy consumption (Jaroszek and Trojnacki, 2014; Dogru and Marques, 2015, 2016; Go Sakayori and Ishigami, 2017; Yuan et al , 2017).…”

“…These algorithms can generally be divided into two main categories: algorithm types and optimization directions. Algorithm types include: the Dijkstra algorithm (Zhang et al , 2021; Emna et al , 2022); the A* algorithm (Liu and Sun, 2011; Jaroszek and Trojnacki, 2014; Liu and Sun, 2014; Sakayori and Ishigami, 2017; Liu et al , 2020; Liu et al , 2021; Shukla and Kumar, 2022); The optimal control theory (Kim and Kim, 2012; Kim and Kim, 2014) ; Tabu-search (Wei et al , 2012); the genetic algorithm (GA); ant colony optimization algorithm (Wongwirat and Anuntachai, 2011; Anuntachai et al , 2014); bee swarm optimization (BSO); PSO; cuckoo–beetle swarm search (CBSS) algorithm (Dechao et al , 2022); and deep reinforcement learning algorithm (Nguyen et al , 2020). …”

“…Dechao et al (2022) compared the performance of various algorithms under specific conditions. Table 5 presents a summary of the experimental data collected.…”

“…The optimal control theory (Kim and Kim, 2012; ; Tabu-search (Wei et al, 2012); the genetic algorithm (GA); ant colony optimization algorithm (Wongwirat and Anuntachai, 2011;Anuntachai et al, 2014); bee swarm optimization (BSO); PSO; cuckoo-beetle swarm search (CBSS) algorithm (Dechao et al, 2022); and deep reinforcement learning algorithm (Nguyen et al, 2020).…”

An Improved Future Search Algorithm Based on the Sine Cosine Algorithm for Function Optimization Problems

“…In recent years, scholars have proposed many advanced metaheuristic algorithms, such as african vultures optimization algorithm (AVOA) [5] , grey wolf optimizer (GWO) [6] , crow search algorithm (CSA) [7] , artificial butterfly optimization (ABO) [8] , gravitational search algorithm (GSA) [9] , chao game optimization (CGO) [10] , wild horse optimizer (WHO) [11] , whale optimization algorithm (WOA) [12] , equilibrium optimizer (EO) [13] , teaching learning based optimization (TLBO) [14] , symbiotic organisms search(SOS) [15] , Electro-search algorithm (ES) [16] , water wave optimization (WWO) [17] , moth flame optimization algorithm (MFO) [18] , spotted hyena optimizer (SHO) [19] , mine blast algorithm (MBA) [20] , and so on [21][22][23][24][25][26] . The high efficiency of optimization algorithms sets the strong support in industry fields, such as global optimization problem [27][28][29][30] , 0-1 knapsack problem [31][32][33][34] , path planning problems [35][36][37][38] , image fields [39][40] , and so on [41][42][43][44] .…”