Journal Article

Mobile robot path planning using improved mayfly optimization algorithm and dynamic window approach

Li, Weimin, Wang, Lei, Wang, Hai, Tan, Tielong, Zou, Awei, Cai, Jingcao

2023

Overview

In order to solve the problems of the basic mayfly optimization algorithm (MOA) in the field of robot path planning, such as slow convergence speed, poor accuracy, insufficient stability, and only applicable to static environment, a fusion algorithm combining improved mayfly optimization algorithm and dynamic window approach is proposed in this paper. Firstly, an improved mayfly optimization algorithm based on Q-learning (IMOA-QL) is proposed to solve robot global path planning problem. Q-learning is taken as the core of the improved mayfly optimization algorithm. For the basic MOA, the inertia weight and positive attraction coefficients are set to fixed values, which are not reasonable and will make the global search ability unbalanced, fall into local optimization easily, and also limit the iteration speed. In this paper, the parameters are adaptively adjusted based on Q-learning, and the appropriate parameters are selected according to the fitness of each mayfly. Meanwhile, the memory mechanism is introduced to speed up the convergence speed and implement the global path planning. Then, the global path nodes are extracted as the sub-target points, and the improved dynamic window approach is used to carry out the local path planning, which effectively improves the dynamic real-time avoidance ability. In order to verify the effectiveness of the proposed IMOA-QL algorithm in this paper, 20 random simulation experiments are carried out in the 100 × 100 static map environment and compared with the basic mayfly optimization algorithm (MOA) and the mayfly optimization algorithm based on linear adaptive inertia weight (MOA-LAIW). The results show that the average path length of the proposed IMOA-QL algorithm is reduced by 4.48% and 2.17% compared with MOA and MOA-LAIW in simple environment, and the average path length of the proposed IMOA-QL algorithm is reduced by 6.58% and 3.24% compared with MOA and MOA-LAIW in complex environment. In 20 experiments, the average variance of the proposed IMOA-QL algorithm in this paper is reduced by 74.15% and 57.67% compared with MOA and MOA-LAIW in simple environment, and the average variance of the proposed IMOA-QL algorithm is reduced by 51.22% and 38.67% in complex environment compared with MOA and MOA-LAIW. The simulation results show that the proposed IMOA-QL algorithm has significantly improved the accuracy and speed of solution. Moreover, dynamic obstacles are added in the static environment to carry out the simulation test of the fusion dynamic path planning algorithm. The results show that a fusion algorithm combining improved mayfly optimization algorithm and dynamic window approach in this paper can better complete the path planning task well in the complex dynamic environment.

• Publisher: Springer US,Springer Nature B.V
• Subject: Algorithms / Compilers / Computer Science / Convergence / Genetic algorithms / Inertia / Interpreters / Local optimization / Machine learning / Optimization algorithms / Parameters / Path planning / Planning / Processor Architectures / Programming Languages / Real time / Robots / Teaching methods / Variance / Velocity