Path Planning of Mobile Robots Based on Improved Genetic Algoritm

Abstract

With the development of intelligent manufacturing, whether from the consideration of capacity, efficiency, or convenience, the requirements for mobile robots are increasing, reasonable regional path planning is one of the most critical needs, and a genetic algorithm is the best way to solve this problem, but in some complex working environments, traditional genetic algorithms will cause some problems, such as the path is not smooth, the steering angle is too large, the number of turns is large, etc. In this paper, an improved genetic algorithm is utilized to optimize the path-planning problem of mobile robots to circumvent the common issues arising from other approaches. The Improved Genetic Algorithm (IGA) has emerged as a significant advancement in the field of optimization techniques. By incorporating adaptive features, this refined approach yields enhanced performance and accuracy when compared to traditional genetic algorithms. Building on the foundational principles of evolutionary computation, the IGA employs innovative strategies, such as adaptive crossover and mutation operators, to navigate complex solution spaces effectively. It can also reduce computation time and increase efficiency by considering various considerations, such as environmental constraints and avoiding obstacle.