PID Path-Planning Robot Optimized by Genetic Algorithm

With the rapid development of automation and intelligent technology, path planning and control technology has become an important part of robotics and has important applications in industrial production and intelligent systems. Although there are abundant algorithm optimization methods for path planning, the research on PID control strategy optimization in robot path planning is limited. The purpose of this paper is to study the application of a genetic algorithm in PID parameter optimization and to evaluate its performance in terms of stability and rapidity. Experimental results show that the PID controller optimized by genetic algorithm can significantly improve the path planning accuracy and dynamic response speed of the robot. In addition, this paper also discusses the application challenges and future research directions of genetic algorithms in path planning, including algorithm parameter selection, computational efficiency improvement, and multi-objective optimization. Finally, this paper concludes that the method of optimizing PID control strategy based on genetic algorithm not only performs well in existing applications but also has a wide range of development prospects and application potential, which is worthy of further exploration and research.