Motion path planning of soccer training auxiliary robot based on genetic algorithm in fixed-point rotation environment

Abstract

Soccer training assisted robot system is a combination of robotics and artificial intelligence. Motion path planning is an important part of soccer training assisted robot decision system. Path planning aims to find an optimal path, complete dynamic and static obstacle avoidance, and thus can timely and quickly carry out the path planning of the soccer training assisted robot cooperation strategy. Aiming at the problem of soccer training auxiliary robot motion path planning, this paper proposes a global path planning method based on genetic algorithm, which avoids the problem of unsmooth path and large computation. Under the premise of ensuring the completeness of search, this method transforms the soccer training assisted robot path planning problem into the path search problem of the soccer training assisted robot system center. Under the premise of using the fixed-point rotation method on a large scale, the genetic algorithm is adopted for the case of some special narrow-channel environments, and the action area is determined by simple search. Finally, the planning path of each soccer training-assisted robot in the system is obtained. Finally, the simulation results verify the effectiveness of the algorithm.