Applying edge computing to analyse path planning algorithm in college football training

In order to assist college football training, an edge computing-based path planning algorithm for the football robot, and an improved PSO (particle swarm optimization) algorithm are proposed. First, the cartesian coordinate system is transformed into the polar coordinate system, and the coordinates of the robots’ path coding are calculated and displayed in the polar coordinate system. Then, the robots’ coordinates in the current, the next, and the obstacle point are measured and compared to determine whether path planning is needed. Consequently, the PSO, optimized by the “concentration selection” strategy in the artificial immune algorithm, is chosen for path planning. The experiments show that with the improved PSO, five robots of 0.4 m in radius and moving less than 2 m/s can be avoided easily. The static obstacle avoidance experiment shows that obstacles can be avoided more easily, using the path planned through improved PSO. The dynamic obstacle avoidance experiments indicate that the robot can avoid all moving obstacles, blocking in front, intercepting from all sides, and tracking from behind timely. The experiments prove that the proposed path planning is effective, which has reference value for promoting the application of path planning algorithms in college football training.