An improved PSO-based path planning algorithm for humanoid soccer playing robots

In this paper we introduce an improvement in the path planning algorithm of the humanoid soccer playing robot which uses Ferguson splines and PSO (Particle Swarm Optimization). Ferguson splines create preliminary paths by using random generated parameters. The random parameters are then iteratively feed into the PSO for optimization and converging to optimal paths. The objective of the algorithm is to find a path between the humanoid soccer playing robot and the ball which should be as short as possible and yet satisfying the specified safety in the path in terms of the distance from the obstacles. Our proposed method make a balance between the path shortness and the safety which makes it more efficient in the specified case study for humanoid soccer playing robots and also any path planning among various obstacles in other crowded environments. Finally the experimental results show that our proposed algorithm converges in at most 60 iterations with the average accuracy of 92% and path length overhead of 14%, planning the shortest and yet safest path.