In this paper we introduce an improvement in the path planning algorithm for the humanoid soccer playing robot which uses Ferguson splines and PSO (Particle Swarm Optimization). The objective of the algorithm is to find a path through other playing robots to the ball, w More
In this paper we introduce an improvement in the path planning algorithm for the humanoid soccer playing robot which uses Ferguson splines and PSO (Particle Swarm Optimization). The objective of the algorithm is to find a path through other playing robots to the ball, which should be as short as possible and also safe enough. Ferguson splines create preliminary paths using random generated parameters. The random parameters are then iteratively fed into the PSO for optimization and converging to optimal path. Our proposed method makes a balance between the path shortness and the safety which makes it more efficient for humanoid soccer playing robots and also for any other crowded environment with various moving obstacles. Experimental results show that our proposed algorithm converges in at most 60 iterations with the average accuracy of 92% and the maximum path length overhead of 14% for planning the shortest and yet safest path.
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