Application of improved chaotic optimization algorithm to problems with motion planning for mobile robots

The ergodicity of chaotic phenomena can avoid being trapped in local optimum. The initial values of chaotic variables and the adjustable coefficients of the second logistic mapping and the third Ulam-von Neumann mapping are discussed, and it is shown that the interval of searching is adjusted when the optimal values don't vary for a lot times. The proposed method is applied to problems with motion planning for mobile robots and the numerical simulation shows that the convergent speed of the method is faster and the accuracy is better.