Research of the obstacle avoidance based on RBFNN for the mobile robot under dynamic environment

A new obstacle avoidance algorithm for the mobile robot is introduced. When the dynamic obstacle is in a nonlinear random movement, a Radial Basis Function Neural Network (RBFNN) is used to build the prediction model. The next location of the obstacle is predicted based on the three adjacent value of time sequence. Thus the dynamic obstacle avoidance issue is converted into the instantaneous static one and the realization of real-time planning is achieved. The prediction model performance of RBFNN has been compared with a Back Propagation Neural Network (BPNN) forecast model which is normally used. The results show that RBFNN model has the higher forecast accuracy and faster learning rate. Combined with the designed N/M data division, the model is very suitable for systems of nonlinear time series prediction.