Abstract
A reliable navigation of a mobile robot in a dynamic change condition is demonstrated. To complete non-collision movement when the robot is in a complex dynamic change condition, a trajectory prediction system is developing to reliably predict the target trajectory during occlusion caused by obstacles. Algorithms for the device to perform path planning and trajectory prediction are described. After the information of the dynamic workspace is constructed, a path planning algorithm based on neural network is used to realize path planning. Neural network retraining is automatically triggered if major changes in the target behavior pattern are detected. The path planner uses super quadratic potential fields and incorporates a height change mechanism that is triggered where necessary and in order to get over short massif or pass by the taller obstacle. Simulation results for the system are presented.
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