An efficient neural network method for real-time motion planning with safety consideration
An efficient neural network method is proposed for real-time motion planning of a mobile robot or a multi-joint robot manipulator with safety consideration in a nonstationary environment. The optimal robot motion is planned through the dynamic neural activity landscape of the biologically inspired neural network without any prior knowledge of the dynamic environment and without any learning procedures. The planned real-time comfortable path does not suffer from the too close or the too far problems. The proposed model is stable, computationally efficient, and not very sensitive to parameter variations. The effectiveness and the efficiency are demonstrated through simulation and comparison studies.
neural networks real-time motion planning obstacle clearance global stability efficient algorithms