Real-time robot motion planning and tracking control using a neural dynamics based approach

In this paper, a novel neural dynamics based approach is proposed for realtime motion planning and tracking control of a mobile robot in an arbitrarily changing environment. The dynamic environment is represented by a neural activity landscape of a neural network, where each neuron in the topologically organized neural network is characterized by a shunting equation that is derived from Hodgkin and Huxley's (1952) biological membrane equation. The collision-free robot motion is generated in real-time through the activity landscape without any explicit searching procedures and without any prior knowledge of the dynamic environment. The real-time tracking control of the robot to follow the planned dynamic motion path is designed using shunting equations as well. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies. Simulation in several computer-synthesized virtual environments further demonstrates the advantages of the proposed approach with encouraging experimental results.