A biological inspired neural network approach to real-time collision-free motion planning of a nonholonomic car-like robot

In this paper, a novel biologically inspired neural network approach is proposed for real-time motion planning with obstacle avoidance of a nonholonomic car-like robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. The robot configuration space constitutes the state space of the neural network. There are only local connections among neurons. Thus the computational complexity linearly depends on the neural network size. The neural activity propagation is subject to the kinematic constraints of the nonholonomic car-like robot. The real-time robot motion is planned through the dynamic neural activity landscape without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of the robot movement. Therefore the model algorithm is computationally efficient. The stability of the neural network system is proved by qualitative analysis and a Lyapunov stability theory. Simulation in several computer-synthesized virtual environments further demonstrates the advantages of the proposed approach with encouraging experimental results.