A Biological Inspired Approach to Collision-Free Path Planning and Tracking Control of a Mobile Robot
Simon X. Yang*, Max Q.-H. Meng**, Gavin X. Yuan*, and Peter X. Liu***
*Advanced Robotics and Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph
Guelph, ON, N1G 2W1, Canada
**Department of Electronic Engineering, Chinese University of Hong Kong, Shatin, N.T., Hong Kong
***Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, K1S 5B6, Canada
In this paper, a novel biologically inspired neural network approach is proposed for real-time collision-free path planning and tracking control of a nonholonomic mobile robot in a dynamic environment. The real-time collision-free robot trajectory is generated by a topologically organized neural network, where the dynamics of each neuron is characterized by a shunting equation derived from Hodgkin and Huxley’s biological membrane equation. The dynamically changing environment is represented by the dynamic activity landscape of the neural network, where the neural activity propagation is subject to the nonholonomic kinematic constraint of the mobile robot. The tracking velocities for the mobile robot are generated by a novel neural dynamics based controller, which is based on two shunting equations and the conventional backstepping technique. Unlike the conventional backstepping controllers suffer from sharp jumps, the proposed tracking controller can generate smooth and continuous commands. The effectiveness and efficiency of the proposed approach are demonstrated through simulation and comparison studies.