A neural computational algorithm for coverage path planning in changing environments

Coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications. In this paper, a novel biologically inspired neural computational algorithm is proposed for coverage path planning with sudden changes and moving obstacles in a varying environment. The dynamics of each neuron in the topologically organized neural network is characterized by an additive equation derived from Hodgkin and Huxley´s (1952) membrane equation. The computational complexity linearly depends on the neural network size. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally efficient. Two case studies of coverage path planning in changing environments are conducted to demonstrate the effectiveness of the proposed algorithm.