Heuristic template approach to complete coverage path planning of mobile robot

In this paper, a novel synthetic planning method based on biologically inspired neural network for complete coverage path planning of mobile robot is developed, which integrates template-based model, heuristic searching and obstacle approaching algorithm. The biological neural network described by the shunting equation can be used to build the environment information model of the workspace of mobile robot. The template-based model, heuristic searching and obstacle approaching algorithm are synthesized a heuristic template approach to plan an efficient collision-free path for mobile robot in complicated static environment. The obstacle approaching algorithm is employed to cover the vicinity of an irregular obstacle so that the coverage rate of the planning path in the workspace of mobile robot is further improved. The simulations show that the performance of the path planned by the proposed synthetic method is superior to the one generated by the method based on biologically inspired neural network.