Heuristically arrival time field-biased (HeAT) random tree: An online path planning algorithm for mobile robot considering kinodynamic constraints

摘要

This paper presents a new path planning algorithm for mobile robot in dynamic environments. We calculate the arrival time field as a bias which gives larger weights for shorter and safer paths towards a goal. We apply a randomized path search guided by the arrival time field for constructing the path considering kinematic and dynamic (kinodynamic) constraints of actual robot. We also consider path quality by adding heuristic constraints such as directing the initial heading of the robot and reducing unstable movements of the robot by using a heading criterion. The path will be extracted by backtracking the nodes which reach the goal area to the root of the tree generated by the randomized search. We provide a brief comparison between our algorithm and other existing algorithms. Simulation and experimental results prove that our algorithm is fast enough to be applied to the real robot and show the effectiveness of the algorithm for handling kinodynamic problems.