Path planning with the derivative of heuristic angle based on the GBFS algorithm - Lim, Daehee; Jo, Jungwook (Frontiers in Robotics and AI, 2022)
Robots used in extreme environments need a high reactivity on their scene. For fast response, they need the ability to find the optimal path in a short time. In order to achieve this goal, this study introduces WA*DH+, an improved version of WA*DH (weighted A* with the derivative of heuristic angle). In some path planning scenes, WA*DH cannot find suboptimal nodes with the small inflation factor called the critical value due to its filtering method. It is hard to develop a new filtering method, so this study inflated the suboptimality of the initial solution instead. Critical values vary in every path planning scene, so increasing the inflation factor for the initial solution will not be the solution to our problem. Thus, WA*DH + uses the GBFS algorithm with the infinitely bounded suboptimal solution for its initial solution. Simulation results demonstrate that WA*DH + can return a better solution faster than WA*DH by finding suboptimal nodes in the given environment.