Abstract:

Resorting to certain heuristic functions to guide the search, the computation efficiency of prevailing path planning algorithms such as A*, D* and their variants is solely determined by how good the heuristic function approximates the true path cost. In this study, we propose a novel approach to learn heuristic functions using a deep neural network (DNN) to improve the computation efficiency. Even though DNNs have been widely used for object segmentation, natural language processing, and perception, their role in helping to solve path planning has not been well investigated. This work shows how DNNs can be applied to path planning problems and what kind of loss functions is suitable for learning such a heuristic. Our preliminary results show that an appropriately designed and trained DNN can learn a heuristic which effectively guides conventional path planning algorithms and speeds up the path generation.