Open AccessAugmented Value Iteration Networks with Artificial Potential Fields for Path Planning
Author(s) -
Xiang Yang Jin,
Wei Lan,
Pengyao Yu,
Wang Tian-lin
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2203/1/012051
Subject(s) - motion planning , computer science , computation , path (computing) , generalization , mathematical optimization , a priori and a posteriori , grid , route planning , shortest path problem , any angle path planning , algorithm , mathematics , artificial intelligence , theoretical computer science , mathematical analysis , philosophy , geometry , epistemology , robot , programming language , graph
Value iterative networks (VINs), as a differentiable path planning method, taking environmental images as input, can solve path planning problems in new and unseen environments with powerful generalization capabilities. But the planning success rate decreases rapidly as the planning space scales. We propose the potential field augmented VIN by replacing the environmental map with the potential map as input and implementing this process in the form of dilated convolutions, giving more a priori information to the subsequent planning computation while hardly increasing the computational cost. Experiments on 2D grid-world show that the improved method has higher path planning success rates and smaller difference of predicted paths from shortest paths, as well as smaller performance degradation with increasing map size.