Adaptive Deep Path: Efficient Coverage of a Known Environment under Various Configurations

Abstract

Coverage path planning of a known environment sees a variety of applications, including cleaning, surveillance, agriculture and 3D printing. Most approaches employ hard-coded heuristics or other application-specific requirements, making them hard to extend to other problem scenarios or “configurations,” such as different motion strategies or robot size. This work presents a unifying, general, and adaptive framework, called adaptive deep path (AD Path), for coverage path planning problems under a variety of configurations. It can improve path efficiency with respect to both path length and number of turns, and can flexibly accommodate different problem configuration options. We evaluate AD Path against a state-of-the-art baseline in four complex environments with different configurations. We show that our approach can produce efficient paths; our experimental results show that AD Path can reduce the path length by 21.8% and the number turns by 38.6% on average compared with the baseline.