Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning
This addresses the challenge of online path planning for robots in unknown areas, such as in lawn mowing or search-and-rescue, with incremental improvements over existing methods.
The paper tackled the problem of coverage path planning in unknown environments by proposing a deep reinforcement learning approach with an egocentric map representation and a novel reward term, achieving performance that surpasses previous RL-based and specialized methods across multiple variations.
Coverage path planning (CPP) is the problem of finding a path that covers the entire free space of a confined area, with applications ranging from robotic lawn mowing to search-and-rescue. When the environment is unknown, the path needs to be planned online while mapping the environment, which cannot be addressed by offline planning methods that do not allow for a flexible path space. We investigate how suitable reinforcement learning is for this challenging problem, and analyze the involved components required to efficiently learn coverage paths, such as action space, input feature representation, neural network architecture, and reward function. We propose a computationally feasible egocentric map representation based on frontiers, and a novel reward term based on total variation to promote complete coverage. Through extensive experiments, we show that our approach surpasses the performance of both previous RL-based approaches and highly specialized methods across multiple CPP variations.