Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning

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 …

applications coverage cpp cs.lg cs.ro cs.sy eess.sy environment environments free mapping path planning reinforcement reinforcement learning robotic search space the environment