Risk-Aware Complete Coverage Path Planning Using Reinforcement Learning

Abstract

Complete coverage path planning (CCPP) is a trending research area in floor cleaning robotics. CCPP is often approached as an optimization problem, typically solved by considering factors, such as power consumption and time as key objectives. In recent years, the safety of cleaning robots has become a major concern, which can critically limit the performance and lifetime of the robots. However, so far, optimizing safety has rarely been addressed in CCPP. Most of the path-planning algorithms in literature tend to identify and avoid the hazards detected by the robot’s perception. However, these systems can limit the area coverage of the robot or pose a risk of failing when the robot is near a hazard. Therefore, this article proposes a novel CCPP method with the awareness of risk levels for a robot to minimize possible hazards to the robot during a coverage task. The proposed CCPP strategy uses reinforcement learning (RL) to obtain a safety-ensured path plan that evaluates and when necessary, avoid the hazardous components in their environment in real time. Furthermore, the failure mode and effect analysis (FMEA) method has been adopted to classify the hazards identified in the environment of the robot and suitably modified to evaluate the risk levels. These risk levels are used in the reward architecture of the RL. Thus, the robot can cross the low-risk hazardous environments if it is necessary to obtain complete coverage. Experimental results showed a noticeable reduction in overall risk faced by a robot compared to the existing methods, while also effectively achieving complete coverage.