Learning-based approach to enable mobile robots to charge batteries using standard wall outlets

Autonomous battery charging is crucial for mobile service robots in human-center indoor environments, enabling them to extend operational hours and coverage without human assistance. This paper presents an innovative approach for mobile service robots to charge their batteries using standard wall outlets, introducing no additional maintenance cost and requiring no modification to environments. A portable self-charging device, equipped with cameras, a force sensor, and a 2-degree-of-freedom end-effector carrying a standard 3-pin 120V power plug, is attached to an existing mobile robot. The robot identifies a wall outlet and navigates to it using an onboard depth camera. It inserts the plug into the wall outlet while the vision is obstructed. The plug-insertion operation is guided by a control policy that was trained by a simulation model using a deep reinforcement learning technique. This approach achieved a success rate of nearly \(90\%\) $90\%$ in experiments of inserting a power plug into a wall outlet. It eliminates the need of an installed docking station for autonomous charging or human plugging-in for manual charging.