Novel Complete Coverage Path Planning of a Biped Wall-Climbing Robot for Window Cleaning

Cleaning exterior walls of high-altitude buildings is a critical task typically performed by human workers. However, manual maintenance exhibits several disadvantages. To address these issues, this paper presents a biped wall-cleaning robotic system (BWCRS) dedicated to the maintenance of multi-isolate areas on exoskeleton-structured windows. To tackle the complete coverage cleaning path planning (CCCPP) problem, an innovative approach is proposed by integrating a deep Qnetwork (DQN)-based method with the BWCRS. The proposed method enhances the cleaning process by optimizing the robot's travel path using DQN, ensuring stable navigation on walls with obstacles through advanced biped loco-manipulation. The effectiveness of the reported approach is validated by conducting extensive simulation and experimental investigations. Comparative studies reveal that the reported approach outperforms the existing method. The results demonstrate the BWCRS's capability to efficiently and safely clean exoskeleton-structured windows. Moreover, the proposed framework can be extended to path planning of other types of climbing robots.