Full-Scale Autonomous Highway Inspection with Quadruped Robot: Multi-Level Locomotion Learning in Complex Environments

Abstract

This paper proposes an innovative approach of full-scale autonomous highway inspection in complex environments using quadruped robot to enhance the adaptability and coverage of inspection tasks. Considering adaptive locomotion control as the foundation of autonomous inspection, a multi-level locomotion learning framework based on reinforcement learning is developed, including primitive-level, skill-level and inspection-level. Primitive-level control policy built upon Vector Quantized Variational Autoencoder is trained through imitation learning from existing open-source robots locomotion models, thereby achieving discrete embedding and reusability of foundational locomotion knowledge. At skill-level, to support diverse inspection skills learning, parametric modular scenario modeling method of the highway environment is proposed. Each skill-level control network is trained in corresponding modular scenario while reusing primitive-level control network. Inspection-level control network is established through multi-skill distillation from trained skill control networks. Combined with coverage path generator, automatic inspection can be completed. In a simulated complex highway environment, inspection robot demonstrates diverse inspection skills, successfully completing inspection of 14,400m2 area in 0.4h, with speed of 2.37m/s. Coverage and hazard detection rates both reach 100%. Compared to the existing highway inspection forms, the proposed highway inspection framework with quadruped robot enables efficient, stable, and full-scale autonomous inspection in complex highway environments, which provides general deployment capability for intelligent inspection systems.