Industrial Inspection Robot With Large Configuration Space Based on Geometry-Changing Tracked Robot
Abstract
With the increasing demand for automated inspection solutions in complex industrial environments, existing robotic platforms face significant limitations in terms of endurance, payload capacity and obstacle-crossing capabilities. In this paper, we present a novel inspection robot system based on the CubeTrack tracked platform, featuring a large configuration space achieved through the integration of a manipulator and advanced mobility mechanisms. Our system incorporates a quad-slider elliptical trammel mechanism (Qs-ETM) that enables geometry-changing tracks for enhanced terrain adaptability while maintaining track tension stability. To address multi-layer navigation challenges, we propose an efficient trajectory planning algorithm that extracts traversable planes from three-dimensional (3D) point clouds and constructs a lightweight plane graph for path optimisation. Additionally, we develop a flipper control algorithm that uses only low-cost local sensor measurement (time-of-flight [TOF] sensors and inertial measurement unit [IMU]) to enable autonomous stair navigation without pre-mapped environments. The inspection system integrates multiple sensors, including light detection and ranging (LiDAR) sensor, RGB cameras, gas sensors and thermal cameras, providing comprehensive monitoring capabilities for industrial inspection demands. Extensive real-world experiments demonstrate the system’s effectiveness in navigating complex environments with stairs, multiple layers and narrow passages, validating both the mechanical design and algorithmic approaches for practical industrial inspection tasks.
Ge Wan
Ph.D. student
My research interests include distributed robotics, relative localization and reinforcement learning.
Yanjun Cao
Principal Investigator
My research interests focuse on key challenges in multi-robot systems, such as collaborative localization, perception, communication, and system organization.