Motion control and positioning system of multi-sensor tunnel defect inspection robot: from methodology to application

Area of Science:

• Robotics and Automation
• Navigation Systems
• Control Systems Engineering

Background:

• Increasing subway mileage necessitates automatic inspection equipment for efficiency and frequency.
• Subway tunnels present complex environments lacking GPS, with interference and wheel-rail dynamics hindering conventional positioning.
• Existing speed tracking and positioning methods perform poorly due to these environmental challenges.

Purpose of the Study:

• To develop a multi-sensor motion control system for subway tunnel inspection robots.
• To enhance speed tracking and positioning accuracy in challenging underground environments.
• To overcome limitations of conventional methods in non-GPS environments.

Main Methods:

• Proposed a multi-sensor motion control system incorporating trapezoidal speed planning and Model Predictive Control (MPC) for speed tracking.
• Developed an "Inertial Navigation System (INS) + Odometer" positioning method, reducing algorithmic dimensions.
• Utilized a closed-loop Kalman filter for real-time error correction and combined positioning model establishment.

Main Results:

• The MPC-based speed tracking algorithm achieved 0.89% overshoot and 0.32% stability error, outperforming PID control.
• The multi-sensor fusion positioning algorithm demonstrated a maximum error of 0.15% and an average error of 0.08% at 40 km/h over 2 km.
• Significantly improved accuracy and stability in speed following and positioning compared to single-sensor methods.

Conclusions:

• The proposed multi-sensor motion control system effectively addresses the challenges of subway tunnel inspection.
• The integrated INS + Odometer positioning with Kalman filtering provides highly accurate and stable robot navigation.
• The system significantly improves upon conventional methods, enabling reliable autonomous operation in complex underground environments.