Servo Collision Detection Control System Based on Robot Dynamics

Abstract

Collision detection and inspection of industrial robots have become essential functions in modern industrial automation. Sensor-based detection methods are commonly employed in research to achieve collision detection, including high-precision force sensors, ultrasonic ranging sensors, electronic skins, and others. While collision detection using force sensors or electronic skin sensors offers very high accuracy, the inclusion of these sensors increases the overall cost. This article proposes a solution using dynamic modeling for collision detection. First, the theoretical torque generated by each axis of the industrial robot under different pose conditions is analyzed in real time. Then, the actual torque is calculated by sampling the motor current of each axis. By setting error margins and collision detection thresholds, collision detection can be achieved in a cost-effective manner without the need for additional sensors. Experiments were conducted to evaluate this dynamic modeling approach to collision detection. The findings indicated that the approach is efficacious and capable of identifying the impacts of diverse collision objects. However, compared to sensor-based detection methods, collision detection using dynamic modeling has the disadvantage of lower accuracy. Future research will concentrate on enhancing the calculation accuracy of the theoretical torque to enhance the sensitivity of collision detection.