Switched Momentum Dynamics Identification for Robot Collision Detection

In modern industry, human–robot collaboration is becoming the norm. Since the robots need to share the same workspace with humans in an unstructured/semistructured environment, robot–human and robot–environment collisions are inevitable in general. To reduce the harm caused by these collisions, it is necessary to detect them in real time so that actions can be taken accordingly. In this article, we propose a general robot collision detection method based on switched momentum dynamics identification. This enables real-time collision detection without any additional sensors, which are usually required by most of the existing real-time collision detection methods. Our algorithm identifies the specific parts in robot momentum dynamics that are affected by collisions and reports a collision occurrence whenever the identified parts deviate from a known collision-free model. The identification results are further analyzed using a support vector machine classifier to locate the linkage involved in the collisions. Finally, the effectiveness of our method is verified through experiments in the PyBullet environment and on a real 6-DOF robot, showing improved robustness to noise for identical collision detection accuracies.