Joint Position Bounds in Resolved-Acceleration Control: A Comparison

Abstract

The implementation of human-friendly robots is based on the deployment of robots that can safely and effectively work with humans in various environments. To this end, enforcing joint limits in planning and control play a fundamental role in avoiding the robot to exceed its physical constraint and preventing joint damages or failures that could lead to unpredictable behavior or compromised safety. However, the implementation of such limitations in instantaneous controllers is not trivial when position, velocity, and acceleration limits are all considered together. In this work, we compare three State-of-the-Art methods, namely the P-Step Ahead Predictor, the Control Barrier Function, and Invariance. Finally, we select the most performing one applied in a real use case based on a UR5e manipulator for a picking task where hitting joint limits may represent an issue.