High Fidelity Human Modeling via Integration of Skeleton Tracking for Predictive HRC Collision Detection

Abstract This paper develops a predictive collision detection algorithm for enhancing safety while respecting productivity in a Human Robot Collaborative (HRC) setting that operates on outputs from a Computer Vision (CV) environmental monitor. This prediction can trigger reactive and proactive robot action. The algorithm is designed to address two key challenges: 1) outputs from CV techniques are often highly noisy and incomplete due to occlusions and other factors, and 2) human tracking CV approaches typically provide a minimal set of points on the human. This noisy set of points must be augmented to define a high-fidelity model of the human’s predicted spatial and temporal occupancy. A filter is applied to decrease sensitivity of the algorithm to errors in the CV predictions. Kinematics of the human are leveraged to infer a full model of the human from a set of, at most, 18 points, and transform them into a point cloud occupying the swept volume of the human’s motion. This form can then quickly be compared with a compatible robot model for collision detection. Timed tests show that creation of human and robot models, and the subsequent collision check occurs in less than 30 ms on average, making this algorithm real-time capable.