Overapproximative Human Arm Occupancy Prediction for Collision Avoidance

Abstract

Abstract-Predicting the occupancy of a human in real time is of great interest in human-robot coexistence for obtaining regions that a robot should avoid in safe motion planning. The human body is composed of joints and links, suiting approximation by a kinematic chain, but the control strategy of the human is completely unknown, meaning the potential occupancy grows very fast and it is difficult to compute tightly in real time. As such, most previous work considers only specific, known, or probable movements, and usually does not account for a range of human dimensions. Focusing on the human arm, we analyze archetypal movements performed by test subjects to create a dynamic model. Motion-capture data of subjects are fitted, for modeling purposes, to two abstractions: a 4-degree of freedom (DOF) model and a 3-DOF model, to obtain dynamic parameters. We validate our approach on movements from a publicly available database. The prediction is shown to be computationally fast, and reachable sets of the abstraction are shown to enclose all possible future occupancies of the arm for different subjects, tightly but overapproximatively. The 3-DOF model has advantages over the 4-DOF in terms of speed, though the 4-DOF model is tighter at smaller time horizons. Such an overapproximative representation is intended for certifiable safety-guaranteed collision avoidance algorithms for robots.Note to Practitioners-Motivated by the need to keep humans safe when working alongside robots, our earlier work proposes a method of trajectory planning where the robot certifies each movement as safe before it performs it. For this to prove that unsafe collisions cannot occur, an overapproximative prediction of the human is needed, meaning that no possible future position of the human is outside the predicted region, or reachable occupancy. However, making this prediction both small enough (so that it does not include unreachable regions) and fast enough for real-time use is not straightforward. We find the limits of human motion by asking a range of test subjects to perform movements as fast as possible. We calculate the reachable occupancies based on these limits and show that our predictions are indeed overapproximative, fast, and not wasteful of volume. One can then use the aforementioned approach to guarantee safety; future challenges are reliably sensing the human's pose and implementing our approach on an industrial robot.

Reachset Conformance Testing of Human Arms with a Biomechanical Model

“…One can therefore abstract the dynamics to simpler, linear dynamics, for which reachable sets can be found online. Such a method for the human arm is shown in [10], [11].…”

“…A prediction of the future occupancy of the human arm can be generated from a low-order kinematic model of the human arm, and maximum joint accelerations, velocities and positions obtained from motion capture data as presented in [11]. There is a risk that movements that are not in the motion capture data set may not be accounted for in the occupancy space obtained, in which case the prediction does not account for all movement of the arm, i.e.…”

“…The low-order, set-based prediction [11] is performed as follows. Offline, we obtain motion-capture data of 38 test subjects (see Fig.…”

“…1. Method as in [11]: offline, the dynamics are obtained from analysis of human movement and abstraction to a simple kinematic model. Online, the human pose from sensors is fit to the same model, and reachability analysis used to determine the possible reachable occupancies.…”

“…6). From these markers, we identify the shoulder x S , elbow x E and wrist x W , and a base coordinate system at the shoulder as in [11]. 6.…”