On-line feasible wrench polytope evaluation based on human musculoskeletal models: an iterative convex hull method

TL;DR

This paper introduces a real-time iterative convex hull algorithm for evaluating the feasible wrench polytope of human musculoskeletal models, enabling adaptive human-robot collaboration based on real-time human capacity estimation.

Contribution

The paper presents a novel iterative convex hull method for real-time feasible wrench polytope analysis, improving computational efficiency over traditional exponential-time algorithms.

Findings

Algorithm runs in near-linear time relative to muscle count.

Real-time application demonstrated in collaborative carrying task.

Maintains load at 30% of human capacity during robot assistance.

Abstract

Many recent human-robot collaboration strategies, such as Assist-As-Needed (AAN), are promoting humancentered robot control, where the robot continuously adapts its assistance level based on the real-time need of its human counterpart. One of the fundamental assumptions of these approaches is the ability to measure or estimate the physical capacity of humans in real-time. In this work, we propose an algorithm for the feasibility set analysis of a generic class of linear algebra problems. This novel iterative convex-hull method is applied to the determination of the feasible Cartesian wrench polytope associated to a musculoskeletal model of the human upper limb. The method is capable of running in real-time and allows the user to define the desired estimation accuracy. The algorithm performance analysis shows that the execution time has near-linear relationship to the considered number of muscles, as opposed to the exponential relationship of the conventional methods. Finally, real-time robot control application of the algorithm is demonstrated in a Collaborative carrying experiment, where a human operator and a Franka Emika Panda robot jointly carry a 7kg object. The robot is controlled in accordance to the AAN paradigm maintaining the load carried by the human operator at 30% of its carrying capacity.