A Framework for Adaptive Load Redistribution in Human-Exoskeleton-Cobot Systems
Emir Mobedi, Gokhan Solak, Arash Ajoudani
AI summary
Problem
Limited-DOF exoskeletons often fail to align external loads with supported joints, causing overloading of unsupported joints and reducing usability. Current human-robot collaboration methods lack integrated physical support that dynamically optimizes joint loading.
Approach
The framework uses online optimization to calculate an optimal arm configuration, then guides the human via a cobot and visual avatar feedback to align their pose, while an exoskeleton provides targeted joint support.
Key results
- Validated in industrial painting task with 4 subjects across multiple arm configurations and payloads
- Adaptively redistributed excessive joint torques to supported joints using optimized weight matrices
- Enabled real-time visual pose alignment between human and optimized avatar via cobot guidance
- Demonstrated reduced joint effort through EMG measurements under varying optimization weights
Why it matters
Improves ergonomics and safety for industrial workers by maximizing the effectiveness of wearable assistive devices in collaborative tasks.
Abstract
Wearable devices like exoskeletons are designed to reduce excessive loads on specific joints of the body. Specifically, single- or two-degrees-of-freedom (DOF) upper-body industrial exoskeletons typically focus on compensating for the strain on the elbow and shoulder joints. However, during daily activities, there is no assurance that external loads are correctly aligned with the supported joints. Optimizing work processes to ensure that external loads are primarily (to the extent that they can be compensated by the exoskeleton) directed onto the supported joints can significantly enhance the overall usability of these devices and the ergonomics of their users. Collaborative robots (cobots) can play a role in this optimization, complementing the collaborative aspects of human work. In this study, we propose an adaptive and coordinated control system for the human-cobot-exoskeleton interaction. This system adjusts the task coordinates to maximize the utilization of the supported joints. When the torque limits of the exoskeleton are exceeded, the framework continuously adapts the task frame, redistributing excessive loads to non-supported body joints to prevent overloading the supported ones. We validated our approach in an equivalent industrial painting task involving a single-DOF elbow exoskeleton, a cobot, and four subjects, each tested in four different initial arm configurations with five distinct optimisation weight matrices and two different payloads.