Exoskeleton robots are capable of safe torque- controlled interactions with a wearer while moving their limbs through pre-defined trajectories. However, affecting and assist- ing the wearer’s movements while incorporating their inputs (effort and movements) effectively during an interaction re- mains an open problem due to the complex and variable nature of human motion. In this paper, we present a control algorithm that leverages task-specific movement behaviors to control robot torques during unstructured interactions by implementing a force field that imposes a desired joint angle coordination behavior. This control law, built by using principal component analysis (PCA), is implemented and tested with the Harmony exoskeleton. We show that the proposed control law is versatile enough to allow for the imposition of different coordination behaviors with varying levels of impedance stiffness. We also test the feasibility of our method for unstructured human-robot interaction. Specifically, we demonstrate that participants in a human-subject experiment are able to effectively perform reaching tasks while the exoskeleton imposes the desired joint coordination under different movement speeds and interaction modes. Survey results further suggest that the proposed control law may offer a reduction in cognitive or motor effort. This control law opens up the possibility of using the exoskeleton for training the participating in accomplishing complex m
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This content will become publicly available on May 12, 2025
Human Uncertainty-Aware MPC for Enhanced Human-Robot Collaborative Manipulation
This paper presents the development of a novel control algorithm designed for tasks involving human-robot collaboration. By using an 8-DOF robotic arm, our approach aims to counteract human-induced uncertainties added to the robot's nominal trajectory. To address this challenge, we incorporate a variable within the regular Model Predictive Control (MPC) framework to account for human uncertainties, which are modeled as following a normal distribution with a non-zero mean and variance. Our solution involves formulating and solving an uncertainty-aware Discrete Algebraic Ricatti Equation (ua-DARE), which yields the optimal control law for all joints to mitigate the impact of these uncertainties. We validate our methodology through theoretical analysis, demonstrating the effectiveness of the ua-DARE in providing an optimal control strategy. Our approach is further validated through simulation experiments using a Fetch robot model, where the results highlight a significant improvement in performance over a baseline algorithm that does not consider human uncertainty while solving for optimal control law.
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- Award ID(s):
- 2332210
- PAR ID:
- 10546377
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-6301-2
- Page Range / eLocation ID:
- 1 to 6
- Subject(s) / Keyword(s):
- Model Predictive Control, Human-Robot Interaction, Human Uncertainty
- Format(s):
- Medium: X
- Location:
- St. Louis, MO, USA
- Sponsoring Org:
- National Science Foundation
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