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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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This content will become publicly available on June 1, 2026
DARC: Disturbance-Aware Redundant Control for Human–Robot Co-Transportation
This paper introduces Disturbance-Aware Redundant Control (DARC), a control framework addressing the challenge of human–robot co-transportation under disturbances. Our method integrates a disturbance-aware Model Predictive Control (MPC) framework with a proactive pose optimization mechanism. The robotic system, comprising a mobile base and a manipulator arm, compensates for uncertain human behaviors and internal actuation noise through a two-step iterative process. At each planning horizon, a candidate set of feasible joint configurations is generated using a Conditional Variational Autoencoder (CVAE). From this set, one configuration is selected by minimizing an estimated control cost computed via a disturbance-aware Discrete Algebraic Riccati Equation (DARE), which also provides the optimal control inputs for both the mobile base and the manipulator arm. We derive the disturbance-aware DARE and validate DARC with simulated experiments with a Fetch robot. Evaluations across various trajectories and disturbance levels demonstrate that our proposed DARC framework outperforms baseline algorithms that lack disturbance modeling, pose optimization, or both.
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- Award ID(s):
- 2332210
- PAR ID:
- 10640034
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Electronics
- Volume:
- 14
- Issue:
- 12
- ISSN:
- 2079-9292
- Page Range / eLocation ID:
- 2480
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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