Human-Robot Co-Transportation with Human Uncertainty-Aware MPC and Pose Optimization

2024-03-31

arXiv.org

AI Summary

To address disturbances arising from human motion uncertainty and robotic actuation noise in human-robot collaborative load transportation, this paper proposes a model predictive control (MPC) framework integrating full-body kinematic modeling with online manipulator pose optimization. The key contributions are: (i) the first real-time solution framework for an uncertainty-aware discrete algebraic Riccati equation (DARE), enabling explicit incorporation of pose selection into the MPC receding-horizon optimization for joint configuration and control co-design; and (ii) a pose candidate set search strategy coupled with enhanced uncertainty modeling. Evaluations in simulation and on a physical Fetch robot platform demonstrate that the method significantly reduces control effort while maintaining strong robustness under multi-trajectory tracking and stochastic disturbances, outperforming existing baseline algorithms.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper proposes a new control algorithm for human-robot co-transportation based on a robot manipulator equipped with a mobile base and a robotic arm. The primary focus is to adapt to human uncertainties through the robot's whole-body kinematics and pose optimization. We introduce an augmented Model Predictive Control (MPC) formulation that explicitly models human uncertainties and contains extra variables than regular MPC to optimize the pose of the robotic arm. The core of our methodology involves a two-step iterative design: At each planning horizon, we select the best pose of the robotic arm (joint angle combination) from a candidate set, aiming to achieve the lowest estimated control cost. This selection is based on solving an uncertainty-aware Discrete Algebraic Ricatti Equation (DARE), which also informs the optimal control inputs for both the mobile base and the robotic arm. To validate the effectiveness of the proposed approach, we provide theoretical derivation for the uncertainty-aware DARE and perform simulated and hardware experiments using a Fetch robot under varying conditions, including different trajectories and noise levels. The results reveal that our proposed approach outperforms baseline algorithms.

Problem

• Develop disturbance-aware MPC for human-robot co-transportation
• Optimize robotic arm pose to minimize control cost
• Validate approach with simulations and hardware experiments

Innovation

• Disturbance-aware MPC with pose optimization
• Two-step iterative pose and control design
• DARE-based whole body control optimization