DOI: 10.1002/rob.70320 ISSN: 1556-4959

Efficient Whole‐Body Model Predictive Control for Online Compliant Dual‐Arm Mobile Manipulation

Wenqian Du, Ran Long, João Moura, Jiayi Wang, Saeid Samadi, Sethu Vijayakumar

ABSTRACT

Dual‐arm mobile manipulators can transport and manipulate large objects with simple end‐effectors. Interacting with dynamic environments subject to strict safety and compliance requirements, achieving whole‐body motion planning online while meeting various hard constraints for highly redundant mobile manipulators poses a significant challenge. We tackle this challenge by presenting an efficient whole‐body motion‐planning approach based on model‐based predictive control (MPC). We construct a hierarchical MPC framework in which the first MPC optimizes the end‐effectors' collision‐free motion, thereby guiding the second MPC to optimize whole‐body joint trajectories. In the first stage, we employ a Bézier‐curve representation to parameterize the high‐degree‐of‐freedom (high‐DOF) motion in of two collaborating end‐effectors, notably introducing a novel and efficient method to optimize quaternion trajectories. This facilitates fast long‐horizon motion planning of coupled translational and rotational trajectories while accounting for approximated feasibility constraints. In the second stage, this is the first work to novelly incorporate the representation into a whole‐body MPC for online high‐DOF motion generation with predictive admittance control over a relatively short horizon while satisfying whole‐body hard constraints. Compared with the usage of a discretization approach, our whole‐body MPC ensures accurate model‐state transitions with faster, more stable computation speeds, and consistent motion command generation which enhances tracking performance for our hybrid position/velocity‐controlled robot. Both MPCs perform replanning in each control loop to adapt to a changing environment. Simulations and real‐world experiments validate the proposed hierarchical MPC framework, demonstrating that its two novel MPCs achieve efficient and robust performance in scenarios involving static and dynamic obstacle avoidance, compliant interaction with manipulated objects, and external disturbances.

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