An Efficient Representation of Whole-body Model Predictive Control for Online Compliant Dual-arm Mobile Manipulation

TL;DR

This paper introduces an efficient bilevel model predictive control framework using Bézier-curve parameterization for real-time whole-body motion planning of dual-arm mobile manipulators, ensuring safety, compliance, and obstacle avoidance.

Contribution

It presents a novel trajectory representation method within a bilevel MPC framework that enables fast, online, and constraint-satisfying whole-body motion planning for highly redundant mobile manipulators.

Findings

Demonstrates real-time obstacle avoidance in simulation and real-world tests.

Shows improved robustness and efficiency over existing MPC methods.

Enables compliant interaction control under external disturbances.

Abstract

Dual-arm mobile manipulators can transport and manipulate large-size objects with simple end-effectors. To interact with dynamic environments with strict safety and compliance requirements, achieving whole-body motion planning online while meeting various hard constraints for such highly redundant mobile manipulators poses a significant challenge. We tackle this challenge by presenting an efficient representation of whole-body motion trajectories within our bilevel model-based predictive control (MPC) framework. We utilize B\'ezier-curve parameterization to represent the optimized collision-free trajectories of two collaborating end-effectors in the first MPC, facilitating fast long-horizon object-oriented motion planning in SE(3) while considering approximated feasibility constraints. This approach is further applied to parameterize whole-body trajectories in the second MPC for whole-body motion generation with predictive admittance control in a relatively short horizon while satisfying whole-body hard constraints. This representation enables two MPCs with continuous properties, thereby avoiding inaccurate model-state transition and dense decision-variable settings in existing MPCs using the discretization method. It strengthens the online execution of the bilevel MPC framework in high-dimensional space and facilitates the generation of consistent commands for our hybrid position/velocity-controlled robot. The simulation comparisons and real-world experiments demonstrate the efficiency and robustness of this approach in various scenarios for static and dynamic obstacle avoidance, and compliant interaction control with the manipulated object and external disturbances.