Collaborative Planar Pushing of Polytopic Objects with Multiple Robots in Complex Scenes

TL;DR

This paper presents a hybrid optimization framework enabling multiple mobile robots to collaboratively push polytopic objects in complex, obstacle-rich environments, addressing contact mode switching and under-actuation challenges.

Contribution

It introduces a novel hybrid optimization approach with multi-directional feasibility estimation, hierarchical mode selection, and adaptive nonlinear control for multi-robot pushing tasks.

Findings

Validated in high-fidelity simulations and hardware experiments

Demonstrated robustness to motion and actuation uncertainties

Achieved efficient and effective collaborative pushing in complex scenes

Abstract

Pushing is a simple yet effective skill for robots to interact with and further change the environment. Related work has been mostly focused on utilizing it as a non-prehensile manipulation primitive for a robotic manipulator. However, it can also be beneficial for low-cost mobile robots that are not equipped with a manipulator. This work tackles the general problem of controlling a team of mobile robots to push collaboratively polytopic objects within complex obstacle-cluttered environments. It incorporates several characteristic challenges for contact-rich tasks such as the hybrid switching among different contact modes and under-actuation due to constrained contact forces. The proposed method is based on hybrid optimization over a sequence of possible modes and the associated pushing forces, where (i) a set of sufficient modes is generated with a multi-directional feasibility estimation, based on quasi-static analyses for general objects and any number of robots; (ii) a hierarchical hybrid search algorithm is designed to iteratively decompose the navigation path via arc segments and select the optimal parameterized mode; and (iii) a nonlinear model predictive controller is proposed to track the desired pushing velocities adaptively online for each robot. The proposed framework is complete under mild assumptions. Its efficiency and effectiveness are validated in high-fidelity simulations and hardware experiments. Robustness to motion and actuation uncertainties is also demonstrated.