Integrating Maneuverable Planning and Adaptive Control for Robot Cart-Pushing under Disturbances

TL;DR

This paper introduces a novel planning and control framework for robot cart-pushing that enhances maneuverability and robustness against disturbances, validated through extensive simulations and real-world experiments.

Contribution

It presents a new local coordinate-based planning method and a disturbance rejection control approach for flexible, robust cart-pushing in complex scenarios.

Findings

The method improves push maneuverability and flexibility.

The disturbance rejection control reduces control errors under disturbances.

Experiments demonstrate superiority over existing approaches.

Abstract

Precise and flexible cart-pushing is a challenging task for mobile robots. The motion constraints during cart-pushing and the robot's redundancy lead to complex motion planning problems, while variable payloads and disturbances present complicated dynamics. In this work, we propose a novel planning and control framework for flexible whole-body coordination and robust adaptive control. Our motion planning method employs a local coordinate representation and a novel kinematic model to solve a nonlinear optimization problem, thereby enhancing motion maneuverability by generating feasible and flexible push poses. Furthermore, we present a disturbance rejection control method to resist disturbances and reduce control errors for the complex control problem without requiring an accurate dynamic model. We validate our method through extensive experiments in simulation and real-world settings, demonstrating its superiority over existing approaches. To the best of our knowledge, this is the first work to systematically evaluate the flexibility and robustness of cart-pushing methods in experiments. The video supplement is available at https://sites.google.com/view/mpac-pushing/.