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

2025-06-23

AI Summary

Addressing the challenges of motion constraints, time-varying payload, and significant external disturbances in mobile robot cart-pushing tasks, this paper proposes a flexible and robust whole-body coordination framework. First, a variable-posture kinematic model is formulated in a local coordinate frame to explicitly represent and optimize cart orientation. Second, an adaptive disturbance-rejection controller—requiring no precise dynamical prior—is designed by integrating nonlinear optimization with online disturbance estimation. This work presents the first systematic quantitative evaluation of flexibility and robustness in cart-pushing operations. Simulation and real-world experiments demonstrate that, compared to state-of-the-art methods, the proposed approach reduces positioning error by 42% and improves orientation tracking accuracy by 35% under complex dynamic disturbances. Moreover, it exhibits strong generalization capability against payload variations and uneven terrain.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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/.

Problem

• Enhancing robot maneuverability in cart-pushing tasks
• Addressing motion constraints and redundancy in planning
• Robust control against disturbances and variable payloads

Innovation

• Local coordinate representation for maneuverable planning
• Disturbance rejection control without dynamic model
• Nonlinear optimization for flexible push poses