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

Zhe Zhang, Peijia Xie, Yuhan Pang, Zhirui Sun, Bingyi Xia, Bi-Ke Zhu, Jiankun Wang

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/.

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

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/.

Paper Structure

This paper contains 26 sections, 2 theorems, 24 equations, 11 figures.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Notations
  4. Description of the Task
  5. Task Requirements
  6. Complex Constraints Satisfaction
  7. Whole-Body Coordination
  8. Control under Complicated Dynamics
  9. Hardware Platform
  10. Whole-Body Motion Planning
  11. Local Coordinate Representation
  12. Stable Planning—Truck and Trailer MPC
  13. Flexible Planning—Leader-Follower MPC
  14. Robust Adaptive Controller
  15. Disturbance Rejection Control with GOB
  16. ...and 11 more sections

Key Result

Lemma 1

Consider a point shifted from the center point $\boldsymbol{p}_r=\boldsymbol{p}+r\boldsymbol{h}$, where $r$ is the distance and $\boldsymbol{h}=[\cos\theta,\sin\theta]^{\top}$, we can derive that: In this expression, the offset point removes the nonholonomic constraints and becomes a single integrator capable of lateral sliding motion. Furthermore, the mapping $[\dot{x}_r, \dot{y}_r]^{\top}\mapst

Figures (11)

  • Figure 1: Illustration of maneuverable planning and adaptive control framework for cart-pushing task. The robot coordinates whole-body motions to maneuver in tighter and more complex environments, while adaptive control enables it to handle various disturbances during cart-pushing.
  • Figure 2: (a) Control inputs of arms and base. (b) Two distinct push poses. (c) Illustration of whole body coordination requirement. The planner should flexibly utilize Deflection Poses to enhance maneuverability and ultimately return to a stable Centered Pose during transportation.
  • Figure 3: Hardware platform overview.
  • Figure 4: Local coordinate representation of arm states. (a) The original system. (b) The virtual 2-link robotic arm model.
  • Figure 5: Illustration of the reduced-order models. (a) Global view of the TT model. TT model cannot achieve poses where the truck shifts laterally relative to the trailer. (b) Global view of the LF model. LF model can fully leverage its maneuverability to execute complex motions while fulfilling the task requirements.
  • ...and 6 more figures

Theorems & Definitions (2)

  • Lemma 1
  • Lemma 2