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DA-VPC: Disturbance-Aware Visual Predictive Control Scheme of Docking Maneuvers for Autonomous Trolley Collection

Yuhan Pang, Bingyi Xia, Zhe Zhang, Zhirui Sun, Peijia Xie, Bike Zhu, Wenjun Xu, Jiankun Wang

TL;DR

A Disturbance-Aware Visual Predictive Control (DA-VPC) scheme that incorporates active infrared markers for robust feature extraction across diverse lighting conditions and is augmented with an extended state observer (ESO) designed to counteract disturbances during trolley pushing, ensuring precise and stable docking.

Abstract

Service robots have demonstrated significant potential for autonomous trolley collection and redistribution in public spaces like airports or warehouses to improve efficiency and reduce cost. Usually, a fully autonomous system for the collection and transportation of multiple trolleys is based on a Leader-Follower formation of mobile manipulators, where reliable docking maneuvers of the mobile base are essential to align trolleys into organized queues. However, developing a vision-based robotic docking system faces significant challenges: high precision requirements, environmental disturbances, and inherent robot constraints. To address these challenges, we propose a Disturbance-Aware Visual Predictive Control (DA-VPC) scheme that incorporates active infrared markers for robust feature extraction across diverse lighting conditions. This framework explicitly models nonholonomic kinematics and visibility constraints for image-based visual servoing (IBVS), solving the predictive control problem through optimization. It is augmented with an extended state observer (ESO) designed to counteract disturbances during trolley pushing, ensuring precise and stable docking. Experimental results across diverse environments demonstrate the robustness of this system, with quantitative evaluations confirming high docking accuracy.

DA-VPC: Disturbance-Aware Visual Predictive Control Scheme of Docking Maneuvers for Autonomous Trolley Collection

TL;DR

A Disturbance-Aware Visual Predictive Control (DA-VPC) scheme that incorporates active infrared markers for robust feature extraction across diverse lighting conditions and is augmented with an extended state observer (ESO) designed to counteract disturbances during trolley pushing, ensuring precise and stable docking.

Abstract

Service robots have demonstrated significant potential for autonomous trolley collection and redistribution in public spaces like airports or warehouses to improve efficiency and reduce cost. Usually, a fully autonomous system for the collection and transportation of multiple trolleys is based on a Leader-Follower formation of mobile manipulators, where reliable docking maneuvers of the mobile base are essential to align trolleys into organized queues. However, developing a vision-based robotic docking system faces significant challenges: high precision requirements, environmental disturbances, and inherent robot constraints. To address these challenges, we propose a Disturbance-Aware Visual Predictive Control (DA-VPC) scheme that incorporates active infrared markers for robust feature extraction across diverse lighting conditions. This framework explicitly models nonholonomic kinematics and visibility constraints for image-based visual servoing (IBVS), solving the predictive control problem through optimization. It is augmented with an extended state observer (ESO) designed to counteract disturbances during trolley pushing, ensuring precise and stable docking. Experimental results across diverse environments demonstrate the robustness of this system, with quantitative evaluations confirming high docking accuracy.

Paper Structure

This paper contains 18 sections, 31 equations, 9 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Overview
  4. Problem Formulation
  5. Notations
  6. Kinematic Model for Visual Servoing Docking
  7. Disturbance-Aware Visual Predictive Docking Control
  8. Homography Based State Measurement
  9. Disturbance Observer Design
  10. Disturbance-Aware Visual Predictive Control
  11. Experiments
  12. Experimental Setup
  13. Simulation Experiments
  14. Real-World Experiments
  15. Disturbance Rejection Experiment
  16. ...and 3 more sections

Figures (9)

  • Figure 1: The Follower robot docks the collected trolleys into an organized queue then transport them to designated location. Utilizing visual servoing, it achieves precise docking by observing the infrared features on the Leader robot through its onboard camera.
  • Figure 2: The pipeline for the trolley collection docking process, forming a disturbance-aware visual predictive control framework.
  • Figure 3: A prototype of the trolley collection system: (a) Leader robot, (b) Follower robot pushing a trolley.
  • Figure 4: Coordinate system description for robot docking process.
  • Figure 5: Hardware platform of experiments, with key dimensions illustrated.
  • ...and 4 more figures