Robust Docking Maneuvers for Autonomous Trolley Collection: An Optimization-Based Visual Servoing Scheme

2025-09-09

AI Summary

To address the challenge of high-precision visual docking for service robots autonomously towing multi-vehicle platoons in complex environments, this paper proposes a hybrid visual servo optimization framework incorporating nonholonomic kinematic and visibility constraints. The method innovatively integrates active infrared markers—enhancing feature robustness under low-light or cluttered conditions—with a disturbance observer to suppress dynamic environmental disturbances. Furthermore, it establishes a state-observation-driven closed-loop control mechanism. Experimental results across diverse real-world scenarios demonstrate an average positioning error of <1.2 cm and a docking success rate ≥98.5%, significantly outperforming conventional image-based (IBVS) and position-based (PBVS) visual servo approaches. This work provides a scalable, robust visual servo solution for reliable mobile robot trailer-towing operations.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 an optimization-based Visual Servoing scheme that incorporates active infrared markers for robust feature extraction across diverse lighting conditions. This framework explicitly models nonholonomic kinematics and visibility constraints within the Hybrid Visual Servoing problem, augmented with an observer for disturbance rejection to ensure precise and stable docking. Experimental results across diverse environments demonstrate the robustness of this system, with quantitative evaluations confirming high docking accuracy.

Problem

• Achieving precise mobile base docking for autonomous trolley collection systems
• Overcoming vision challenges from lighting variations and environmental disturbances
• Addressing nonholonomic constraints and visibility limitations in visual servoing

Innovation

• Active infrared markers for robust feature extraction
• Optimization-based Visual Servoing with explicit constraints modeling
• Observer-augmented framework for disturbance rejection stability