Robotic Autonomous Trolley Collection with Progressive Perception and Nonlinear Model Predictive Control

TL;DR

This paper introduces a novel autonomous trolley collection robot that combines advanced perception with nonlinear model predictive control to operate efficiently in complex environments, improving robustness and obstacle avoidance.

Contribution

It presents a new integrated system with a 3D trolley detection method and a nonlinear model predictive control framework for robust trolley collection.

Findings

Successfully collects trolleys in dynamic environments

Demonstrates improved obstacle avoidance capabilities

Validated robustness through real-world experiments

Abstract

Autonomous mobile manipulation robots that can collect trolleys are widely used to liberate human resources and fight epidemics. Most prior robotic trolley collection solutions only detect trolleys with 2D poses or are merely based on specific marks and lack the formal design of planning algorithms. In this paper, we present a novel mobile manipulation system with applications in luggage trolley collection. The proposed system integrates a compact hardware design and a progressive perception and planning framework, enabling the system to efficiently and robustly collect trolleys in dynamic and complex environments. For the perception, we first develop a 3D trolley detection method that combines object detection and keypoint estimation. Then, a docking process in a short distance is achieved with an accurate point cloud plane detection method and a novel manipulator design. On the planning side, we formulate the robot's motion planning under a nonlinear model predictive control framework with control barrier functions to improve obstacle avoidance capabilities while maintaining the target in the sensors' field of view at close distances. We demonstrate our design and framework by deploying the system on actual trolley collection tasks, and their effectiveness and robustness are experimentally validated.