Curb-Tracker: An Integrated Curb Following System for Autonomous Vehicles

Curb following is a critical technology for autonomous road sweeping vehicles. However, existing solutions face two primary challenges: unreliable curb detection and inefficient motion generation. Unreliable curb detection stems from the wide variability in curb dimensions and types, as well as interference from roadside features such as vegetation and infrastructure. Inefficient motion generation occurs when existing methods prioritize tracking accuracy while neglecting task completion efficiency, leading to prolonged operation times. To address these challenges, we propose Curb-Tracker, an integrated curb-following system designed for autonomous vehicles operating in diverse road environments. Firstly, we develop a robust and adaptive curb detection algorithm that leverages a 2.5D elevation map of the local environment and dynamically adjusts key parameters online to ensure reliable detection across varying scenarios. Secondly, to achieve accurate and efficient curb-aligned motion generation, we leverage Model Predictive Contouring Control (MPCC) as a tailored framework specifically designed for the curb-following task to generate an optimal control sequence for the vehicle to maintain a specified lateral offset from the curb while maximizing travel progress along it. The proposed system has been implemented on a Hunter 2.0, a front-wheel Ackerman-steering mobile robot, and has been validated through extensive experiments in both Gazebo simulation and real-world environments. Experimental results demonstrate the effectiveness, adaptability, and robustness of the proposed system across a wide range of road scenarios. Video of real-world experiments is available at https://youtu.be/H1xaV6QdJ10.