Auto-Follower: A Person-Following System for Urban Ackermann Human–Machine Collaborative Robotics

Industry 5.0 is emerging as the next phase of industrial evolution, emphasizing human-centric manufacturing through close human–robot collaboration and the deployment of intelligent autonomous systems. As a representative example of such autonomy, person-following robots are typically implemented on differential-drive or omnidirectional mobile bases. However, certain tasks require Ackermann-steered robots, which face unique challenges due to limited maneuverability and the complexity of urban environments, often leading to target loss or navigation into non-drivable areas. To address these issues, we propose Auto-Follower, a person-following framework with enhanced perception and navigation capabilities. Auto-Follower integrates a vision–LiDAR servo tracker that fuses camera images with LiDAR points from a motorized rotating sensor, enabling 360° target perception. Instead of relying on a global map, the system employs real-time LiDAR-based local mapping for efficient path planning. In addition, an Iterative Radius Points Search (IRPS) method is developed to identify obstacle-free navigation goals when the target enters non-drivable regions, ensuring safe and continuous following. The framework has been validated extensively in both laboratory and urban environments and demonstrates robust, reliable performance, with strong potential for adaptation to diverse real-world person-following applications.