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This paper explores the integration of the Robot Operating System (ROS) with human-robot interaction models for autonomous vehicle navigation in social robotics. Utilizing ROS as middleware, OptiTrack for positioning and localization, and MATLAB for control and ROS node deployment, this study investigates leader-follower formation dynamics in mixed environments. The leader vehicle is controlled using an RVIZ pointer, serving as a medium for human interaction, while both the leader and follower vehicles maintain safe distances using trajectory tracking methods. These methods are implemented and deployed to the robot cars as ROS nodes. The experiment involves a physical leader car and a virtual follower car, both tracked using OptiTrack. Numerical simulations and real-world experiments demonstrate ROS’s role in enhancing coordination and interaction, providing insights into effective human-robot interaction and collision avoidance.

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Leader-Follower Formation of a Car-Like Robot Using ROS and Trajectory Tracking

  • 会议论文

  • 发表日期：

    2025年

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  • Faris Shahab,
  • Anas Dhahri,
  • Adam Hamadi,
  • Mohammed Noorizadeh,
  • John-John Cabibihan,
  • Nader Meskin

摘要

This paper explores the integration of the Robot Operating System (ROS) with human-robot interaction models for autonomous vehicle navigation in social robotics. Utilizing ROS as middleware, OptiTrack for positioning and localization, and MATLAB for control and ROS node deployment, this study investigates leader-follower formation dynamics in mixed environments. The leader vehicle is controlled using an RVIZ pointer, serving as a medium for human interaction, while both the leader and follower vehicles maintain safe distances using trajectory tracking methods. These methods are implemented and deployed to the robot cars as ROS nodes. The experiment involves a physical leader car and a virtual follower car, both tracked using OptiTrack. Numerical simulations and real-world experiments demonstrate ROS’s role in enhancing coordination and interaction, providing insights into effective human-robot interaction and collision avoidance.