Evaluation of Robot Suspension Systems, in a Human-Robot Interaction Point of View

Abstract

I developed an active suspension system for mobile robots to enable them to communicate intent through fluid, biological body language, bridging the gap between functional robotics and social interaction. By modeling the robot using a quarter-car state-space approach and implementing it in ROS 2, I successfully created a system that mimics natural movement. I overcame technical challenges like kinematic ghosting by integrating IMU sensor data, which allowed me to optimize the damping ratio and achieve a critically damped, smooth response. To test this, I evaluated the system's stability under unpredictable conditions using a dynamic human tracking script in a Gazebo simulation. Ultimately, my research demonstrates that active suspension serves as a powerful non-verbal communication framework, providing robots with a natural presence that can significantly enhance user trust.