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High Speed Robotic Table Tennis Swinging Using Lightweight Hardware with Model Predictive Control

David P. Nguyen, Kendrick D. Cancio, Sangbae Kim

Year
2025
Citations
2

Abstract

We present a robotic table tennis platform that achieves a variety of hit styles and ball-spins with high precision, power, and consistency. This is enabled by a custom lightweight, high-torque, low rotor inertia, five degree-of-freedom arm capable of high acceleration. To generate swing trajectories, we formulate an optimal control problem (OCP) that constrains the state of the paddle at the time of the strike. The terminal position is given by a predicted ball trajectory, and the terminal orientation and velocity of the paddle are chosen to match various possible styles of hits: loops (topspin), drives (flat), and chops (backspin). Finally, we construct a fixed-horizon model predictive controller (MPC) around this OCP to allow the hardware to quickly react to changes in the predicted ball trajectory. We validate on hardware that the system is capable of hitting balls with an average exit velocity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$11 \mathrm{m} / \mathrm{s}$</tex> at an 88% success rate across the three swing types.

Keywords

Table (database)Computer scienceModel predictive controlLookup tableSimulationControl (management)Computer hardwareArtificial intelligenceOperating systemDatabase

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