Autonomous Air-Hockey Playing Cobot Using Optimal Control and Vision-Based Bayesian Tracking

  • Ahmad AlAttarEmail author
  • Louis Rouillard
  • Petar Kormushev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11650)


This paper presents a novel autonomous air-hockey playing collaborative robot (cobot) that provides human-like gameplay against human opponents. Vision-based Bayesian tracking of the puck and striker are used in an Analytic Hierarchy Process (AHP)-based probabilistic tactical layer for high-speed perception. The tactical layer provides commands for an active control layer that controls the Cartesian position and yaw angle of a custom end effector. The active layer uses optimal control of the cobot’s posture inside the task nullspace. The kinematic redundancy is resolved using a weighted Moore-Penrose pseudo-inversion technique. Experiments with human players show high-speed human-like gameplay with potential applications in the growing field of entertainment robotics.


Air hockey Cobot Bayesian tracking Analytic Hierarchy Process Autonomous robot Entertainment robotics 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Robot Intelligence LabImperial College LondonLondonUK

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