Abstract
This work presents a real-time system consisting of a standard industrial arm, two cameras and a controlling PC that is able to catch and juggle a ping-pong ball thrown by a human. We briefly discuss the experimental setup and flow of information, but we focus on overcoming the two main problems: predicting the impact velocity and coping with the slow reaction time of the robot. We explain why this task is impossible to accomplish using the linear model for the flight path of the ball that is predominant in the robot juggling literature and we present a novel fluid-dynamical model. Experiments are presented comparing both models and showing that a better model bundled with a little more computation can drastically increase the systems intelligence and performance. This drastically reduces the demands on the robot’s agility and speed.
Based on “A Ping-Pong Ball Catching and Juggling Robot: a Real-Time Framework for Vision Guided Acting of an Industrial Robot Arm”, by Holger H. Rapp which appeared in the Proceedings of the 5th International Conference on Automation, Robotics and Applications (ICARA 2011). © 2011 IEEE.
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References
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Rapp, H.H. (2013). Improving System Performance Via Reevaluation of Models. In: Sen Gupta, G., Bailey, D., Demidenko, S., Carnegie, D. (eds) Recent Advances in Robotics and Automation. Studies in Computational Intelligence, vol 480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37387-9_15
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DOI: https://doi.org/10.1007/978-3-642-37387-9_15
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