Control of Planar Rigid Body Sliding with Impacts and Friction

Author:

Partridge Chad B.,Spong Mark W.1

Affiliation:

1. Coordinated Science Laboratory, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, USA

Abstract

In this paper, we continue our investigations into robotic air hockey by studying the problem of controlling the trajectory of a puck subject to impacts. Impacts are modeled using the Routh two-dimensional impact model, which incorporates the effects of spin and friction. Using this model, we derive an explicit mapping between preimpact and postimpact velocities of the puck as a function of the velocities of the impacting objects and the coefficients of restitution and friction. We will see that this mapping depends fundamentally on whether or not relative sliding between the objects occurs throughout the duration of the impact event, or terminates during the impact. We then present results on planning of puck trajectories and control of the puck through impacts with a mallet or striker, which is essentially the problem of inverting the mapping between preimpact and postimpact velocities. Our results confirm the intuitive notion that a large coefficient of friction between the mallet and puck, and between the puck and table walls, enhances the ability to control puck motion through impacts.

Publisher

SAGE Publications

Subject

Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modeling and Simulation,Software

Cited by 24 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficient and Reactive Planning for High Speed Robot Air Hockey;2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2021-09-27

2. Mixing and segregation of freely-falling granular materials through a vertical pipe;Granular Matter;2021-02-20

3. Adaptive Terminal Sliding Mode Control for Attitude and Position Tracking Control of Quadrotor UAVs in the Existence of External Disturbance;IEEE Access;2021

4. Batting an in-flight object to the target;The International Journal of Robotics Research;2019-01-01

5. Learning Control for Air Hockey Striking Using Deep Reinforcement Learning;2017 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO);2017-05

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