A novel trajectory prediction approach for table-tennis robot based on nonlinear output feedback observer

Wang, Yingshi; Sun, Lei; Liu, Jingtai; Yang, Qifan; Zhou, Lü; He, Shan

doi:10.1109/robio.2010.5723488

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…The ball trajectory collected by high-speed stereo vision is analyzed to establish the air flight model of the ball and the collision model between the ball and the racket. 23 After the design process of the robot mechanism, a realistic 3D humanoid robot model is built in the simulation system for the prediction of the ball trajectory and the motion planning of the robot arm. [24][25][26] The virtual table tennis experiment is carried out to verify the trajectory prediction algorithm of the ball, the trajectory planning algorithm of the humanoid robot arm and the leg, and so on.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a three-dimensional simulation system for a humanoid table tennis robot

Zhou

2020

Measurement and Control

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This paper designs and implements a simulation system that can verify the planning algorithms such as table tennis trajectory planning and robot leg movement planning. The simulation system and the humanoid robot’s control system are connected based on the RTnet real-time Ethernet protocol. The table tennis game of the humanoid robot is implemented using the simulation of the forward and inverse kinematics of the robot arm, the hitting planning, and the forward and inverse kinematics of the robot leg.

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Section: Introductionmentioning

confidence: 99%

Design and implementation of a three-dimensional simulation system for a humanoid table tennis robot

Zhou

2020

Measurement and Control

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An Adaptive Trajectory Prediction Method for Ping-Pong Robots

Zhang

Xiong

Zhao

et al. 2012

Intelligent Robotics and Applications

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Learning-based model predictive control on a quadrotor: Onboard implementation and experimental results

Bouffard

Aswani

Tomlin

2012

2012 IEEE International Conference on Robotics and Automation

163

113

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In this paper, we present details of the real time implementation onboard a quadrotor helicopter of learningbased model predictive control (LBMPC). LBMPC rigorously combines statistical learning with control engineering, while providing levels of guarantees about safety, robustness, and convergence. Experimental results show that LBMPC can learn physically based updates to an initial model, and how as a result LBMPC improves transient response performance. We demonstrate robustness to mis-learning. Finally, we show the use of LBMPC in an integrated robotic task demonstrationThe quadrotor is used to catch a ball thrown with an a priori unknown trajectory.

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A novel trajectory prediction approach for table-tennis robot based on nonlinear output feedback observer

Cited by 4 publications

References 6 publications

Design and implementation of a three-dimensional simulation system for a humanoid table tennis robot

Design and implementation of a three-dimensional simulation system for a humanoid table tennis robot

An Adaptive Trajectory Prediction Method for Ping-Pong Robots

Learning-based model predictive control on a quadrotor: Onboard implementation and experimental results

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