Motion prediction in a high-speed, dynamic environment

Yu, Shengquan; Yong-hai, Wu

doi:10.1109/ictai.2005.87

Cited by 3 publications

(2 citation statements)

References 3 publications

(3 reference statements)

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“…ANN base motion planer can respond to changing real time situation [11]. Latency effect in a closed loop system can be reduced for motion prediction [12]. To design the vehicle controller behavioral cloning machine learning algorithm and neural network algorithms can also be used [13].…”

Section: Present Levelmentioning

confidence: 99%

Motion Planning of An Autonomous Mobile Robut Using Artificial Neural Network

Tripath¹

2012

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Section: Present Levelmentioning

confidence: 99%

Motion Planning of An Autonomous Mobile Robut Using Artificial Neural Network

Tripath¹

2012

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…Their result showed that the performance of the neural predictor had small errors than the linear prediction. Sheng and Wu [7] proposed a systematic approach based on neural networks to predict the ball position in the competition of RoboCup Small Size League. The inputs of the neural network were the vision distances of the ball between the current frame and the other six past frames.…”

mentioning

confidence: 99%

Latency removal for teleoperating a paddle-juggling robot

Baca

Lin

2016

2016 IEEE International Conference on Industrial Technology (ICIT)

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In order to develop a real-time teleoperated robotic system it is necessary to cope with the latency that is contributed by all the components of the system. Usually, the transmission elapsed time is one of the most serious issues in the latency problem. In the proposed teleoperated robotic system, there are a motion-capture system to acquire human motion data, a paddle-juggling robot to bounce a ping-pong ball up and down, and a proposed latency-removal algorithm to remove the motion latency between the human and the robot. It is assumed that the delay induced by the latency-removal algorithm is negligible. To reduce the lump-sum latency effect of the teleoperated system, the proposed latency-removal algorithm is based on a emulator/controller neural-network architecture to predict the robot motion command in advance. In the task of bouncing a ping-pong ball up and down, the movement is near cyclic. The simulation result shows that the performance by the proposed method is even better for a near cyclic movement.

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Motion Compensation of Omnidirectional Wheel Robot Using Neural Networks

Yong-hai¹,

Youxiang²

2006

Sixth International Conference on Intelligent Systems Design and Applications

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Motion prediction in a high-speed, dynamic environment

Cited by 3 publications

References 3 publications

Motion Planning of An Autonomous Mobile Robut Using Artificial Neural Network

Motion Planning of An Autonomous Mobile Robut Using Artificial Neural Network

Latency removal for teleoperating a paddle-juggling robot

Motion Compensation of Omnidirectional Wheel Robot Using Neural Networks

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