Trajectory prediction for moving objects using artificial neural networks

Author

Payeur, Pierre ; Le-Huy, Hoang ; Gosselin, Clément M.

Author_Institution

Dept. of Electr. Eng., Laval Univ., Que., Canada

Volume

42

Issue

2

fYear

1995

fDate

4/1/1995 12:00:00 AM

Firstpage

147

Lastpage

158

Abstract

A method to predict the trajectory of moving objects in a robotic environment in real-time is proposed and evaluated. The position, velocity, and acceleration of the object are estimated by several neural networks using the six most recent measurements of the object coordinates as inputs. The architecture of the neural nets and the training algorithm are presented and discussed. Simulation results obtained for both 2D and 3D cases are presented to illustrate the performance of the prediction algorithm. Real-time implementation of the neural networks is considered. Finally, the potential of the proposed trajectory prediction method in various applications is discussed

Keywords

learning (artificial intelligence); motion estimation; neural nets; real-time systems; robots; 2D; 3D; acceleration; applications; architecture; artificial neural networks; moving objects; object coordinates; performance; position; prediction algorithm; real-time; robotic environment; simulation; training algorithm; trajectory prediction; velocity; Acceleration; Accelerometers; Artificial neural networks; Coordinate measuring machines; Neural networks; Position measurement; Predictive models; Robot kinematics; Trajectory; Velocity measurement;

fLanguage

English

Journal_Title

Industrial Electronics, IEEE Transactions on

Publisher

ieee

ISSN

0278-0046

Type

jour

DOI

10.1109/41.370380

Filename

370380