Title :
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
fDate :
4/1/1995 12:00:00 AM
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;
Journal_Title :
Industrial Electronics, IEEE Transactions on