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Use Of Time Varying Dynamics In Neural Network To Solve Multi-Target Classification, S. N. Balakrishnan, J. Rainwater
Use Of Time Varying Dynamics In Neural Network To Solve Multi-Target Classification, S. N. Balakrishnan, J. Rainwater
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Several types of solutions exist for multiple target tracking. These techniques are computation-intensive and in some cases very difficult to operate online. The authors report on a backpropagation neural network which has been successfully used to identify multiple moving targets using kinematic data (time, range, range-rate and azimuth angle) from sensors to train the network. Preliminary results from simulated scenarios show that neural networks are capable of learning target identification for three targets during the time period used during training and a time period shortly after. This effective classification period can be extended by the use of networks in coordination …
Moving Object Recognition And Guidance Of Robots Using Neural Networks, Abhijit Neogy, S. N. Balakrishnan, Cihan H. Dagli
Moving Object Recognition And Guidance Of Robots Using Neural Networks, Abhijit Neogy, S. N. Balakrishnan, Cihan H. Dagli
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The design of a robust guidance system for a robot is discussed. The two major tasks for this guidance system are the online recognition of a moving object invariant to rotation and translation, and tracking the moving object using a neural-network-driven vision system. This system included computer software ported to the IBM PC and interfaced with an IBM 7535 robot. The operation of this guidance system involved recognition of a moving object and the ability to track it till the robot and effector was in close proximity of the object. It was found that the robot was able to track …