The project explores the development of neural networks for performing complicated computations in (a) collision avoidance under known, static working environments, and (b) manipulator dynamics, which is a mapping from joint positions, velocities and accelerations to joint torques/forces. For collision avoidance, the detection of collisions is treated as a pattern recognition problem, which can be solved efficiently and easily by neural networks. For computing manipulator dynamics, a three-layer neural network is being studied for mapping between continuous- valued input and output. Collision avoidance and manipulator dynamics are very computationally intensive operations. For real-time control usage, these computations must be accomplished in an acceptable time period, which is usually tens of milliseconds or less. Neural networks use massive parallelism, which allows these computations to be possible in real time. The proposed research represents a feasible approach towards these problems.
