Abstract
This article concerns the use of a neural system in a previously unreported, but highly significant, application in robotics safety. It presents a neural network based decision unit for a real-time robot safety system. The decision unit occupies a high level position in the sensory processing hierarchy, using information from signal processing and detection at lower levels. The developed neural network accepts the following input: a map containing potential collision zones encoded as belief values into grids, the magnitude of a robot's velocity vector, and the robot's steering angle. The neural network decision is based on computation of the scalar product of two vectors, the virtual repulsive force vector generated by the obstacle and the robot's velocity vector; it produces one of the following safety decisions: move as intended, slow down, or emergency stop. The effectiveness of this approach is verified by computer simulation. The response time of the neural network measured on the state of the art personal computer is 6 msec, and the correctness of the safety decisions exceeds 90%.
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