Title :
A neural network-based robot safety system
Author :
Zurada, Jozef ; Graham, James H.
Author_Institution :
Dept. of Comput. Inf. Syst., Louisville Univ., KY, USA
Abstract :
This paper presents a new approach for real-time robot safety system based on artificial neural networks. This approach includes a neural network detection unit and a neural network decision unit, implemented at an intermediate and high level of sensory processing, respectively. Both the detection and decision units have been implemented and tested by simulation, both separately and as an integrated unit. The response time of the integrated system measured on the 90 MHz, P5 microprocessor is less than 11 ms, and the correctness of safety decisions is 97%
Keywords :
decision theory; industrial robots; neural nets; robots; safety systems; uncertainty handling; neural network decision unit; neural network detection unit; neural network-based robot safety system; safety decisions; sensory processing; Artificial neural networks; Computational complexity; Neural networks; Real time systems; Robot control; Robot sensing systems; Safety; Sensor systems; Service robots; Uncertainty;
Conference_Titel :
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-2559-1
DOI :
10.1109/ICSMC.1995.538227
