A neuro-fuzzy approach for robot system safety

Abstract

Robot safety is a critical and largely unsolved problem involving the interaction of man and machine. The paper presents a new approach to robot safety which uses an integrated sensing architecture for monitoring the robot workspace, and a new detection and decision logic for regulating the safe operation of the robot. Sensory information is fused through a trained neural network to produce a map of the hazards. Using this combined map, and information about the robot's current position and velocity, a set of fuzzy logic rules has been implemented to regulate robot activity. Simulation results presented in the paper indicate that this method is both effective in detection of potentially hazardous situations and computationally feasible.