A neural network approach for safety and collision avoidance in robotic systems

A major factor which has limited the application of robots in industrial and human service applications has been the lack of robust sensing and control algorithms for detection and prevention of collision conditions. This paper discusses an approach to the collision avoidance control of robots using a neural network methodology for the integration of sensory input data from the robotʹs environment. The paper presents a formulation of the collision avoidance problem using the occupancy grid formulation, and discusses the use of a combination of Dempster-Shafer inference and neural networks in fusing the sensory information and making robot movement decisions. Initial studies have shown this approach to be both robust and computationally tractable in providing enhanced safety capabilities.