A scheme integrating neural networks for real-time robotic collision detection

Abstract

We present a scheme incorporating neural network mappings for geometric modeling and interference determination in robotic collision detection. The scheme promises to greatly reduce the computational time associated with calculating collision points, which makes real-time obstacle avoidance more achievable. The scheme includes three modules: a geometric modeling module, a collision detection module, and a decision support module. The geometric modeling module employs the restricted Coulomb energy (RCE) paradigm to describe the spatial occupancy of a 3-D object by a number of overlapping spheres. The collision detection module receives the geometric pattern in the robot's environment and updates the spherical representation to perform geometric computation for existence of interference. The decision support module, using neural networks, provides online information for the collision detection module. A PUMA 560 robot's CAD model was built to test the scheme. The performances using the scheme and using the CAD model were compared and presented.