Collision identification between convex polyhedra using neural networks

Abstract

Collision identification between convex polyhedra is a major research focus in computer-aided manufacturing and path planning for robots. This paper presents a collision-identification neural network (CINN) to identify possible collisions between two convex polyhedra. It consists of a modified Hamming net and a constraint subnet. The modified Hamming net is designed for point-to-polyhedron collision identification, and the constraint subnet is designed to move a point within a polyhedron and detect possible collisions with another polyhedron. A CINN has a simple canonical structure. It is very easy to program and can be implemented by a modest number of nonlinear amplifiers and three analog integrators. The working principle of the CINN is very similar to the well-known Hopfield net model. Its simple collective computing power accomplishes the relatively complicated task of collision identification between convex polyhedra, rendering a suitable device for online path planning of robots. An example is presented to demonstrate the application of CINN's to collision-free motion planning.