A formulation for collision identification and distance calculation in motion planning using neural networks

Abstract

The collision identification and object-to-object distance calculation play an important role in the motion planning for robots and manufacturing facilities. A formulation for collision identification and distance calculation in motion planning, using neural networks, is presented. The method calculates the distances between the vertices of an object and the given polyhedral obstacles using the modified Hamming net. This formulation is derived from the homogeneous geometric transformations. The method can be used to identify collision between the vertices of a moving object and the obstacles, to calculate the distance and interference between the moving object and the obstracle, and to find the optimal direction for collision removal. The parallel computation formulation is simple in form, and can be extended to line-to-object and object-to-object collision identification and distance calculation. The method can considerably decrease required computation time, and has the potential for being applied to on-line trajectory planning.