Neural computation for planning AND/OR precedence-constraint robot assembly sequences

The problem of finding AND/OR precedence-constraint assembly sequences for a set of
n
parts that construct a mechanical object using neural computation is discussed. The geometric constraints of the assembled object are transformed into the elements of the connection matrix which specifies the connection strength among neurons. A modified Hopfield network is used to tackle the AND/OR precedence-constraint assembly-sequence problem. The designed algorithm can accommodate various constraints and applications. Detailed algorithms and analysis, and examples and experiments are presented