Adaptive pyramidal clustering for shortest path determination

Keith Olson; Scott A. Speigle

doi:10.1117/12.241070

27 May 1996 Adaptive pyramidal clustering for shortest path determination

Keith Olson, Scott A. Speigle

Proceedings Volume 2738, Navigation and Control Technologies for Unmanned Systems; (1996) https://doi.org/10.1117/12.241070
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

This paper will present a unique concept implemented in a software design that determines near optimal paths between hundreds of randomly connected nodes of interest in a faster time than current near optimal path determining algorithms. The adaptive pyramidal clustering (APC) approach to determining near optimal paths between numerous nodes uses an adaptive neural network along with classical heuristic search techniques. This combination is represented by a nearest neighbor clustering up function (performed by the neural network) and a trickle down pruning function (performed by the heuristic search). The function of the adaptive neural network is a significant reason why the APC algorithm is superior to several well known approaches. The APC algorithm has already been applied to autonomous route planning for unmanned ground vehicles. The intersections represent navigational waypoints that can be selected as source and destination locations. The APC algorithm then determines a near optimal path to navigate between the selected waypoints.

Citation Download Citation

Keith Olson and Scott A. Speigle "Adaptive pyramidal clustering for shortest path determination", Proc. SPIE 2738, Navigation and Control Technologies for Unmanned Systems, (27 May 1996); https://doi.org/10.1117/12.241070

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