Swarming methods for geospatial reasoning

Abstract

Geospatial data are often used to predict or recommend movements of robots, people, or animals (‘walkers’). Analysis of such systems can be combinatorially explosive. Each decision that a walker makes generates a new set of possible future decisions, and the tree of possible futures grows exponentially. Complete enumeration of alternatives is out of the question. One approach that we have found promising is to instantiate a large population of simple computer agents that explore possible paths through the landscape. The aggregate behaviour of this swarm of agents estimates the likely behaviour of the real‐world system. This paper will discuss techniques that we have found useful in swarming geospatial reasoning, illustrate their behaviour in specific cases, compare them with existing techniques for path planning, and discuss the application of such systems.