Multi-Agent Search for a Moving and Camouflaging Target

TL;DR

This paper develops advanced optimization methods for multi-agent search strategies targeting a moving, camouflaging target, incorporating heterogeneous searcher capabilities and Markovian target movement, with extensive computational validation.

Contribution

It introduces novel reformulation and solution techniques for a complex mixed-integer nonlinear program in multi-agent search planning, including linearization, preprocessing, and cutting plane methods.

Findings

Methods are computationally efficient for large-scale problems.

Different approaches are recommended based on problem instance characteristics.

Markov chain modeling improves search strategy effectiveness.

Abstract

In multi-agent search planning for a randomly moving and camouflaging target, we examine heterogeneous searchers that differ in terms of their endurance level, travel speed, and detection ability. This leads to a convex mixed-integer nonlinear program, which we reformulate using three linearization techniques. We develop preprocessing steps, outer approximations via lazy constraints, and bundle-based cutting plane methods to address large-scale instances. Further specializations emerge when the target moves according to a Markov chain. We carry out an extensive numerical study to show the computational efficiency of our methods and to derive insights regarding which approach should be favored for which type of problem instance.