Multi-Agent Discrete Search with Limited Visibility

TL;DR

This paper introduces efficient algorithms for multi-agent discrete search problems with limited visibility, including a specialized solution for location-dependent detection and an approximation for the NP-hard joint detection case.

Contribution

It reformulates the multi-agent search problem as a network optimization and develops a faster algorithm, also providing an approximation method for the more complex NP-hard scenario.

Findings

The specialized algorithm is faster than general min-cost flow algorithms.

The approximation algorithm guarantees performance for NP-hard cases.

Simulations demonstrate the effectiveness of the proposed algorithms.

Abstract

The problem of search by multiple agents to find and localize objects arises in many important applications. In this paper, we study a class of multi-agent search problems in which each agent can access only a subset of a discrete search space, with detection performance that depends only on the location. We show that this problem can be reformulated as a minimum cost network optimization problem, and develop a fast specialized algorithm for the solution. We prove that our algorithm is correct, and has worst case computation performance that is faster than general minimum cost flow algorithms. We also address the problem where detection performance depends on both location and agent, which is known to be NP-Hard. We reduce the problem to a submodular maximization problem over a matroid, and provide an approximate algorithm with guaranteed performance. We illustrate the performance of our algorithms with simulations of search problems and compare it with other min-cost flow algorithms.