Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking

TL;DR

This paper introduces a PSO-based strategy for heterogeneous maritime swarms, enhancing target search and tracking by using adaptive dynamics and simulating varied agent capabilities to improve performance.

Contribution

It proposes a novel PSO-based approach with adaptive parameters for heterogeneous swarms, addressing challenges of integrating upgraded agents without degrading mission performance.

Findings

Increased collective response with heterogeneous agents

Enhanced target tracking performance

Adaptive parameters improve swarm robustness

Abstract

Current strategies employed for maritime target search and tracking are primarily based on the use of agents following a predetermined path to perform a systematic sweep of a search area. Recently, dynamic Particle Swarm Optimization (PSO) algorithms have been used together with swarming multi-robot systems (MRS), giving search and tracking solutions the added properties of robustness, scalability, and flexibility. Swarming MRS also give the end-user the opportunity to incrementally upgrade the robotic system, inevitably leading to the use of heterogeneous swarming MRS. However, such systems have not been well studied and incorporating upgraded agents into a swarm may result in degraded mission performances. In this paper, we propose a PSO-based strategy using a topological k-nearest neighbor graph with tunable exploration and exploitation dynamics with an adaptive repulsion parameter. This strategy is implemented within a simulated swarm of 50 agents with varying proportions of fast agents tracking a target represented by a fictitious binary function. Through these simulations, we are able to demonstrate an increase in the swarm's collective response level and target tracking performance by substituting in a proportion of fast buoys.