Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs

TL;DR

This paper introduces a motion-encoded particle swarm optimization algorithm tailored for UAVs to efficiently locate moving targets, outperforming existing methods in detection accuracy and search speed through simulation and real-world experiments.

Contribution

The novel MPSO algorithm encodes UAV trajectories within the PSO framework, enhancing search coherence and performance in dynamic target detection tasks.

Findings

Improves detection performance by 24% over standard PSO

Achieves 4.71 times faster search times

Outperforms other metaheuristics like ABC, ACO, GA, DE, TSA

Abstract

This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm including the cognitive and social coherence, and thus resulting in better solutions. Results from extensive simulations with existing methods show that the proposed MPSO improves the detection performance by 24\% and time performance by 4.71 times compared to the original PSO, and moreover, also outperforms other state-of-the-art metaheuristic optimization algorithms including the artificial bee colony (ABC), ant colony optimization (ACO), genetic algorithm (GA), differential evolution (DE), and tree-seed algorithm (TSA) in most search scenarios. Experiments have been conducted with real UAVs in searching for a dynamic target in different scenarios to demonstrate MPSO merits in a practical application.