Polar Coordinate-based Differential Evolution for Moving Target Search Using Vision Sensor on Unmanned Aerial Vehicles

2026-07-20

AI Summary

This study addresses the low detection probability and insufficient search efficiency of unmanned aerial vehicles (UAVs) in locating mobile targets during the critical “golden hour” of search-and-rescue operations. To this end, the authors propose a Bayesian search framework that integrates dynamic target modeling with visual perception. The target’s motion is modeled as a Markov process, and a detection likelihood function is constructed based on image quality. Bayesian inference is then employed to generate a posterior distribution over the target’s location, which serves as the fitness function for path planning. Furthermore, the authors introduce a novel Polar-coordinate Differential Evolution (PDE) algorithm that explicitly incorporates UAV kinematic constraints to enhance exploration of the solution space. Experimental results demonstrate that PDE consistently outperforms existing methods across diverse scenarios, achieving superior detection probability and reduced execution time, with validation on a real-world UAV platform confirming its practical efficacy.

Abstract

In search and rescue operations, there is a period known as the "golden time" during which the probability of finding the target alive is highest. The objective of this work is to propose a new search algorithm for unmanned aerial vehicles (UAVs) with a focus on improving the detection probability and execution time. We approach this problem by first modeling target dynamics as a Markov process and the detection likelihood as a function of image quality and the observer's vision. We then employ Bayesian theory to derive a fitness function representing the probability distribution of the target's location over the search area. Finally, we introduce a new algorithm named polar coordinate-based differential evolution (PDE) to generate a UAV search path that maximizes this fitness function. The PDE algorithm utilizes polar coordinates to incorporate kinematic constraints and maneuver properties of the UAV, allowing for better exploration of the solution space. A series of simulations and comparative analyses have been conducted to evaluate the performance of the proposed algorithm. Experiments involving a real UAV have also been conducted. Results demonstrate that the PDE algorithm outperforms state-of-the-art algorithms in terms of detection probability and execution time across diverse search scenarios while remaining practical for real-world applications. The source code of the algorithm is available at https://github.com/thuhangkhuat/PDE_target_search.

Problem

• moving target search
• unmanned aerial vehicles
• detection probability
• search and rescue
• vision sensor

Innovation

• Polar Coordinate-based Differential Evolution
• UAV search path planning
• Bayesian target localization
• Markov target dynamics
• vision-based detection