Polar Coordinate-based Differential Evolution for Moving Target Search Using Vision Sensor on Unmanned Aerial Vehicles
2026-07-20 • Robotics
Robotics
AI summaryⓘ
The authors created a new method for drones to find lost people or objects faster and more reliably during search and rescue missions. They modeled how the target moves and how well the drone can see it, then used a math tool called Bayesian theory to guess where the target might be. Their innovation, called the PDE algorithm, plans the drone's path using polar coordinates to respect how drones move. Tests using simulations and a real drone showed their method works better than current ones for finding targets quickly. The authors also shared their code online for others to use.
unmanned aerial vehiclessearch and rescueMarkov processBayesian theorydetection probabilitypolar coordinatesdifferential evolutionpath planningtarget trackingalgorithm optimization
Authors
Thu Hang Khuat, Duy-Nam Bui, Thuy Ngan Duong, Manh Duong Phung
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.