An Active Search Strategy with Multiple Unmanned Aerial Systems for Multiple Targets

TL;DR

A novel method in which global and local information is adeptly merged to avoid issues such as myopia and redundant back-and-forth movements is introduced, ensuring a reduction in repetitive flight patterns and making the control of multiple agents in a decentralized way.

Abstract

The challenge of efficient target searching in vast natural environments has driven the need for advanced multi-UAV active search strategies. This paper introduces a novel method in which global and local information is adeptly merged to avoid issues such as myopia and redundant back-and-forth movements. In addition, a trajectory generation method is used to ensure the search pattern within continuous space. To further optimize multi-agent cooperation, the Voronoi partition technique is employed, ensuring a reduction in repetitive flight patterns and making the control of multiple agents in a decentralized way. Through a series of experiments, the evaluation and comparison results demonstrate the efficiency of our approach in various environments. The primary application of this innovative approach is demonstrated in the search for horseshoe crabs within their wild habitats, showcasing its potential to revolutionize ecological survey and conservation efforts.

Figures (6)

• Figure 1: Illustration of the search problem. The red stars represent the target objects that are randomly located at the map.
• Figure 2: Illustration of sampling rays. Voronoi partition is used to bind the rays.
• Figure 3: Illustration of simulation environments. The map with different colors represents the change in height.
• Figure 4: Comparison results of different algorithms when finding different numbers of targets.
• Figure 5: Illustration of the update of region division and the path of each UAV.