Heterogeneous Unmanned Aerial Vehicles Cooperative Search Approach for Complex Environments

TL;DR

This paper presents a cooperative heterogeneous UAV search method for complex environments, utilizing distributed control, genetic algorithms, and adaptive decision-making to enhance target discovery and robustness.

Contribution

It introduces a novel integrated approach combining jump grid decision, dynamic parameter selection, and low-bandwidth communication for heterogeneous UAV cooperation.

Findings

Significantly improved search efficiency over homogeneous UAVs.

Enhanced robustness in complex, dynamic environments.

Effective adaptive decision-making under constraints.

Abstract

This paper studies a heterogeneous Unmanned Aerial Vehicles (UAVs) cooperative search approach suitable for complex environments. In the application, a fixed-wing UAV drops rotor UAVs to deploy the cluster rapidly. Meanwhile, the fixed-wing UAV works as a communication relay node to improve the search performance of the cluster further. The distributed model predictive control and genetic algorithms are adopted to make online intelligent decisions on UAVs search directions. On this basis, a jump grid decision method is proposed to satisfy the maneuverability constraints of UAVs, a parameter dynamic selection method is developed to make search decisions more responsive to task requirements, and a search information transmission method with low bandwidth is designed. This approach can enable UAVs to discover targets quickly, cope with various constraints and unexpected situations, and make adaptive decisions, significantly improving the robustness of search tasks in complex, dynamic, and unknown environments. The proposed approach is tested with several search scenarios, and simulation results show that the cooperative search performance of heterogeneous UAVs is significantly improved compared to homogeneous UAVs.