Cooperation Of Multiple Unmanned Aerial Vehicles Research Articles

An unmanned air combat system based on swarm intelligence

Unmanned aerial vehicles (UAVs) are usually controlled by radio or by autonomous control algorithms. Compared with manned aerial vehicles, they have great advantages in performing dangerous tasks but, presently, no UAV system can cope with high-intensity air combat. In addition, the robustness of a single UAV cannot be guaranteed in air combat missions; on the other hand, a multi-UAV system not only ensures this robustness but also improves the mission success rate by using saturation attacks. Therefore, this paper presents a multi-UAV air combat system based on swarm intelligence. Considering the problem of multi-UAV cooperative arrival at the air battlefield and the accomplishment of combat tasks, the aerodynamic model of the aircrafts and the threat area on the path to the battlefield are modeled; the path planning is completed through an ant colony algorithm. Based on the control algorithm of single-UAV finite-state machine and the cooperation of multiple UAVs, an autonomous control algorithm for multi-UAV systems is proposed to improve the success rate of UAV clusters in air combat. The effectiveness of the proposed algorithm is tested with a simulation platform.

Multiple Unmanned Aerial Vehicles Cooperative Combat Task Allocation Method Based on Receding Horizon Control

Multiple Unmanned Aerial Vehicles Cooperative Combat Task Allocation Method Based on Receding Horizon Control

Cooperative and Geometric Learning Algorithm (CGLA) for path planning of UAVs with limited information

In this paper, we propose a new learning algorithm, named as the Cooperative and Geometric Learning Algorithm (CGLA), to solve problems of maneuverability, collision avoidance and information sharing in path planning for Unmanned Aerial Vehicles (UAVs). The contributions of CGLA are three folds: (1) CGLA is designed for path planning based on cooperation of multiple UAVs. Technically, CGLA exploits a new defined individual cost matrix, which leads to an efficient path planning algorithm for multiple UAVs. (2) The convergence of the proposed algorithm for calculating the cost matrix is proven theoretically, and the optimal path in terms of path length and risk measure from a starting point to a target point can be calculated in polynomial time. (3) In CGLA, the proposed individual weight matrix can be efficiently calculated and adaptively updated based on the geometric distance and risk information shared among UAVs. Finally, risk evaluation is introduced first time in this paper for UAV navigation and extensive computer simulation results validate the effectiveness and feasibility of CGLA for safe navigation of multiple UAVs.