The reliability of data link system of unmanned aerial vehicle (UAV, or unmanned system) is the key to determine the success of unmanned system mission execution, and is also a difficult point of reliability modeling and analysis of unmanned system.Aiming at the characteristics of multi-service integration, complex structure and dynamic reconfiguration of UAV data link system, this paper proposes a method to build a dynamic reliability simulation model of data link service based on generalized stochastic Petri net (GSPN).By analyzing the characteristics of the UAV data link system, this method uses the resources, places, transitions and other elements in the GSPN model to describe various resources, states, behaviors and collaborative relationships in the data link system, and uses specific random variables to represent the component states and their durations to simulate the dynamic operation process of the data link system.Three typical services modes of UAV data link system as uplink remote control, downlink telemetry and downlink mission load transmission are modeled and simulated by using the Pipe software tool, which could accurately describe the synchronization, concurrency, distribution, conflict, resource sharing or competition, and reliability of the data link with dynamic reconfiguration behavior under the multi-service background could be evaluated.Finally, this paper proves that the GSPN based method could obtain reliable reliability evaluation results for the data link through a case analysis.
In the context of multidestination material transportation tasks for unmanned aerial vehicles (UAVs) during wartime, task allocation and path planning play crucial roles. Although numerous research methods have been proposed, most algorithms treat these two aspects as relatively independent components, resulting in limited flexibility in decision making and real-time path planning on highly confrontational battlefields. To address this issue, this article presents a novel approach to multidestination path planning based on ant colony decision making and rolling control. First, an information sharing module is established to process real-time updated environmental data, providing a foundation for decision making and path planning. Second, a new ant colony decision method is developed by designing an objective function that minimizes consumption to determine the order of destination material transportation. In addition, an online and multiround destination decision strategy is formulated based on the updated environmental information from the information sharing module. This strategy prevents situations where total consumption for multiple destinations becomes excessive due to UAVs not considering real-time changes in material demand during transportation but making decisions only once per destination. Subsequently, the receding horizon control with extended solution method is employed to achieve multidestination path planning by integrating online path planning and decision strategies. Finally, experiments are conducted in a multidestination environment with obstacles to validate the effectiveness of our proposed method. The results demonstrate that compared to several other advanced optimization methods, our approach exhibits advantages such as shorter paths and lower overall material consumption.
Explosive ordnance disposal (EOD) robots can replace humans that work in hazardous environments to ensure worker safety. Thus, they have been widely developed and deployed. However, existing EOD robots have some limitations in environmental adaptation, such as a single function, slow action speed, and limited vision. To overcome these shortcomings and solve the uncertain problem of bomb disposal on the firing range, we have developed an intelligent bomb disposal system that integrates autonomous unmanned aerial vehicle (UAV) navigation, deep learning, and other technologies. For the hardware structure of the system, we design an actuator constructed by a winch device and a mechanical gripper to grasp the unexploded ordnance (UXO), which is equipped under the six-rotor UAV. The integrated dual-vision Pan-Tilt-Zoom (PTZ) pod is applied in the system to monitor and photograph the deployment site for dropping live munitions. For the software structure of the system, the ground station exploits the YOLOv5 algorithm to detect the grenade targets for real-time video and accurately locate the landing point of the grenade. The operator remotely controls the UAV to grasp, transfer, and destroy grenades. Experiments on explosives defusal are performed, and the results show that our system is feasible with high recognition accuracy and strong maneuverability. Compared with the traditional mode of explosives defusal, the system can provide decision-makers with accurate information on the location of the grenade and at the same time better mitigate the potential casualties in the explosive demolition process.
针对多无人机受模型偏差和外部扰动导致的编队构型不稳定问题,提出一种模型补偿控制的思想,在此基础上设计基于补偿函数观测器(CFO)的模型补偿反步控制器并应用于编队协同控制,把编队协同控制问题转化为传统控制问题,实现多无人机在复杂外部扰动下的编队集合、飞行和队形切换.所提出的控制器不仅保留了基于Lyapunov准则的全局指数渐近稳定性,还采用CFO来准确估计模型偏差和干扰并补偿到控制器中,有效增强了编队系统整体的抗扰能力.最后通过仿真和实验验证了算法的有效性.
针对当前无人机集群协同侦察目标规模小、易重复侦察、不考虑能量约束而导致侦察效能低等问题,提出了一种基于能量约束的集群协同侦察多目标航迹规划算法.首先,对无人机任务过程中所需的各种能量进行建模,定量分析了飞行能量、悬停能量和数据传输能量的消耗;然后,借鉴图论思想对集群的侦察区域和侦察目标进行建模,在能量约束条件下构建了一种新的航迹规划算法模型,旨在对无人机集群航迹进行协同优化,降低集群整体能量消耗.仿真结果表明,该算法在能量消耗和侦察时间等方面都明显优于典型的轨迹距离算法,在10架无人机侦察100个目标情况下,能量消耗降低了48％,其侦察效率较高,收敛速度更快.
