Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform

Abstract

Route planning is a key technology for an unmanned aerial vehicle (UAV) to fly reliably and safely in the presence of a threat environment. Existing route planning methods are mainly based on the simulation scene, whereas approaches based on the virtual globe platform have rarely been reported. In this paper, a new planning space for the virtual globe and the planner is proposed and a common threat model is constructed for threats including a no-fly zone, hazardous weather, radar coverage area, missile killing zone and dynamic threats. Additionally, an improved ant colony optimization (ACO) algorithm is developed to enhance route planning efficiency and terrain masking ability. Our route planning methods are optimized on the virtual globe platform for practicability. A route planning system and six types of planners were developed and implemented on the virtual globe platform. Finally, our evaluation results demonstrate that our optimum planner has better performance in terms of fuel consumption, terrain masking, and risk avoidance. Experiments also demonstrate that the method and system described in this paper can be used to perform global route planning and mission operations.

Sun Tracking Technique Applied to a Solar Unmanned Aerial Vehicle

“…The angles of deflection of the control surfaces used for the last simulation are shown in the Figure 11, where a smoother behavior of the control surfaces movement can be seen in Figure 9. Finally, we can appreciate that the controllers proposed for the SUAV are useful and can reach the desired roll and yaw angles, nevertheless, to perform trajectories used for photogrammetry as shown in Figure 2, it is important to develop or integrate a tracking trajectory control as in References [22][23][24].…”

Softwarization of UAV Networks: A Survey of Applications and Future Trends

“…The associate editor coordinating the review of this manuscript and approving it for publication was Kai Li . can provide a scalable and higher chance of obstacle-free wireless communications [6]. UAVs can be easily deployed in the sky and quickly form a flexible aerial platform [7].…”

2D Optimal Trajectory Planning Problem in Threat Environment for UUV with Non-Uniform Radiation Pattern

“…The formalization of these problems can differ in dependence of many parameters: physical nature of detection fields, classes of acceptable control, the type of quality criteria, the number of detectors, the volume of information available to conflicting parties. As a result, based on known data, a distribution map of normalized or absolute levels of risks (threats) is created, as shown in Reference [ 11 ].…”

“…These algorithms include the shortest graph-based path algorithm known as A *, the artificial potential field algorithm, sequential quadratic programming and so on. Additionally, an approach based on ant-colony behavior is often used [ 11 , 17 ]. In recent years, due to the progress of neural networks, the deep learning and specifically deep reinforcement learning approaches [ 18 , 19 ] are becoming more and more popular.…”