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Multiple Fixed-Wing Unmanned Aerial Vehicles

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Such enemies can be included in the convex hull based on measured information of enemy vehicles. For the sake of feasibility, the optimization domain is limited to a certain area where all fixed-wing UAVs can arrive at a specific time (i.e., joint impact time). Note that, the optimized impact points move with the change of the convex hull caused by the movement of the opposing clutch.

By considering the physical limitations of fixed-wing UAVs, the most effective set of impact points within the fuselage is selected. In addition, the combination of impact point and controller optimization dramatically increases the effectiveness of the defense mission. The shape of the convex hull is subject to change due to the movement of the opposing swarm and limited measurements.

Especially for sensors with a limited field of view, the measurement may change depending on the location of the fixed-wing UAV. Therefore, the fixed-wing UAV sensor measurement is assumed to be perfect without noise or information loss. Due to the physical limitations of multi-rotor vehicles, enemy vehicles are considered to have poorer mobility than fixed-wing defensive UAVs [35].

In addition, the optimization domain is limited to ensure the reachability of fixed-wing UAVs, similar to [36]. By combining the above-mentioned utility functions, Jcoverage and Jtime, the optimized impact points can achieve maximum coverage and minimum arrival time of fixed-wing UAVs. Obviously, the number of enemies detected will be limited when fixed-wing UAVs approach them due to their limited field of view.

Figure 1: Concept of adversarial swarm defence.
Figure 1: Concept of adversarial swarm defence.

Scenario 1

In this regard, we assume that the opposing swarm and the corresponding convex hull can be considered relatively static with respect to the fixed-wing UAV for the first three simulations. The numerical simulations performed in this paper focus on proving the feasibility of the proposed algorithm rather than proving its real-time performance. For GA, we use the GA solver in the MATLAB global optimization tool to optimize the hit points.

The purple circles enclosing the impact points are the explosive area of ​​radius Rexp. One can notice that the fuselage degenerates when the UAVs get closer to the enemies due to the limited FOV. To prevent serious loss of information, the UAVs stop updating their impact points if they get closer than Ropt as explained before.

The black circle is the outer boundary of the enemy while the points at the end of the trajectories are the final hit points of the respective UAVs. Meanwhile, the history of the distance between the UAVs and their impact points is shown in Fig. It can be easily observed that the UAVs arrive at the destination simultaneously, reaching consensus on the range values ​​to go before reaching the impact points.

Moreover, the derivative of the range-to-go values ​​also converged to V, as shown in Fig. It implies that the fixed-wing UAVs are directly heading towards their impact points with the same distance-to-go. In addition, the reduction of residual distance is observed in the case of impact point optimization, marked as dashed lines in Fig.

During the simulation, the defending UAVs achieve the CRof 1 for most of the time despite the distance-to-go (i.e. impact time) decrease. This means that the fixed-wing swarm achieves maximum interception efficiency by placing the entire explosive area in the opposing swarm without any overlap between them. From the above results, it is shown that the proposed adversarial swarm defense algorithm can simultaneously achieve the salvo attack, maximum coverage and minimum arrival time.

Figure 10: Time history of scenario 1.
Figure 10: Time history of scenario 1.

Scenario 2

Comparative simulation 1 without the impact point optimization

23, there is a large fluctuation in the distance to go, although the fixed-wing UAVs manage to reach the designated impact points at the same time. This is because the impact time controller detours UAVs to match the impact time. As a result, the time to impact is significantly increased and the fixed-wing UAVs offer more abrupt movement in the absence of the impact point optimization.

This result proves that our proposed algorithm can greatly contribute to the defense swarm mission performance by smoothing the trajectory and reducing the impact time.

Figure 17: Time history of convex hull of scenario 2.
Figure 17: Time history of convex hull of scenario 2.

Comparative simulation 2 against a moving adversarial swarm

The derivative of distance-to-go, shown in Fig. As a result, the time to impact is significantly increased and the fixed-wing UAVs offer more abrupt movement in the absence of the impact point optimization. For Case A, since the fixed-wing UAVs update the impact points at every 3[s], the coverage ratio may decrease during the optimization interval. This gap will be larger if the opposing swarm moves faster, and at some point the fixed-wing UAVs cannot find the right impact points even before approaching Ropt.

As a future work, the estimator to estimate the enemy swarm states will be explored. Gao, “A formation maintenance and reconstruction method of UAV swarm based on distributed control,” Aerospace Science and Technology, vol. Munasypov, “Consensus-based cooperative control of parallel fixed-wing UAV formations via adaptive backstepping,” Aerospace Science and Technology, vol.

Hwang, “Hybrid flocking control algorithm for fixed-wing aircraft,” Journal of Guidance, Control, and Dynamics, vol. Gong, “A self-organized search and attack algorithm for multiple unmanned aerial vehicles,” Aerospace Science and Technology , vol. Shim, “An Autonomous Air Combat Framework for Two-on-Two Combat Based on Basic Combat Maneuvers,” Aerospace Science and Technology, vol.

Duan, "Chaotic predator-prey biogeography-based optimization approach for UCAV path planning," Aerospace Science and Technology, vol. Han, "An intelligent cooperative mission planning scheme for UAV swarm in uncertain dynamic environment," Aerospace Science and Technology, vol. Kim, "Persistent standoff tracking guidance using constrained particle filter for multiple UAVs,” Aerospace Science and Technology , vol.

Guo, “Two-stage cooperative guidance strategy using a prescribed time-optimal consensus method,” Aerospace Science and Technology, vol. Xie, “Distributed guidance for interception using multiple rotary-wing unmanned aerial vehicles,” IEEE Transactions on Industrial Electronics, vol. Cheng, “Three-dimensional robust fixed-time cooperative guidance law for simultaneous attack with impact angle limitation,” Aerospace Science and Technology, p.

Yuan, “Reachability-Based Cooperative Strategy for Intercepting a Highly Maneuvering Target Using Inferior Missiles,” Aerospace Science and Technology, vol. In addition, I would like to thank Professor Jeong hwan Jeon for all the opportunities given to me to further my research.

Figure 26: Trajectory of fixed-wing UAVs against moving adversarial swarm.
Figure 26: Trajectory of fixed-wing UAVs against moving adversarial swarm.

수치

Figure 1: Concept of adversarial swarm defence.
Figure 2: Overview of swarm defence algorithm.
Figure 3: Two-dimensional geometry of a fixed-wing UAV.
Figure 4: Reachable area of the fixed-wing UAV for a certain period of time.
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