Unmanned aerial systems coordinate target allocation based on wolf behaviors

Duan, Haibin; Qing, Yang; Deng, Yimin; Li, Pei; Qiu, Huaxin; Zhang, Tianjie; Zhang, Daifeng; Huo, Mengzhen; Shen, Yankai

doi:10.1007/s11432-018-9587-0

Cited by 27 publications

(10 citation statements)

References 8 publications

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“…The advantage of wolf pack hunting is to rely on strong bullying; to attack less [ 27 , 28 , 29 , 30 , 31 ]; clear division of labor; mutual help; and group attacks. The success of a wolf pack lies in cooperation, which has two meanings: the first is the information sharing of a situation, knowing oneself and the enemy, and winning a hundred battles.…”

Section: Gray Wolf Algorithmmentioning

confidence: 99%

Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm

Zhang

Liu

2022

Biomimetics

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The Gray Wolf (GWO) algorithm aims to address the path planning problem of multiple UAVs, and the scene setting is mainly to avoid threats, meet the constraints of UAVs themselves and avoid obstacles between UAVs. The scene setting is relatively simple. To address such problems, the problem of time windows is considered in this paper, so that the UAV can arrive at the same time, and the Gray Wolf algorithm is used to optimize the problem. Finally, the experimental results verify that the proposed method can plan a safe flight path in the process of multi-UAV flight and reach the goal point at the same time. The mean error of flight time between UAVs of the GWO is 0.213, which is superior to PSO (0.382), AFO (0.315) and GA (0.825).

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Section: Gray Wolf Algorithmmentioning

confidence: 99%

Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm

Zhang

Liu

2022

Biomimetics

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“…机载的各种传感器, 负责感知环境信息, 为无人机的控制系统提供反馈, 如三轴加速度计、微型陀螺仪、磁力计、全球定位系统 (global positioning system, GPS), 为无人机提供位置和姿态等信息. 无人机机间通信系统由一套无线自组织网络 (wireless ad hoc network, WANET) 组成, 负责多无人机之间的状态、指令的发送和接收 [22] . 此外, 地面站和数据通信系统负责无人机与地面工作人员之间、无人机之间的通信交流.…”

Section: 无人机集群系统设计unclassified

Verification of a UAV swarm flight simulating the passive inertial emergency obstacle avoidance behavior of a pigeon flock

et al. 2019

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“…Researchers are inspired by the swarm intelligence of wolves to solve concerned problems in cooperative control technology in [22,23]. Among the most bio-inspired cooperative approaches [24,25], the swarm intelligence of wolves is used make task assignment more reasonable and improve efficiency, rather than focusing on UAV swarms' cooperative control.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Tracking of ShenZhou Reentry Capsules Based on Heterogeneous UAV Swarms

Liu

Han

et al. 2022

Drones

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The safe landing and rapid recovery of the reentry capsules are very important to manned spacecraft missions. A variety of uncertain factors, such as flight control accuracy and wind speed, lead to a low orbit prediction accuracy and a large landing range of reentry capsules. It is necessary to realize the autonomous tracking and continuous video observation of the reentry capsule during the low-altitude phase. Aiming at the Shenzhou return capsule landing mission, the paper proposes a new approach for the autonomous tracking of Shenzhou reentry capsules based on video detection and heterogeneous UAV swarms. A multi-scale video target detection algorithm based on deep learning is developed to recognize the reentry capsules and obtain positioning data. A self-organizing control method based on virtual potential field is proposed to realize the cooperative flight of UAV swarms. A hardware-in-the-loop simulation system is established to verify the method. The results show that the reentry capsule can be detected in four different states, and the detection accuracy rate of the capsule with parachute is 99.5%. The UAV swarm effectively achieved autonomous tracking for the Shenzhou reentry capsule based on the position obtained by video detection. This is of great significance in the real-time searching of reentry capsules and the guaranteeing of astronauts’ safety.

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Unmanned aerial systems coordinate target allocation based on wolf behaviors

Cited by 27 publications

References 8 publications

Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm

Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm

Verification of a UAV swarm flight simulating the passive inertial emergency obstacle avoidance behavior of a pigeon flock

Autonomous Tracking of ShenZhou Reentry Capsules Based on Heterogeneous UAV Swarms

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