Multi-UAVs Formation Autonomous Control Method Based on RQPSO-FSM-DMPC

Zhou, Shaolei; Kang, Yuhang; Dai, Hongde; Zhou, Chao

doi:10.1155/2016/4878962

Cited by 7 publications

(6 citation statements)

References 38 publications

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“…Comparing the fitness graph with a similar technique presented in Zhou et al (2016), we can see that the fitness levels for the traditional PSO and their proposed method QPSO are in the range of 71-72. While our designed method performs significantly better with fitness levels in the range of 20-30.…”

Section: Scenariomentioning

confidence: 98%

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Multi-unmanned aerial vehicle swarm formation control using hybrid strategy

Ali

Han

2021

Transactions of the Institute of Measurement and Control

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This study proposes a novel hybrid strategy for formation control of a swarm of multiple unmanned aerial vehicles (UAVs). To enhance the fitness function of the formation, this research offers a three-dimensional formation control for a swarm using particle swarm optimization (PSO) with Cauchy mutant (CM) operators. We use CM operators to enhance the PSO algorithm by examining the varying fitness levels of the local and global optimal solutions for UAV formation control. We establish the terrain and the fixed-wing UAV model. Furthermore, it also models different control parameters of the UAV as well. The enhanced hybrid algorithm not only quickens the convergence rate but also improves the solution optimality. Lastly, we carry out the simulations for the multi-UAV swarm under terrain and radar threats and the simulation results prove that the hybrid method is effective and gives better fitness function.

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Section: Scenariomentioning

confidence: 98%

“…This study implements the designed hybrid algorithm effectively for multi-UAV swarm formation under the radar, mountainous terrain, and obstacle avoidance parameters. Finally, the fitness of the multi-UAV formation is evaluated in detail and the results are better than similar techniques, like the one described by Zhou et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Multi-unmanned aerial vehicle swarm formation control using hybrid strategy

Ali

Han

2021

Transactions of the Institute of Measurement and Control

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“…For example, the formation of the finite state machine for multiple UAVs to solve an assigned task is shown in figure 17. Zhou et al [245] discussed the problem of solving the formation of multiple UAV's using finite state automata. The steps followed for multiple UAV movement in finite machine are described as follows:…”

Section: F Multivariate Cryptosystemmentioning

confidence: 99%

Survey of Promising Technologies for Quantum Drones and Networks

Kumar¹,

Bhatia

Kaushik

et al. 2021

IEEE Access

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Due to recent advancements in quantum drones, the Internet of Quantum Drones (IoQDs), and Drone-to-Satellite connectivity, several advantages have been anticipated for real-time applications. This work examines quantum computing issues, including quantum data processing, techniques, circuits, and algorithms important for quantum drones or their networks. Here, we discussed the current research trends on quantum computing, quantum-safe computing, or post-quantum cryptography important to quantum networks, followed by the numerous advantages, limitations, future advancements, and research issues connected with quantum technologies, drones, and their network. This work has also prepared a taxonomy of quantum-related areas depending upon the logic of their learning, followed by a review of each of these areas. We review the most recent work over quantum algorithms used in various-quantum-related areas and networks, the role of quantum satellites for drone-based networks and communications, how quantum artificial intelligence and quantum machine learning are important for quantum drones, networks and futuristic applications, quantum attacks, quantum genetic algorithms, and the importance of post-quantum and quantum-safe cryptography. The challenges and research directions in these domains are explored as well. Lastly, this work presents an overview of the current state of knowledge in various promising technologies that are recently found to be important for quantum drones and networks.

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“…In [52], authors presented that the mutual collision avoidance problem of UAVs should be taken into design consideration for the mission-critical applications such as covert penetration and active combat against enemy. In addition, the communication constraint problem in such networks is also addressed.…”

Section: H Combat With External Disturbancesmentioning

confidence: 99%

“…In addition, the communication constraint problem in such networks is also addressed. The authors in [52] used particle swarm optimization technique to solve the cost function of multi-UAV formation.…”

Section: H Combat With External Disturbancesmentioning

confidence: 99%

Routing Protocols for Unmanned Aerial Vehicle Networks: A Survey

2019

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Unmanned aerial vehicles (UAVs) have gained popularity for diverse applications and services in both the military and civilian domains. For cooperation and collaboration among UAVs, they can be wirelessly interconnected in an ad hoc manner, resulting in a UAV network. UAV networks have unique features and characteristics that are different from mobile ad hoc networks and vehicular ad hoc networks. The dynamic behavior of rapid mobility and topology changes in UAV networks makes the design of a routing protocol quite challenging. In this paper, we review the routing protocols for UAV networks, in which the topology-based, position-based, hierarchical, deterministic, stochastic, and social-network-based routing protocols are extensively surveyed. The routing protocols are then compared qualitatively in terms of their major features, characteristics, and performance. Open issues and research challenges are also discussed in the perspective of design and implementation.INDEX TERMS Unmanned aerial vehicle network, flying ad hoc network, drone ad hoc network, routing protocol, rapid mobility, dynamic topology, scalability.

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Multi-UAVs Formation Autonomous Control Method Based on RQPSO-FSM-DMPC

Cited by 7 publications

References 38 publications

Multi-unmanned aerial vehicle swarm formation control using hybrid strategy

Multi-unmanned aerial vehicle swarm formation control using hybrid strategy

Survey of Promising Technologies for Quantum Drones and Networks

Routing Protocols for Unmanned Aerial Vehicle Networks: A Survey

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