Wireless Network Communications Architecture for Swarms of Small UAVs

Bamberger, Robert J.; Scheidt, David; Hawthorne, Ryan; Farrag, Osama; White, Michael J.

doi:10.2514/6.2004-6594

Cited by 11 publications

(4 citation statements)

References 1 publication

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“…Indeed, depending on to the type of robot, they will have more or less degree of freedom to move [55]. Finally, the utilization of WiFi and XBee allows us to send the required data in a short period of time, facilitating and reducing the time of decision-making [56][57][58].…”

Section: Related Workmentioning

confidence: 99%

Toward a robot swarm protecting a group of migrants

Vaidis

Otis

2020

Intel Serv Robotics

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Different geopolitical conflicts of recent years have led to mass migration of several civilian populations. These migrations take place in militarized zones, indicating real danger contexts for the populations. Indeed, civilians are increasingly targeted during military assaults. Defense and security needs have increased; therefore, there is a need to prioritize the protection of migrants. Very few or no arrangements are available to manage the scale of displacement and the protection of civilians during migration. In order to increase their security during mass migration in an inhospitable territory, this article proposes an assistive system using a team of mobile robots, labeled a rover swarm that is able to provide safety area around the migrants. We suggest a coordination algorithm including CNN and fuzzy logic that allows the swarm to synchronize their movements and provide better sensor coverage of the environment. Implementation is carried out using on a reduced scale rover to enable evaluation of the functionalities of the suggested software architecture and algorithms. Results bring new perspectives to helping and protecting migrants with a swarm that evolves in a complex and dynamic environment.

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Section: Related Workmentioning

confidence: 99%

Toward a robot swarm protecting a group of migrants

Vaidis

Otis

2020

Intel Serv Robotics

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“…Any order from ground station is sent to the UAV IC via 802.11b. The next generation of such UAVs will work as a collaborative autonomous unit where each UAV is receiving high level mission commands from the ground station [38] to accomplish a set of objectives. Communication between UAVs should be established without significant setup so there is a need for future plans to enhance communication architecture with strong support by new transport layer protocols.…”

Section: Unmanned Aerial Vehiclementioning

confidence: 99%

Cross layer design for mobile Ad-Hoc Unmanned Aerial Vehicle communication networks

Alshbatat

Dong

2010

2010 International Conference on Networking, Sensing and Control (ICNSC)

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Mobile Ad-Hoc network (MANET) is a popular type of wireless network that is formed by a collection of mobile nodes. Each node in such a network has the capability to communicate with its neighbors and non-neighbors through a wireless medium without using any existing network infrastructure. Due to the lack of infrastructure, all nodes in Ad-Hoc network are designed to act as an end system and a router for other nodes. Traditionally, the dominant design methodology for network protocols was based on the open systems interconnection (OSI) reference model. This methodology divided the stack into seven layers in which each layer operates independently. Due to the dynamics of the Unmanned Aerial Vehicle (UAV) Ad-Hoc network, the conventional protocol stack is not sufficiently flexible to achieve certain quality of services (QoS) required by some applications. To overcome the limitations of the layering technique, cross-layering approach was implemented in this dissertation to adjust some key parameters in the first three layers of the OSI model based on the aircraft attitude variations (pitch, roll and yaw) and the variation of wireless links. To that respect, directional antennas were used by the UAVs to extend the coverage area and reduce the number of hops between the source and destination. Meanwhile, since the traditional Medium Access Control (MAC) protocol assumed the use of Omni-directional antennas, we designed a new MAC scheme that adapts its parameters based on the channel Bit-Error-Rate (BER) which is affected by the new antenna system and aircraft attitude. As for the routing protocol, we modified the Optimized Link State Routing (OLSR) protocol in such a way that the decision for selecting the route will be based on a local profile that holds the gathered information from the first three layers. UAV Ad-Hoc network was implemented by using a discrete event simulator called Optimized Network Engineering Tool (OPNET). We investigated the performance of the proposed techniques and compared them with the existing schemes. The simulation results showed that the proposed techniques improved the network performance and gave results better than the existing protocols in terms of throughput and End-to-End delay.

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“…As technological advancements continue and application scenarios broaden, the distinctive value of UAV swarm cooperative networks is anticipated to be increasingly prominent across diverse fields. A pivotal challenge, however, is the effective management and control of these UAV swarms to maximize collaborative effects, particularly in real-time coordination within expansive, dynamic environments [6][7][8][9]. Consequently, the formulation of an efficient distributed computing optimization strategy has become imperative.…”

Section: Introductionmentioning

confidence: 99%

Distributed Computing Optimization in Unmanned Aerial Vehicle Swarm Cooperative Networks

Luo,

Zhang,

Shi

et al. 2024

CIT. J. comput. inf. technol

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The Unmanned Aerial Vehicle (UAV) swarm cooperative networks across diverse domains present significant challenges in management and control to maximize collaborative efficiency, particularly in applications for large-scale and dynamic environments where real-time coordination is essential. This challenge has led to the development of a UAV swarm cooperative network flow prediction model, facilitating the strategic anticipation of network dynamics. A novel algorithm for distributed, finite-time cooperative control is introduced, grounded in this model. This algorithm optimizes the operational efficiency and control precision of UAV systems, while simultaneously augmenting the collaborative efficiency and stability of the UAV swarms. It marks a significant advancement in distributed computing strategies, offering a viable solution for the real-time coordination challenges in extensive UAV swarm cooperative networks. The proposed approach leverages multi-agent technology, integrating spatial-temporal constraints, location limitations, and path selection in cooperative behavior for dynamic and effective network management. This study not only enhances the understanding of UAV swarm dynamics but also contributes to the practical application of UAV swarms in areas requiring precise coordination and robust network stability.

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Wireless Network Communications Architecture for Swarms of Small UAVs

Cited by 11 publications

References 1 publication

Toward a robot swarm protecting a group of migrants

Toward a robot swarm protecting a group of migrants

Cross layer design for mobile Ad-Hoc Unmanned Aerial Vehicle communication networks

Distributed Computing Optimization in Unmanned Aerial Vehicle Swarm Cooperative Networks

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