Robust Multi-UAV Data Collection

Ajorlou, Amir; Homaifar, Abdollah; Esterline, Albert; Moore, Jay G.; Bamberger, Robert J.

doi:10.2514/6.2007-2811

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Ajorlou et al [75] discuss the co-ordination of a team of UAVs assigned with the task of collecting data from a set of remote unattended ground sensors over time. The control centre creates time-limited sensor-visit tasks and assigns them to the UAVs.…”

Section: Level 5 -Group Co-ordinationmentioning

confidence: 99%

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Suresh

Ghose

2010

Proceedings of the Institution of Mechanical Engineers, Part G:

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Autonomous mission control, unlike automatic mission control which is generally pre-programmed to execute an intended mission, is guided by the philosophy of carrying out a complete mission on its own through online sensing, information processing, and control reconfiguration. A crucial cornerstone of this philosophy is the capability of intelligence and of information sharing between unmanned aerial vehicles (UAVs) or with a central controller through secured communication links. Though several mission control algorithms, for single and multiple UAVs, have been discussed in the literature, they lack a clear definition of the various autonomous mission control levels. In the conventional system, the ground pilot issues the flight and mission control command to a UAV through a command data link and the UAV transmits intelligence information, back to the ground pilot through a communication link. Thus, the success of the mission depends entirely on the information flow through a secured communication link between ground pilot and the UAV. In the past, mission success depended on the continuous interaction of ground pilot with a single UAV, while present day applications are attempting to define mission success through efficient interaction of ground pilot with multiple UAVs. However, the current trend in UAV applications is expected to lead to a futuristic scenario where mission success would depend only on interaction among UAV groups with no interaction with any ground entity. However, to reach this capability level, it is necessary to first understand the various levels of autonomy and the crucial role that information and communication plays in making these autonomy levels possible. This article presents a detailed framework of UAV autonomous mission control levels in the context of information flow and communication between UAVs and UAV groups for each level of autonomy.

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Section: Level 5 -Group Co-ordinationmentioning

confidence: 99%

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Suresh

Ghose

2010

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…The special case of controlled mobility of helper nodes to improve the performance of delay-tolerant networks using unmanned aircraft as communication helpers is seen by many as a promising technique [22], [23]. In addition, combining mobility and distance-dependent communication can lead to better-performing mobile networks.…”

Section: Introductionmentioning

confidence: 99%

Delay-Tolerant Communication using Mobile Robotic Helper Nodes

Henkel¹,

Brown²

2008

Proceedings of the 6th Intl Symposium on Modeling and Optimization

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Delay tolerant networks can exhibit long phases of complete unconnectedness, where several nodes will not be able to communicate effectively. In those circumstances controlling the movement of helper nodes, that facilitate message exchanges instead of relying on direct communication, can improve network performance. We investigate when controlled movement is beneficial and how helper nodes need to move or position themselves to achieve maximum network performance. Three communication modes on a single link and in a hub model are analyzed: direct communication, communication through one or more relays, and communication via data ferries. Hereby we extend the traditional in-range, out-of-range communication model to include a distance dependence which gives a differentiated ratedistance profile. Further, we characterize controlled mobility of mobile data ferries to form static relay paths or dynamically ferry data between nodes. Achievable throughput, delay, and distance performance of the link modes change with channel and ferry characteristics. The union of these performance regions forms the achievable communication space for a given DTN scenario. Novel phase plots show when it is feasible to use one of the mobility schemes versus direct communication. The implications of these modes for practical link-layer designs are discussed using a realworld DTN example of sensor data collection with unmanned aircraft.

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Delay-tolerant communication using mobile robotic helper nodes

Henkel

Brown

2008

2008 6th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks and Workshops

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Robust Multi-UAV Data Collection

Cited by 3 publications

References 5 publications

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Delay-Tolerant Communication using Mobile Robotic Helper Nodes

Delay-tolerant communication using mobile robotic helper nodes

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