CARUS, an operational retasking application for a swarm of autonomous UAVs: First return on experience

Chaumette, Serge; Laplace, Rémi; Mazel, Christophe; Mirault, R.; Dunand, A.; Lecoutre, Y.; Perbet, J-N.

doi:10.1109/milcom.2011.6127613

Cited by 29 publications

(18 citation statements)

References 4 publications

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“…Then, the need for a large number of UAVs is not justified in such a case. Usually, UAV mission involves an average of 3 to 4 UAVs [5] [6] [7] [8] [9]. This has the advantage of reducing the impact of scalability issues which affects several MANET architectures.…”

Section: Uaanets Characteristicsmentioning

confidence: 99%

Emulation-Based Performance Evaluation of Routing Protocols for Uaanets

Maxa¹,

Roudière²,

Larrieu³

2015

Lecture Notes in Computer Science

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Abstract. UAV Ad hoc NETwork (UAANET) is a subset of the wellknown Mobile Ad-hoc NETworks (MANETs). It consists of forming an ad hoc network with multiple small Unmanned Aerial Vehicles (UAVs) and the Ground Control Station (GCS). Similar to MANETs, the UAANET communication architecture is infrastructure-less and self-configuring network of several nodes forwarding data packets. However, it also has some specific features that brings challenges on network connectivity. Consequently, an adapted routing protocol is needed to exchange data packets within UAANETs. In this paper, we introduce a new hybrid experimental system that can evaluate different types of adhoc routing protocols under a realistic UAANET scenario. It is based on virtual machines and the Virtualmesh [1] framework to emulate physical aspects. We evaluated AODV, DSR and OLSR efficiency in a realistic scenario with three UAVs scanning an area. Our results show that AODV outperformed OLSR and DSR.

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Section: Uaanets Characteristicsmentioning

confidence: 99%

Emulation-Based Performance Evaluation of Routing Protocols for Uaanets

Maxa¹,

Roudière²,

Larrieu³

2015

Lecture Notes in Computer Science

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“…We decided on formalizing this model on top of the ADA-GRS [2] [18] (Asynchronous Dynamicity Aware Graph Relabeling System) which is only based on one way broadcasts and local computations. The limitations imposed by this model are the keys to ensure degraded mode of operation and autonomy by fostering a totally decentralized and distributed approach.…”

Section: Amirale Global Timing Modelmentioning

confidence: 99%

“…The goal is to collect information about its state of degradation and to construct a 3D map of the inside (i.e., SLAM -Simultaneous Localization and Mapping). Another example is the CARUS project [2] which has been developed at the University of Bordeaux. The goal is to enable a swarm of UAVs (Unmanned Aerial Vehicles) to autonomously achieve the surveillance of a region by dynamically sharing small areas of this region between a number of UAVs.…”

Section: Introductionmentioning

confidence: 99%

A Mission-Oriented Service Discovery Mechanism for Highly Dynamic Autonomous Swarms of Unmanned Systems

Autefage

Chaumette²,

Magoni³

2015

2015 IEEE International Conference on Autonomic Computing

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Abstract-Over the past few years, many research projects have begun to focus on swarms of mobile unmanned systems (e.g., drones, ground robots) globally referred as UMS. These systems, because of the many sensors and actuators they can embed, are suitable for autonomous missions in 3D (Dull, Dirty and Dangerous) environments for instance. However, embedding a large number of capabilities in all of members of a swarm is expensive in terms of cost, weight and energy consumption. Thus, it is usually more efficient to embed only a single or a few capabilities within each UMS. It then becomes necessary to provide a discovery mechanism built into the swarm in order to allow its members to share their capabilities and to collaborate for achieving a global mission. These shared capabilities are called services. In this paper, we propose a new service discovery system called AMiRALE for Asynchronous Missions Relay for Autonomous and Lively Entities dedicated to highly volatile, autonomous and mobile swarms of UMS. Our solution is independent of both nodes' mobility and connectivity patterns. Moreover, it supports heterogeneous swarms and degraded conditions of operation (i.e., message loss, UMS loss and disconnected network). It is also totally decentralized and enables both discovery and service usage. We provide a description of the theoretical model of our AMiRALE system as well as several simulation results obtained from a park cleaning scenario.

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“…Drones are used in many military and civil applications [1], [2], such as surveillance, policing, firefighting, and search and rescue missions. Drones can form swarms for self-organization and collaboration in airborne mobile ad hoc networks [3]. In addition to their military applications, drone swarms will become an increasingly important component of smart cities [4], [5].…”

Section: Introductionmentioning

confidence: 99%

ADDSEN: Adaptive Data Processing and Dissemination for Drone Swarms in Urban Sensing

Arkhipov

Kim

et al. 2016

IEEE Trans. Comput.

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Abstract-We present ADDSEN middleware as a holistic solution for Adaptive Data processing and dissemination for Drone swarms in urban SENsing. To efficiently process sensed data in the middleware, we have proposed a cyber-physical sensing framework using partially ordered knowledge sharing for distributed knowledge management in drone swarms. A reinforcement learning dissemination strategy is implemented in the framework. ADDSEN uses online learning techniques to adaptively balance the broadcast rate and knowledge loss rate periodically. The learned broadcast rate is adapted by executing state transitions during the process of online learning. A strategy function guides state transitions, incorporating a set of variables to reflect changes in link status. In addition, we design a cooperative dissemination method for the task of balancing storage and energy allocation in drone swarms. We implemented ADDSEN in our cyber-physical sensing framework, and evaluation results show that it can achieve both maximal adaptive data processing and dissemination performance, presenting better results than other commonly used dissemination protocols such as periodic, uniform and neighbor protocals in both single-swarm and multi-swarm cases.

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CARUS, an operational retasking application for a swarm of autonomous UAVs: First return on experience

Cited by 29 publications

References 4 publications

Emulation-Based Performance Evaluation of Routing Protocols for Uaanets

Emulation-Based Performance Evaluation of Routing Protocols for Uaanets

A Mission-Oriented Service Discovery Mechanism for Highly Dynamic Autonomous Swarms of Unmanned Systems

ADDSEN: Adaptive Data Processing and Dissemination for Drone Swarms in Urban Sensing

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