Using heterogeneous multilevel swarms of UAVs and high-level data fusion to support situation management in surveillance scenarios

The development and usage of Unmanned Aerial Vehicles (UAVs) quickly increased in the last decades, mainly for military purposes. This technology is also now of high interest in non-military contexts like logistics, environmental studies and different areas of civil protection. While the technology for operating a single UAV is rather mature, additional efforts are still necessary for using UAVs in fleets (or swarms). The Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques (ASIMUT) project which is supported by the European Defence Agency (EDA) aims at investigating and demonstrating dedicated surveillance services based on fleets of UAVs. The aim is to enhance the situation awareness of an operator and to decrease his workload by providing support for the detection of threats based on multi-sensor multi-source data fusion. The operator is also supported by the combination of information delivered by the heterogeneous swarms of UAVs and by additional information extracted from intelligence databases. As a result, a distributed surveillance system increasing detection, high-level data fusion capabilities and UAV autonomy is proposed.

[1]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[2]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[3]  Ioannis M. Kyprianidis,et al.  A chaotic path planning generator for autonomous mobile robots , 2012, Robotics Auton. Syst..

[4]  George J. Vachtsevanos,et al.  Handbook of Unmanned Aerial Vehicles , 2014 .

[5]  Serge Chaumette,et al.  CARUS, an operational retasking application for a swarm of autonomous UAVs: First return on experience , 2011, 2011 - MILCOM 2011 Military Communications Conference.

[6]  Simin Nadjm-Tehrani,et al.  Mobility Models for UAV Group Reconnaissance Applications , 2006, 2006 International Conference on Wireless and Mobile Communications (ICWMC'06).

[7]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[8]  M. Melamed Detection , 2021, SETI: Astronomy as a Contact Sport.

[9]  Matthew Richardson,et al.  Just Add Weights: Markov Logic for the Semantic Web , 2008, URSW.

[10]  Paulo Cesar G. da Costa,et al.  PR-OWL: A Framework for Probabilistic Ontologies , 2006, FOIS.

[11]  Gerd Schneider,et al.  ISR analytics: Architectural and methodic concepts , 2012, 2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF).

[12]  Jürgen Beyerer,et al.  Extending adaptive world modeling by identifying and handling insufficient knowledge models , 2016, J. Appl. Log..

[13]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[14]  R. Steele Optimization , 2005 .

[15]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[16]  Mark D. Bedworth,et al.  High level data fusion , 1999 .

[17]  Ozgur Koray Sahingoz,et al.  Mobile networking with UAVs: Opportunities and challenges , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).

[18]  Florian Segor,et al.  Towards Autonomous Micro UAV Swarms , 2011, J. Intell. Robotic Syst..

[19]  Henry Leung,et al.  A Cooperative Mobile Robot Task Assignment and Coverage Planning Based on Chaos Synchronization , 2010, Int. J. Bifurc. Chaos.

[20]  Alan N. Steinberg,et al.  Revisions to the JDL data fusion model , 1999, Defense, Security, and Sensing.