Collaborating miniature drones for surveillance and reconnaissance

Bürkle, Axel

doi:10.1117/12.830408

Cited by 20 publications

(8 citation statements)

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“…Agent applications are extensively used in the entertainment industry (Damiano et al 2013); computer games for sports and battle simulation (Zuparic et al 2017, Guo andSprague 2016), landscape and land use design, management and visualization (Tieskens et al 2017, Valbuena et al 2010; Urban planning (Motieyan.and Mesgari 2018, Levy et al 2016; crowd modelling for public transport and community infrastructure design (Dickinson et al 2019, Hoy andShalaby 2016); Climate change and adaptation modelling (Amadou et al 2018); Architecture and Engineering design Li 2017, Van Dyke Parunak et al 2001) as well as hazard response and real-time three-dimensional mapping (Schlögl et al 2019, Bürkle 2009; transportation and surveillance using semi-automated or fully-autonomous vehicles such as drones and automobiles (Fagnant.and Kockelman 2014, de Swarte et al 2019 (Azam et al 2015) to name a few examples.…”

Section: Agent Applicationsmentioning

confidence: 99%

Spatial Agents for Geological Surface Modelling

Kemp

2021

Preprint

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Abstract. Semi-autonomous software entities called spatial agents can be programmed to perform spatial and property interrogation functions, estimations and construction operations for simple graphical objects, that may be usable in building three-dimensional geological surfaces. These surfaces form the building blocks from which full topological models are built and may be useful in sparse data environments, where ancillary or a-priori information is available. Critical in developing natural domain models is the use of gradient information. Increasing the density of spatial gradient information (fabric dips, fold plunges, local or regional anisotropies) from geologic feature orientations (planar and linear) is key to more accurate geologic modelling, and core to the functions of spatial agents presented herein. This study, for the first time, examines the potential use of spatial agents to increase these types of gradient constraints in the context of the Loop 3D project (loop3d.org) in which new complementary methods are being developed for modelling complex geology for regional applications. The Spatial Agent codes presented may act to densify and supplement gradient and on contact control points used in LoopStructural (www.github.com/Loop3d/LoopStructural) and Map2Loop (https://doi.org/10.5281/zenodo.4288476). Spatial agents are used to represent common geological data constraints such as interface locations and gradient geometry, and simple but topologically consistent triangulated meshes. Spatial agents can potentially be used to develop surfaces that conform to reasonable geological patterns of interest, provided they are embedded with behaviors that are reflective of the knowledge of their geological environment. Initially this would involve detecting simple geological constraints; locations, trajectories and trends of geological interfaces. Local and global eigenvectors enable spatial continuity estimates which can reflect geological trends with rotational bias using a quaternion implementation. Spatial interpolation of structural geology orientation data with spatial agents employ a range of simple nearest neighbour to inverse distance weighted (IDW) and quaternion based spherical linear interpolation (SLERP) schemes. This simulation environment implemented in NetLogo is potentially useful for complex geology - sparse data environments where extension, projection and propagation functions are needed to create more realistic geological forms.

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Section: Agent Applicationsmentioning

confidence: 99%

Spatial Agents for Geological Surface Modelling

Kemp

2021

Preprint

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“…For special applications, customized sensor modules can be integrated, e.g. smart cameras [1] or detectors for inflammable gases. …”

Section: Amfis Sensor Platformsmentioning

confidence: 99%

Managing heterogeneous networks of mobile and stationary sensors

et al. 2011

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Protecting critical infrastructure against intrusion, sabotage or vandalism is a task that requires a comprehensive situation picture. Modern security systems should provide a total solution including sensors, software, hardware, and a "control unit" to ensure complete security. Incorporating unmanned mobile sensors can significantly help to close information gaps and gain an ad hoc picture of areas where no pre-installed supervision infrastructure is available or damaged after an incident. Fraunhofer IOSB has developed the generic ground control station AMFIS which is capable of managing sensor data acquisition with all kinds of unattended stationary sensors, mobile ad hoc sensor networks, and mobile sensor platforms. The system is highly mobile and able to control various mobile platforms such as small UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles). In order to establish a real-time situation picture, also an image exploitation process is used. In this process, video frames from different sources (mainly from small UAVs) are georeferenced by means of a system of image registration methods. Relevant information can be obtained by a motion detection module. Thus, the image exploitation process can accelerate the situation assessment significantly

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“…Interrobot communication libraries do exist [28,42], but moving from independent parallel operation to system-wide coordination is also difficult, because it requires parallel programming mechanisms such as data sharing, synchronization, and deadlock avoidance. As a result, most drone applications today use only a small number of drones at a time even when more drones would improve performance, or evaluate largerscale cooperative strategies only in simulation [12,40].…”

Section: Background and Related Workmentioning

confidence: 99%

Team-level programming of drone sensor networks

Mottola

Moretta

Whitehouse

et al. 2014

Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems

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Autonomous drones are a powerful new breed of mobile sensing platform that can greatly extend the capabilities of traditional sensing systems. Unfortunately, it is still non-trivial to coordinate multiple drones to perform a task collaboratively. We present a novel programming model called team-level programming that can express collaborative sensing tasks without exposing the complexity of managing multiple drones, such as concurrent programming, parallel execution, scaling, and failure recovering. We create the VOLTRON programming system to explore the concept of team-level programming in active sensing applications. VOLTRON offers programming constructs to create the illusion of a simple sequential execution model while still maximizing opportunities to dynamically re-task the drones as needed. We implement VOLTRON by targeting a popular aerial drone platform, and evaluate the resulting system using a combination of real deployments, user studies, and emulation. Our results indicate that VOLTRON enables simpler code and produces marginal overhead in terms of CPU, memory, and network utilization. In addition, it greatly facilitates implementing correct and complete collaborative drone applications, compared to existing drone programming systems.

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Collaborating miniature drones for surveillance and reconnaissance

Cited by 20 publications

References 1 publication

Spatial Agents for Geological Surface Modelling

Spatial Agents for Geological Surface Modelling

Managing heterogeneous networks of mobile and stationary sensors

Team-level programming of drone sensor networks

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