Live-fly, large-scale field experimentation for large numbers of fixed-wing UAVs

Chung, Timothy H.; Clement, Michael R.; Day, Michael A.; Jones, Kevin D.; Davis, Duane; Jones, Marianna

doi:10.1109/icra.2016.7487257

Cited by 78 publications

(69 citation statements)

References 15 publications

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“…The robots use Visual-Inertial Odometry (VIO) for state estimation, which allows them to navigate in challenging outdoor conditions, including at night and with wind. An even larger outdoor swarm of 50 fixed-wing UAVs was also recently demonstrated [212], with the goal of becoming a testbed to study adversarial swarm systems. Note that popular drones shows performed by Intel 1 or EHang 2 use GPS-based navigation with pre-defined trajectories.…”

Section: A Platformsmentioning

confidence: 99%

“…For example, in [212] the full 50 robots in the swarm were all simultaneously airborne for only 10 minutes out of the 60 minutes it took for all of the vehicles to be launched and land safely. Unlike fixed-wing UAVs, vertical take-off and landing UAVs, such as multi-rotors, are able to simultaneously take off and land but have a significantly shorter flight time.…”

Section: B Vehicle Power Managementmentioning

confidence: 99%

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A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: A Platformsmentioning

confidence: 99%

Section: B Vehicle Power Managementmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…Multi-UAV coordination and autonomy for the NPS (Naval Postgraduate School) system is implemented on a companion computer running Ubuntu 14.04 Linux and the Robot Operating System (ROS) [24]. The companion computer communicates with the Pixhawk autopilot over a serial connection that is simulated by the SITL implementation.…”

Section: Software-in-the-loop Implementationmentioning

confidence: 99%

“…Each behavior is implemented within this architecture as an independent ROS node as described in [24]. The autonomous engagement algorithms presented in this paper are implemented as behaviors in this architecture for testing in the SITL environment and ultimately in live-fly experiments.…”

Section: Software-in-the-loop Implementationmentioning

confidence: 99%

“…Numerical simulation results demonstrate the problem and the missionoriented effectiveness of the developed integrated PPN-based guidance strategy for this problem. Implementation of these guidance laws in a waypoint navigation-based manner, suitable for most modern-day autopilots, including those used in NPS swarm UAV live-fly testbed [23], [24], is demonstrated by software-in-the-loop integrated simulations.…”

Section: Introductionmentioning

confidence: 99%

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Development and testing of the intercept primitives for planar UAV engagement

Ghosh

Davis

Chung

et al. 2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-With the advance in technologies and applications involving unmanned aerial systems cooperation among the autonomous agents within such an unmanned system as well as preparedness for responding to adversarial threat to such a system has become of pivotal importance. A multiphase operational scenario of such cooperative and adversarial engagement, motivated by manned aerial engagement scenarios, is presented in this paper. A Proportional navigation-based integrated guidance methodology is proposed and investigated as a candidate strategy for intercept primitives in such realistic multi-phase UAV engagements. Numerical examples are presented to demonstrate the performance of the proposed guidance strategy for an UAV in such an engagement. Finally, implementation of these guidance laws in a waypoint-based manner, well suited for use in modern-day autopilots of UAVs, is demonstrated in software-in-the-loop simulations, further accelerating future live-fly capabilities for autonomous aerial engagements.

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A mission‐based architecture for swarm unmanned systems

Giles

Giammarco

2019

Systems Engineering

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This research applies a mission engineering approach with model‐based systems engineering foundations to formalize a swarm unmanned system design methodology and architecture. The architectural framework and methodology herein presented extend current swarm system design methods, which are primarily bottom‐up approaches focused on the behavior of individual agents. We introduce a top‐down, hierarchical approach with an overarching mission decomposed into phases, tactics, plays, and algorithms. Three unmanned aerial vehicle swarm case studies (one of which is discussed in this paper) are used to demonstrate the approach and its effectiveness in formalizing a mission‐based framework of common patterns in swarm missions that promote architecture reusability.

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Live-fly, large-scale field experimentation for large numbers of fixed-wing UAVs

Cited by 78 publications

References 15 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Development and testing of the intercept primitives for planar UAV engagement

A mission‐based architecture for swarm unmanned systems

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