In the next wave of swarm-based applications, unmanned aerial vehicles (UAVs) need to communicate with peer drones in any direction of a three-dimensional (3D) space. On a given drone and across drones, various antenna positions and orientations are possible. We know that, in free space, high levels of signal loss are expected if the transmitting and receiving antennas are cross polarized. However, increasing the reflective and scattering objects in the channel between a transmitter and receiver can cause the received polarization to become completely independent from the transmitted polarization, making the cross-polarization of antennas insignificant. Usually, these effects are studied in the context of cellular and terrestrial networks and have not been analyzed when those objects are the actual bodies of the communicating drones that can take different relative directions or move at various elevations. In this work, we show that the body of the drone can affect the received power across various antenna orientations and positions and act as a local scatterer that increases channel depolarization, reducing the cross-polarization discrimination (XPD). To investigate these effects, we perform experimentation that is staged in terms of complexity from a controlled environment of an anechoic chamber with and without drone bodies tomore »
Investigating the survivability of drone swarms with flocking and swarming flight patterns using Virtual Reality
It is now possible to deploy swarms of drones
with populations in the thousands. There is growing interest
in using such swarms for defense, and it has been natural
to program them with bio-mimetic motion models such as
flocking or swarming. However, these motion models evolved to
survive against predators, not enemies with modern firearms.
This paper presents experimental data that compares the
survivability of several motion models for large numbers of
drones. This project tests drone swarms in Virtual Reality (VR),
because it is prohibitively expensive, technically complex, and
potentially dangerous to fly a large swarm of drones in a testing
environment. We model the behavior of drone swarms flying
along parametric paths in both tight and scattered formations.
We add random motion to the general motion plan to confound
path prediction and targeting. We describe an implementation
of these flight paths as game levels in a VR environment. We
then allow players to shoot at the drones and evaluate the
difference between flocking and swarming behavior on drone
survivability.
- Publication Date:
- NSF-PAR ID:
- 10130229
- Journal Name:
- International Conference on Automation Science and Engineering (IEEE CASE 22-26 August 2019, Vancouver, Canada)
- Page Range or eLocation-ID:
- 1718 to 1723
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1109/COASE.2019.8843173
