A swarm of unmanned aerial vehicles (UAVs) can be used for many applications, including disaster relief, search and rescue, and establishing communication networks, due to its mobility, scalability, and robustness to failure. However, a UAV swarm’s performance is typically limited by each agent’s stored energy. Recent works have considered the usage of thermals, or vertical updrafts of warm air, to address this issue. One challenge lies in a swarm of UAVs detecting and taking advantage of these thermals. Inspired by hawks, a swarm could take advantage of thermals better than individuals due to the swarm’s distributed sensing abilities. To determine which emergent behaviors increase survival time, simulation software was created to test the behavioral models of UAV gliders around thermals. For simplicity and robustness, agents operate with limited information about other agents. The UAVs’ motion was implemented as a Boids model, replicating the behavior of flocking birds through cohesion, separation, and alignment forces. Agents equipped with a modified behavioral model exhibit dynamic flocking behavior, including relative ascension-based cohesion and relative height-based separation and alignment. The simulation results show the agents flocking to thermals and improving swarm survival. These findings present a promising method to extend the flight time of autonomous UAV swarms.
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Aerial Swarm Defense by StringNet Herding: Theory and Experiments
This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed a closed formation (“StringNet”) of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The adversarial agents were assumed to remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders toward the attackers. We use a “Density-based Spatial Clustering of Application with Noise (DBSCAN)” algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. We also provide conditions under which defenders can successfully herd all the attackers. The efficacy of the approach is demonstrated via computer simulations, as well as hardware experiments with a fleet of quadrotors.
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- Award ID(s):
- 1738714
- NSF-PAR ID:
- 10291984
- Date Published:
- Journal Name:
- Frontiers in Robotics and AI
- Volume:
- 8
- ISSN:
- 2296-9144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Three prolific earthquake swarms and numerous smaller ones have occurred since 1980 in the Mesozoic igneous plutonic rocks of the Perris block of the Peninsular Ranges, Southern California. The major swarms occurred in 1980–1981, 1983–1984, and 2016–2018, with the latest swarm still ongoing. These swarms have no clear mainshock, with the largest events of
M L 3.6,M L 3.7, andM w 4.4. Each successive swarm had larger cumulative seismic moment release with about 314 and 411 events ofM ≥ 1.5, while the third swarm has produced about 451 events ofM ≥ 1.5 (as of September 2018). The concurrent strike‐slip faulting occurred on north to northwest striking planes but with no orthogonal northeast trending seismicity alignments. These shallow swarms are probably driven by intrablock Pacific‐North America plate boundary stress loading of the two bounding major late Quaternary strike‐slip faults, the Elsinore and San Jacinto faults. The state of stress within the Cahuilla Valley pluton has a ~40° angle between the maximum principal stress and the average trend of the swarms, suggesting that migrating pore fluid pressures aid in the formation and growth of zones of weakness. These swarms, which last more than 600 days each, exhibit clear bilateral spatial migration for distances of up to ~7–8 km and reach their full length in about 20 months. The slow spatial‐temporal development of the swarms corresponds to a fluid diffusivity of 0.006 to 0.01 m2/s, consistent with very low permeability rocks as expected for this block. There is no geodetic or other evidence for a slow slip event driving the swarms.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frobt.2021.640446
