Due to the increasing complexity of robot swarm algorithms, ana-
lyzing their performance theoretically is often very difficult. Instead,
simulators are often used to benchmark the performance of robot
swarm algorithms. However, we are not aware of simulators that
take advantage of the naturally highly parallel nature of distributed
robot swarms. This paper presents ParSwarm, a parallel C++ frame-
work for simulating robot swarms at scale on multicore machines.
We demonstrate the power of ParSwarm by implementing two
applications, task allocation and density estimation, and running
simulations on large numbers of agents.
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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
- 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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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frobt.2021.640446
