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In this paper, a density-driven multi-agent swarm control problem is investigated. Robot swarms can provide a great benefit, especially for applications where a single robot cannot effectively achieve a given task. For large spatial-scale applications such as search and rescue, environmental monitoring, and surveillance, a new multi-agent swarm control strategy is necessary because of physical constraints including a robot number and operation time. This paper provides a novel density-driven swarm control strategy for multi-agent systems based on the Optimal Transport theory, to cover a spacious domain with limited resources. In such a scenario, \textit{efficiency} will likely be a key point in achieving an efficient robot swarm behavior rather than uniform coverage that might be infeasible. With the given reference density, pre-constructed from available information, the proposed swarm control method will drive the multi-agent system such that their time-averaged behavior becomes similar to the reference density. In this way, density-driven swarm control will enable the multiple agents to spend most of their time on high-priority regions that are reflected in the reference density, leading to efficiency. To protect the agents from collisions, the Artificial Potential Field method is employed and combined with the proposed density-driven swarm control scheme. Simulations are conducted to validate density-driven swarm control as well as to test collision avoidance. Also, the swarm performance is analyzed by varying the agent number in the simulation.
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Robotic Harvesting of a Moving Swarm Represented by a Markov Process
This paper investigates motion planning for one or more robot(s) that attempt to harvest agents from a moving swarm. Generating motion paths that maximize the number of agents harvested differs from many traditional coverage problems because the agents move. This movement allows previously cleared areas to become recontaminated. We assume that the swarm agents prefer certain regions over others, and that we can represent the swarm by a Markov Process that encodes the agents' preferred regions and their speed of motion. We exploit this model to design and simulate robotic coverage paths that maximize the number of agents harvested by a fleet of robots in a given time budget.
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- PAR ID:
- 10129752
- Date Published:
- Journal Name:
- Reshaping Particle Configurations by Collisions with Rigid Objects
- Page Range / eLocation ID:
- 1157 to 1162
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/COASE.2019.8843164
