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In this paper, a distributed swarm control problem is studied for large-scale multi-agent systems (LS-MASs). Different than classical multi-agent systems, an LS-MAS brings new challenges to control design due to its large number of agents. It might be more difficult for developing the appropriate control to achieve complicated missions such as collective swarming. To address these challenges, a novel mixed game theory is developed with a hierarchical learning algorithm. In the mixed game, the LS-MAS is represented as a multi-group, large-scale leader–follower system. Then, a cooperative game is used to formulate the distributed swarm control for multi-group leaders, and a Stackelberg game is utilized to couple the leaders and their large-scale followers effectively. Using the interaction between leaders and followers, the mean field game is used to continue the collective swarm behavior from leaders to followers smoothly without raising the computational complexity or communication traffic. Moreover, a hierarchical learning algorithm is designed to learn the intelligent optimal distributed swarm control for multi-group leader–follower systems. Specifically, a multi-agent actor–critic algorithm is developed for obtaining the distributed optimal swarm control for multi-group leaders first. Furthermore, an actor–critic–mass method is designed to find the decentralized swarm control for large-scale followers. Eventually, a series of numerical simulations and a Lyapunov stability proof of the closed-loop system are conducted to demonstrate the performance of the developed scheme.
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Density-Driven Multi-Agent Swarm Control with Potential Field Based Collision Avoidance
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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- Award ID(s):
- 2145810
- PAR ID:
- 10508975
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-0413-8
- Page Range / eLocation ID:
- 0714 to 0721
- Format(s):
- Medium: X
- Location:
- New York, NY, USA
- Sponsoring Org:
- National Science Foundation
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