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Conference proceeding
Distributed Autonomous Swarm Formation for Dynamic Network Bridging
IEEE Conference on Computer Communications workshops (Online), pp.1-6
IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (Vancouver, BC, Canada, 05/20/2024)
05/20/2024
Web of Science ID: WOS:001300418400074
Abstract
Effective operation and seamless cooperation of robotic systems are a fundamental component of next-generation technologies and applications. In contexts such as disaster response, swarm operations require coordinated behavior and mobility control to be handled in a distributed manner, with the quality of the agents' actions heavily relying on the communication between them and the underlying network. In this paper, we formulate the problem of dynamic network bridging in a novel Decentralized Partially Observable Markov Decision Process (Dec-POMDP), where a swarm of agents cooperates to form a link between two distant moving targets. Furthermore, we propose a Multi-Agent Reinforcement Learning (MARL) approach for the problem based on Graph Convolutional Reinforcement Learning (DGN) which naturally applies to the networked, distributed nature of the task. The proposed method is evaluated in a simulated environment and compared to a centralized heuristic baseline showing promising results. Moreover, a further step in the direction of sim-to-real transfer is presented, by additionally evaluating the proposed approach in a near Live Virtual Constructive (LVC) UAV framework.
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Details
- Title
- Distributed Autonomous Swarm Formation for Dynamic Network Bridging
- Publication Details
- IEEE Conference on Computer Communications workshops (Online), pp.1-6
- Resource Type
- Conference proceeding
- Conference
- IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (Vancouver, BC, Canada, 05/20/2024)
- Publisher
- IEEE; 10.1109/INFOCOMWKSHPS61880.2024
- Number of pages
- 6
- Copyright
- © 2024, IEEE
- Identifiers
- WOS:001300418400074; 99381512042806600
- Academic Unit
- Institute for Human and Machine Cognition; Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English