Robotic systems, working together as a team, are becoming valuable players in different real-world applications, from disaster response to warehouse fulfillment services. Centralized solutions to coordinating multi-robot teams often suffer from poor scalability and vulnerability to communication disruptions. This paper develops a decentralized multi-agent task allocation (Dec-MATA) algorithm for multi-robot applications. The task planning problem is posed as a maximum-weighted matching of a bipartite graph, the solution of which using the blossom algorithm allows each robot to autonomously identify the optimal sequence of tasks it should undertake. The graph weights are determined based on a soft clustering process, which also plays a problem decomposition role seeking to reduce the complexity of the individual-agents’ task assignment problems. To evaluate the new Dec-MATA algorithm, a series of case studies (of varying complexity) are performed, with tasks being distributed randomly over an observable 2D environment. A centralized approach, based on a state-of-the-art MILP formulation of the multi-Traveling Salesman problem is used for comparative analysis. While getting within 7–28% of the optimal cost obtained by the centralized algorithm, the Dec-MATA algorithm is found to be 1–3 orders of magnitude faster and minimally sensitive to task-to-robot ratios unlike the centralized algorithm.
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ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 26–29, 2018
Quebec City, Quebec, Canada
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-5175-3
PROCEEDINGS PAPER
Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented With Fuzzy Clustering
Payam Ghassemi,
Payam Ghassemi
University at Buffalo, Buffalo, NY
Search for other works by this author on:
Souma Chowdhury
Souma Chowdhury
University at Buffalo, Buffalo, NY
Search for other works by this author on:
Payam Ghassemi
University at Buffalo, Buffalo, NY
Souma Chowdhury
University at Buffalo, Buffalo, NY
Paper No:
DETC2018-86161, V02AT03A014; 11 pages
Published Online:
November 2, 2018
Citation
Ghassemi, P, & Chowdhury, S. "Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented With Fuzzy Clustering." Proceedings of the ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2A: 44th Design Automation Conference. Quebec City, Quebec, Canada. August 26–29, 2018. V02AT03A014. ASME. https://doi.org/10.1115/DETC2018-86161
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