Dynamic task allocation in an uncertain environment with heterogeneous multi-agents
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Dynamic task allocation (DTA) is a key feature in collaborative robotics. It affects organizations’ profits and allows agents to perform more tasks when efficiently designed. Although some work has been done on DTA, allocating tasks dynamically in an uncertain environment between heterogeneous multi-agents has rarely been investigated. The solutions proposed so far have inefficiently managed uncertainty, and none of them has utilized the semantics of heterogeneous agents’ capabilities. Studies measuring the performance of these techniques on real robots are also scarce. Therefore, this paper proposes an online DTA method, which introduces new functionalities that can be applied in a real environment. In particular, an uncertain incremental cost function is developed with a distributed semantic negotiation strategy that reflects heterogeneous capabilities without needing to communicate them. The proposed method is tested in a dynamic environment and experiments on heterogeneous real/virtual robots are conducted with different numbers of agents. Different statistical and visualization tools are used to analyze the results, including bar graphs for the waiting time metrics, histograms for the waiting time frequency, scatter plots for the result distribution and variance, and critical difference diagrams for ANOVA–Tukey results. The results indicate that the proposed DTA balances allocation quality and reliability, allowing the agents to serve targets equally without neglecting certain targets at the expense of the total performance. Evidently, updating the cost incrementally allows agents to update their allocation and choose better routes to finish the task earlier. Understanding the capability also gives priority to the capable agents that complete the task faster.
KeywordsDynamic task allocation Heterogeneous multi-agent Ontology Uncertainty theory Robotics
The authors would like to thank Saudi Aramco for funding the research reported in this paper under the “Saudi Aramco Ibn Khaldun Fellowship for Saudi Women” in partnership with the Center for Clean Water and Clean Energy at MIT. Further thanks are given to Katy G Muhlrad for helping in implementing the ontological part of the proposed method.
- Bimba, A. T., Idris, N., Al-Hunaiyyan, A., Mahmud, R. B., Abdelaziz, A., Khan, S., et al. (2016). Towards knowledge modeling and manipulation technologies: A survey. International Journal of Information Management, 36(6, Part A), 857–871. https://doi.org/10.1016/j.ijinfomgt.2016.05.022.CrossRefGoogle Scholar
- Drenjanac, D., Tomic, S. D. K., & Kühn, E. (2015). A semantic framework for modeling adaptive autonomy in task allocation in robotic fleets. In 2015 IEEE 24th international conference on enabling technologies: Infrastructure for collaborative enterprises, 15–17 June 2015 (pp. 15–20). https://doi.org/10.1109/wetice.2015.29.
- Liu, L., & Shell, D. A. (2012). Tackling task allocation uncertainty via a combinatorial method. In 2012 IEEE international symposium on safety, security, and rescue robotics (SSRR), 5–8 Nov. 2012 (pp. 1–6). https://doi.org/10.1109/ssrr.2012.6523871.
- Obitko, M., & Marik, V. (2002). Ontologies for multi-agent systems in manufacturing domain. In Proceedings. 13th international workshop on database and expert systems applications, 2–6 Sept. 2002 (pp. 597–602). https://doi.org/10.1109/dexa.2002.1045963.
- Parker, J. E. (2013). Task allocation for multi-agent systems in dynamic environments. In Paper presented at the Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems, St. Paul, MN, USA.Google Scholar
- Pippin, C., Christensen, H., & Weiss, L. (2013). Performance based task assignment in multi-robot patrolling. In Paper presented at the proceedings of the 28th annual ACM symposium on applied computing, Coimbra, Portugal.Google Scholar
- Research, S. C. f. B. I. (2017). A free, open-source ontology editor and framework for building intelligent systems. https://protege.stanford.edu/.
- Su, H.-H., Su, L., Dornhaus, A., & Lynch, N. (2017). Ant-inspired dynamic task allocation via gossiping. In 5th workshop on biological distributed algorithms (BDA 2017), Washington, DC (in press).Google Scholar
- Tenorth, M. M. (2011). Knowledge processing for autonomous robots. Doctoral dissertation, Technische Universität München.Google Scholar
- UNIHB. (2016). Deliverable D5.2: Technical report/publications on knowledge-base realization. Sherpa: Smart collaboration between humans and ground-aerial robots for improving rescuing activities in Alpine environments.Google Scholar
- Ure, N. K., Omidshafiei, S., Lopez, B. T., Agha-Mohammadi, A. A., How, J. P., & Vian, J. (2015). Online heterogeneous multiagent learning under limited communication with applications to forest fire management. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), Sept. 28 2015–Oct. 2 2015 (pp. 5181–5188). https://doi.org/10.1109/iros.2015.7354107.
- Wicke, D., Freelan, D., & Luke, S. (2015). Bounty hunters and multiagent task allocation. In Paper presented at the proceedings of the 2015 international conference on autonomous agents and multiagent systems, Istanbul, Turkey.Google Scholar