Human-Autonomy Interaction
Annual PlanOptimal Distribution of Tasks in Human-Autonomy Teams
Project Team
Government
Victor Paul, Jon Smereka, John Brabbs, U.S. Army GVSC
Charne Folks, Aberdeen Test Center
Industry
Mert Egilmez, Veoneer-Nissin
Student
Haochen Wu, University of Michigan
Project Summary
Project #2.13 started Sep. 2019 and was completed in Q2 2023.
In a team, autonomous assets as well as humans have inherent limitations in distinct mission-related attributes. For example, autonomous assets are limited in terms of real-time computational power, amount and accuracy of sensor data and accuracy of decision-making capabilities of artificial intelligence methods. At the same time, humans are limited in terms of cognitive loads, fatigue and reaction time.
This research aims to answer fundamental research questions including:
- What is the best way to distribute synergistically the tasks in a complex mission among humans and autonomous assets to maximize mission effectiveness?
- How do the unique benefits and limitations of human and autonomy affect the solution approach and performance?
- What is the best investment strategy to improve the overall performance?
The main goal of this research is to create methods for coordination, negotiation, sharing and balancing of tasks in a given team size and composition, while accounting for limitations and advantages of humans and autonomous assets. The effort contributes to real-time decentralized control and coordination strategies that allows dynamic allocation of tasks among humans and autonomous assets based on environmental uncertainty and adversarial actions.
Other Publications:
- Haochen Wu, Charne C. Folks, A. Emrah Bayrak, Jonathon M. Smereka, Bogdan I. Epureanu, “Human-Autonomy Teaming in Immersive Environments”, Interservice/Industry Training, Simulation and Education Conference (I/ITSEC), 2022.
2.13
Publications:
H. Wu, A. Ghadami, A. E. Bayrak, J. M. Smereka and B. I. Epureanu, “Impact of Heterogeneity and Risk Aversion on Task Allocation in Multi-Agent Teams,” in IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7065-7072, Oct. 2021, doi: 10.1109/LRA.2021.3097259.
Wu, H., Ghadami, A., Bayrak, A. E., Smereka, J. M., & Epureanu, B. I. (2021). Impact of heterogeneity and risk aversion on task allocation in multi-agent teams. IEEE Robotics and Automation Letters, 6(4), 7065-7072.
Wu, H., Ghadami, A., Bayrak, A. E., Smereka, J. M., & Epureanu, B. I. (2022, May). Task allocation with load management in multi-agent teams. In 2022 International Conference on Robotics and Automation (ICRA) (pp. 8823-8830). IEEE.