Resilient Teaming: Fleet Organization and Decision Making in Heterogeneous Vehicle Teams to Meet Energy Requirements in Restricted and Unknown Environments

Work began in 2019 and was completed in 2022.

As autonomy becomes more pervasive, the capabilities of different vehicles may become more pronounced as resources are designed to meet specific mission needs, while minimizing energy requirements. The specialization of vehicle capabilities to minimize energy losses and maximize performance introduces an important research problem: How does the configuration of heterogeneous vehicle teams change as autonomy becomes more pervasive in uncertain environments?

Methods to define the unique requirements of the fleet across a mixture of vehicles must be developed. Once the make-up of the team has been determined, a dynamic understanding of the team’s capacity to make real-time decisions based on the team’s current state, environmental conditions, and uncertainties must be derived. This work aims to address these needs through resilient teaming: a fleet organization and decision-making strategy for designing heterogeneous teams to meet these objectives.

This work builds on the investigators’ previous work in single objective decision making and energy mapping for homogeneous vehicles in uncertain environments.

Publications:

• Fu, B., Smith, W., Rizzo, D. M., Castanier, M., Ghaffari, M., & Barton, K. (2023). Robust task scheduling for heterogeneous robot teams under capability uncertainty. IEEE Transactions on Robotics, 39(2), 1087-1105. doi:10.1109/TRO.2022.3216068
• Fu, B., Kathuria, T., Rizzo, D., Castanier, M., Yang, X. J., Ghaffari, M., & Barton, K. (2021). Simultaneous Human-Robot Matching and Routing for Multi-Robot Tour Guiding Under Time Uncertainty. Journal of Autonomous Vehicles and Systems, 1(4), 041005.
• Fu, B., Smith, W., Rizzo, D., Castanier, M., & Barton, K. (2020). Heterogeneous vehicle routing and teaming with Gaussian distributed energy uncertainty. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 4315-4322.
• Quann, M., Ojeda, L., Smith, W., Rizzo, D., Castanier, M., & Barton, K. (2020). Power prediction for heterogeneous ground robots through spatial mapping and sharing of terrain data. IEEE Robotics and Automation Letters, 5(2), 1579-1586.
• Quann, M., Ojeda, L., Smith, W., Rizzo, D., Castanier, M., & Barton, K. (2020). Off-road ground robot path energy cost prediction through probabilistic spatial mapping. Journal of Field Robotics, 37(3), 421-439.
• Quann, M., Ojeda, L., Smith, W., Rizzo, D., Castanier, M., & Barton, K. (2019). Chance constrained reachability in environments with spatially varying energy costs. Robotics and Autonomous Systems, 119, 1-12.