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Systems of Systems & Integration

Annual Plan

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

Project Team

Principal Investigator

Kira Barton, University of Michigan Lauro Ojeda, University of Michigan


Denise Rizzo, William Smith, U.S. Army GVSC


Frank Koss, Andrew Dallas, SoarTech


Michael Quann, University of Michigan

Project Summary

Work begins in 2019.

Experiment Environment

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.

Prior Related Publications:

  • Quann, M., Ojeda, L., Smith, W., Rizzo, D., Castanier, M., and Barton, K., “Ground Robot Terrain Mapping and Energy Prediction in Environments with 3-D Topography”, 2018 Annual American Control Conference (ACC), Milwaukee, WI, 2018, pp. 3532-3537. doi: 10.23919/ACC.2018.8430767
  • Quann, M., Ojeda, L., Smith, W., Rizzo, D., Castanier, M., and Barton, K., “Probabilistic Terrain-Based Energy Prediction for Multi-Robot Reconnaissance”, to be submitted to IEEE Transactions on Robotics, (in preparation).
  • Quann, M., Ojeda, L., Smith, W., Rizzo, D., Castanier, M., and Barton, K., “An Energy-Efficient Method for Multi-Robot Reconnaissance in an Unknown Environment”, presented at American Control Conference, May 2017.