MACE is a multi-vehicle, collaborative control framework built to operate under real-world conditions and restraints. The framework links communications, control, automation, and human-machine interface components of a practical multi-vehicle system into a deployable package. MACE establishes the data management, scheduling, and monitoring required by a multi-vehicle system. Designed as a modular software architecture, MACE implements a core collaborative engine that exposes interfaces with other system components via APIs. This approach abstracts the details of the collaboration away from an individual agent, allowing for rapid integration of third party components.


  • A lightweight robotic hardware/software architecture enabling cooperative communications and planning operations between autonomous systems

  • Designed to support open-integration and research into existing technology bases in core areas of robotics (e.g. communication, planning, sensing, and tasking)

  • MACE is agnostic to specific vehicle architectures and implementations–lightweight APIs allow for easy and intuitive third party integrations

  • Designed to deploy on a COTS companion computer that allows for “plug-and-play” swarm capabilities. With a companion computer mounted on-board, an unmanned system can take advantage of larger swarm capabilities such as intelligent resource and task allocation and increased situational awareness.

  • Custom HMI designed for monitoring multiple, heterogeneous unmanned systems in an intuitive manner

Ongoing Research

  • MACE has been used to demonstrate novel Resource and Task Allocation algorithms applied to multi-agent surveying and search scenarios
    • Using a balanced partitioning algorithm to break up a space into equal areas, we can use multiple vehicles to cover a space faster and concentrate sensor measurements over areas of interest

Publications & Outreach

  • Publications:
    • Nolan, Patrick, Derek A. Paley, and Kenneth Kroeger. “Multi-UAS path planning for non-uniform data collection in precision agriculture.” Aerospace Conference, 2017 IEEE. IEEE, 2017. http://ieeexplore.ieee.org/document/7943794/