Robotics Collaborative Technology Alliance: Difference between revisions

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'''Robotics Collaborative Technology Alliance (R-CTA)''' was a research program initiated and sponsored by the [[US Army Research Laboratory]]. The stated purpose of this alliance was to “bring together government, industrial, and academic institutions to address research and development required to enable the deployment of future military unmanned ground vehicle systems ranging in size from man-portables to ground combat vehicles.”<ref name=":0">{{Cite web|url=https://www.arl.army.mil/www/pages/392/RCTA2017-18BPP_013117_R1.1a_signed.pdf|title=ROBOTICS COLLABORATIVE TECHNOLOGY ALLIANCE (RCTA): Proposed 2017-18 Biennial Program Plan|last=|first=|date=|website=|archive-url=|archive-date=|dead-url=|access-date=}}</ref>

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* Meta-cognition – enabling the use of intuitive, human-level commands for soldier-robot communication, and creating shared mental models and situation awareness.

* Machine learning – leveraging new learning techniques to achieve better and faster training of perception and planning algorithms.

* Hybrid cognitive/metric world model – spanning the range from traditional metric data to associated semantic understanding to support a cognitive level of reasoning.

== Participants ==

The research under this program was performed collaboratively by scientists of the US Army Research Laboratory and by scientists and engineers of the following institutions:<ref name=":0" />

* Army Research Lab (ARL)

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* QinetiQ North America (QNA)

* University of Central Florida (UCF)

* University of Pennsylvania (UPenn)

== Results ==

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* Distributed solutions for efficiently allocating a set of complex tasks to a robot team, by giving individual robots the ability to come up with new ways to perform a task, or by allowing multiple robots to cooperate by sharing the subcomponents of a task, or both.<ref>{{Cite web|url=https://ieeexplore.ieee.org/abstract/document/1570329/|title=Complex Task Allocation For Multiple Robots - IEEE Conference Publication|website=ieeexplore.ieee.org|language=en-US|access-date=2018-09-05}}</ref>

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* A multirobot coordination approach that ensures robustness and promotes graceful degradation in team performance when faced with malfunctions, including communication failures, partial robot malfunction, or robot death.<ref>{{Cite web|url=https://pdfs.semanticscholar.org/41d3/8b66097032b6f02b4f800090fa1988aa9ce0.pdf|title=The Geometric Path Planner for Navigating Unmanned Vehicles in Dynamic Environments|last=|first=|date=|website=|archive-url=|archive-date=|dead-url=|access-date=}}</ref>

Environments|last=|first=|date=|website=|archive-url=|archive-date=|dead-url=|access-date=}}</ref>

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== References ==

{{Reflist}}

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