MAD-VR: machine learning, analysis, and design in virtual reality

J. Rubini

doi:10.1117/12.2305020

3 May 2018 MAD-VR: machine learning, analysis, and design in virtual reality

J. Rubini

Proceedings Volume 10640, Unmanned Systems Technology XX; 1064008 (2018) https://doi.org/10.1117/12.2305020
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

As the modern battlespace continues to evolve, reliance on relatively few, dominant weapon systems is rapidly becoming infeasible. New weapon systems must be able to communicate and coordinate with other actors in the mission arena to achieve warfighter objectives. This is creating an explosion of complexity and data/information processing burden that hampers the warfighter’s ability to effectively operate. This emerging complexity is in turn driving the need for sophisticated autonomous and semi-autonomous systems, as well as adaptive real-time filters and decision-assist mechanisms.

Machine learning, Analysis, and Design in Virtual Reality (MAD-VR) is a tool to facilitate the design, proof-of-concept, and initial testing of algorithms for autonomy, information fusion, and machine learning. Designed as a next-generation front-end for high-speed simulations, it specifically addresses the need for a high-level, system-of-systems environment within which to evaluate the battlespace impact of these critical algorithms. Engineers and technicians will be able to observe the execution of new control systems, sensor data, and decision support systems in a high-fidelity simulation incorporating a diverse breadth of weapons and platforms, all working together to achieve mission success. Additionally, the warfighter will be able to use this tool directly to help with mission planning and optimization, as a means of examining outcomes in a Monte Carlo style of analysis.

Citation Download Citation

J. Rubini "MAD-VR: machine learning, analysis, and design in virtual reality", Proc. SPIE 10640, Unmanned Systems Technology XX, 1064008 (3 May 2018); https://doi.org/10.1117/12.2305020

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