Adaptive Vehicle Make

From Wikiversity
Jump to: navigation, search

Adaptive Vehicle Make (AVM) is a portfolio of programs that address revolutionary approaches to the design, verification, and manufacturing of complex defense systems and vehicles. It consists of three primary programs: META, Instant Foundry Adaptive through Bits (iFAB) and Fast Adaptable Next-Generation Ground Combat Vehicle (FANG). The FANG program encompasses, three AVM Prize Challenges, and the Manufacturing Experimentation and Outreach (MENTOR) effort.


The ultimate goal of the META program is to make a dramatic improvement on the existing systems engineering, integration, and testing process for defense systems. META is not predicated on one particular alternative approach, metric, technique, or tool. Broadly speaking, however, it aims to develop model-based design methods for cyber-physical systems far more complex and heterogeneous than those to which such methods are applied today; to combine these methods with a rigorous deployment of hierarchical abstractions throughout the system architecture; to optimize system design with respect to an observable, quantitative measure of complexity for the entire cyber-physical systems; and to apply probabilistic formal methods to the system verification problem, thereby dramatically reducing the need for expensive real-world testing and design iteration.

The top-level technical objectives of the META program are as follows:

  • Develop a practical, observable metric of complexity for cyber-physical systems to enable cyber-vs-physical implementation trades and to improve parametrization of cost and schedule;
  • Develop a quantitative metric of adaptability associated with a given system architecture that can support trade-offs between adaptability, complexity, performance, cost, schedule, risk, and other system attributes;
  • Develop a structured design flow employing hierarchical abstraction and model-based composition of electromechanical and software components;
  • Develop a component and manufacturing model library for a given airborne or ground vehicle systems domain through extensive characterization of desirable and spurious interactions, dynamics, and properties of all constituent components down to the numbered part level; develop context models to reflect various operational environments;
  • Develop a verification flow that generates probabilistic "certificates of correctness" for the entire cyber-physical system based on stochastic formal methods, scaling linearly with problem size;
  • Apply the above framework and toolset to design, manufacture, integrate, and verify an air and/or ground vehicle of substantial complexity 5X faster than with a conventional design/build/test approach.

Concept image


The Instant Foundry Adaptive through Bits (iFAB) program looks to lay the groundwork for the development of a foundry-style manufacturing capability—taking as input a verified system design specified in an appropriate metalanguage—capable of rapid reconfiguration to accommodate a wide range of design variability and specifically targeted at the fabrication of military ground vehicles. The principal objective of iFAB—coupled with META—is to enable substantial compression of the time required to go from idea to product through a shift in the product value chain for defense systems from "little m=" manufacturing (i.e., fabrication) to the other elements of "big M" Manufacturing (i.e., design, customization, after-market support, etc.). The iFAB vision is to move away from wrapping a capital-intensive manufacturing facility around a single defense product, and toward the creation of a flexible, programmable, potentially distributed production capability capable of accommodating a wide range of systems and system variants with extremely rapid reconfiguration timescales. The specific goals of the iFAB program are to rapidly design and configure manufacturing capabilities to support the fabrication of a wide array of infantry fighting vehicle models and variants.

Concept image


The Fast Adaptable Next-Generation Ground Combat Vehicle (FANG) program seeks to develop the infrastructure for and conduct a series of design challenges intended to precipitate open source design for a prototype of a next-generation infantry fighting vehicle analogous to the Army’s Ground Combat Vehicle (GCV).

The effort is focused on generating an open source development collaboration environment and website for the creation of large, complex, cyber-electro-mechanical systems by numerous unaffiliated designers—with the goal of democratizing the design innovation process by engaging several orders of magnitude more talent than the current industry model. The initial phase of the effort will last 12 months and culminate in the operational deployment of The development of complex software systems has benefitted significantly from the ability to leverage crowd-sourced innovation in the form of open source code development. aims to significantly expand open source collaborative development for defense systems by employing a general representation language—being developed under the META program—that is rich enough to describe a broad range of cyber-electro-mechanical systems, yet formal enough that the system can be “compiled” or verified in some manner when a design change is made to some element or aspect of it.

The FANG program will seek to exercise META, iFAB, and capabilities in a series of design challenges of increasing complexity, seeking to leverage fab-less design, foundry-style manufacturing, and a crowd-sourced innovation model—and culminating in a complete design and fabrication of an infantry fighting vehicle in the span of one year.

The Manufacturing Experimentation and Outreach (MENTOR) effort is focused on engaging high school-age students in a series of collaborative design and distributed manufacturing experiments. DARPA envisions deploying up to a thousand computer-numerically-controlled (CNC) additive manufacturing machines—more commonly known as "3D printers"—to high schools nationwide. The goal is to engage students across clusters of schools to collaborate via social networking media to jointly design and build systems of moderate complexity, such as mobile robots, go carts, etc., in response to prize challenges.