4D-RCS Reference Model Architecture: Difference between revisions

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{{Short description|Reference model for military unmanned vehicles to identify and organize their software components}}

[[File:4D-RCS reference model architecture for an individual vehicle.jpg|thumb|420px|4D-RCS reference model architecture for an individual vehicle. It contains many layers of computational nodes each containing elements of sensory processing, world modeling, value judgment, and behavior generation.]]

The '''4D/RCS Reference Model Architecture''' is a [[reference model]] for military [[unmanned vehicle]]s on how their [[software]] components should be identified and organized.

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The 4D/RCS has been developed by the Intelligent Systems Division (ISD) of the [[National Institute of Standards and Technology]] (NIST) since the 1980s.<ref>Danil Prokhorov (2008) ''Computational Intelligence in Automotive Applications''. p. 315</ref>

This reference model is based on the general [[Real-time Control System]] (RCS) Reference Model Architecture, and has been applied to many kinds of robot control, including autonomous vehicle control.<ref name="Albus06">Albus, J.S. et al. (2006). "[https://www.nist.gov/cgi-bin//get_pdf.cgi?pub_id=822702 Learning in a Hierarchical Control System: 4D/RCS in the DARPA LAGR Program]{{Dead link|date=July 2023 |bot=InternetArchiveBot |fix-attempted=yes }}". NIST June 26, 2006. in: ''ICINCO 06 - International Conference in Control, Automation and Robotics, Setubal, Portugal, August 2006''</ref>

==Overview==

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According to Balakirsky (2003) 4D/RCS is an example of deliberative [[agent architecture]]. These architectures "include all systems that plan to meet future goal or deadline. In general, these systems plan on a model of the world rather than planning directly on processed sensor output. This may be accomplished by real-time [[sensor]]s, [[A priori and a posteriori|a priori]] information, or a combination of the two in order to create a picture or snapshot of the world that is used to update a world model".<ref name="SBB03">S.B. Balakirsky (2003). ''A framework for planning with incrementally created graphs in attributed problem spaces''. IOS Press. {{ISBN|1-58603-370-0}}. p.10-11.</ref> The course of action of a deliberative agent architecture is based on the world model and the commanded mission goal, see image. This goal "may be a given system state or physical ___location. To meet the goal systems of this kind attempts to compute a path through a multi-dimensional space contained in the real world".<ref name="SBB03"/>

The 4D/RCS is a hierarchical deliverativedeliberative architecture, that "plans up to the [[subsystem]] level to compute plans for an [[autonomous vehicle]] driving over rough terrain. In this system, the world model contains a pre-computed dictionary of possible vehicle trajectories known as an [[ego-graph]] as well as information from the real-time sensor processing. The trajectories are computed based on a discrete set of possible vehicle velocities and starting steering aglesangles. All of the trajectories are guaranteed to be dynamically correct for the given velocity and steering angle. The systems runs under a fixed planning cycle, with the sensed information being updated into the world model at the beginning of the cycle. These update information include information on what area is currently under observation by the sensors, where detected obstacles exist, and vehicle status".<ref name="SBB03"/>

==History==

The National Institute of Standards and Technology’sTechnology's (NIST) Intelligent Systems Division (ISD) has been developing the [[RCS reference model architecture]] for over 30 years. 4D/RCS is the most recent version of RCS developed for the Army Research Lab Experimental Unmanned Ground Vehicle program. The 4D in 4D/RCS signifies adding time as another dimension to each level of the three-dimensional (sensor processing, world modeling, behavior generation), hierarchical control structure. ISD has studied the use of 4D/RCS in defense mobility, transportation, robot cranes, manufacturing, and several other applications.<ref name="Albus06"/>

4D/RCS integrates the NIST Real-time Control System (RCS) architecture with the German ([[Bundeswehr University of Munich]]) [[Ernst Dickmanns|VaMoRs 4-D approach]] to dynamic machine vision. It incorporates many concepts developed under the U.S. Department of Defense Demo I, Demo II, and Demo III programs, which demonstrated increasing levels of robotic vehicle autonomy. The theory embodied in 4D/RCS borrows heavily from cognitive psychology, semiotics, neuroscience, and artificial intelligence.<ref name="Albus02">Albus et al. (2002). ''4D-RCS A Reference Model Architecture For Unmanned Vehicle Systems Version 2.0''. National Institute of Standards and Technology, Gaithersburg, Maryland 20899Aug 2002.</ref>

Three [[United States Government|US Government]] funded military efforts known as Demo I (US Army), Demo II (DARPA), and Demo III ([[US Army]]), are currently underway. Demo III (2001)<ref>{{cite conference|url=http://www.isd.mel.nist.gov/documents/albus/4DRCS.pdf |format=PDF |title=4-D/RCS reference model architecture for unmanned ground vehicles |first=J.A. |last=Albus |booktitlebook-title=Proc. of Symposium on Aerospace/Defense Sensing, Simulation and Controls |___location=Orlando, FL |year=2002 |deadurlurl-status=yesdead |archiveurlarchive-url=https://web.archive.org/web/20040725051856/http://www.isd.mel.nist.gov/documents/albus/4DRCS.pdf |archivedatearchive-date=2004-07-25 }}</ref> demonstrated the ability of unmanned ground vehicles to navigate miles of difficult off-road terrain, avoiding obstacles such as rocks and trees. [[James Albus]] at [[NIST]] provided the [[Real-time Control System]] which is a [[hierarchical control system]]. Not only were individual vehicles controlled (e.g. throttle, steering, and brake), but groups of vehicles had their movements automatically coordinated in response to high level goals.

In 2002, the [[DARPA Grand Challenge]] competitions were announced. The [[DARPA Grand Challenge (2005)|2004]] and [[DARPA Grand Challenge (2005)|2005 DARPA competitions]] allowed international teams to compete in fully autonomous vehicle races over rough unpaved terrain and in a non-populated suburban setting. The [[DARPA Grand Challenge (2007)|2007 DARPA challenge]], the DARPA urban challenge, involved autonomous cars driving in an urban setting.

==4D/RCS Building blocks==

The 4D/RCS architecture is characterized by a generic control node at all the [[Hierarchical routing|hierarchical control]] levels. The 4D/RCS hierarchical levels are scalable to facilitate systems of any degree of complexity. Each node within the hierarchy functions as a goal-driven, model-based, [[closed-loop controller]]. Each node is capable of accepting and decomposing task commands with goals into actions that accomplish task goals despite unexpected conditions and dy-namicdynamic perturbations in the world.<ref name="Albus06"/>

===4D/RCS Hierarchy===

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===4D/RCS control loop===

[[File:4D-RCS control loop basic internal structure.jpg|thumb|360px|4D-RCS control loop basic internal structure.]]

At the heart of the control loop through each node is the world model, which provides the node with an internal model of the external world. The world model provides a site for data fusion, acts as a buffer between perception and behavior, and supports both sensory process-ingprocessing and behavior generation.<ref name="Albus06"/>

A high level diagram of the internal structure of the world model and value judgment system is shown in the figure. Within the knowledge database, iconic information (images and maps) is linked to each other and to symbolic information (entities and events). Situations and relationships between entities, events, images, and maps are represented by pointers. Pointers that link symbolic data struc-turesstructures to each other form syntactic, semantic, causal, and situational networks. Pointers that link symbolic data structures to regions in images and maps provide symbol grounding and enable the world model to project its understanding of reality onto the physical world.<ref name="Albus06"/>

A high level diagram of the internal structure of the world model and value judgment system is shown in the figure. Within the knowledge database, iconic information (images and maps) is linked to each other and to symbolic information (entities and events). Situations and relationships between entities, events, images, and maps are represented by pointers. Pointers that link symbolic data struc-tures to each other form syntactic, semantic, causal, and situational networks. Pointers that link symbolic data structures to regions in images and maps provide symbol grounding and enable the world model to project its understanding of reality onto the physical world.<ref name="Albus06"/>

Sensory processing performs the functions of windowing, grouping, computation, estimation, and classification on input from sensors. World modeling maintains knowledge in the form of images, maps, entities, and events with states, attributes, and values. Relationships between images, maps, entities, and events are defined by pointers. These relationships include class membership, ontologies, situations, and inheritance. Value judgment provides criteria for decision making. Behavior generation is responsible for planning and execution of behaviors.<ref name="Albus02"/>

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# sensory processing, and

# value judgment.

There is also a [[knowledge base|knowledge database]] that represents the node’snode's best estimate of the state of the world at the

range and resolution that are appropriate for the behavioral decisions that are the responsibility of that node.

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==External links==

{{commons category|4D-RCS Reference Model Architecture}}

* [https://web.archive.org/web/20091010082639/http://www.isd.mel.nist.gov/projects/rcs/ RCS The Real-time Control Systems Architecture] NIST Homepage

{{DEFAULTSORT:4d-Rcs Reference Model Architecture}}

[[Category:Control theory]]

[[Category:Industrial computing]]

[[Category:UnmannedUncrewed vehicles]]