4D-RCS Reference Model Architecture: Difference between revisions

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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.

4D/RCS has been developed by the [[National Institute of Standards and Technology]] (NIST). It 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). "[http://www.nist.gov/cgi-bin//get_pdf.cgi?pub_id=822702 Learning in a Hierarchical Control System: 4D/RCS in the DARPA LAGR Program]". NIST June 26, 2006. in: ''ICINCO 06 - International Conference in Control, Automation and Robotics, Setubal, Portugal, August 2006''</ref>.

== Overview ==

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== 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-namic perturbations in the world.<ref name="Albus06"/>

=== 4D/RCS Hierarchy ===

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4D/RCS prescribes a hierarchical control principle that decomposed high level commands into actions that employ physical actuators and sensors. The figure for example shows a high level block diagram of a 4D/RCS reference model architecture for a notional [[Future Combat System]] (FCS) battalion. Commands flow down the hierarchy, and status feedback and sensory information flows up. Large amounts of communication may occur between nodes at the same level, particularly within the same subtree of the command tree<ref name="Albus02"/>:

* At the ''Servo level'' : Commands to actuator groups are decomposed into control signals to individual actuators.

* At the ''Primitive level'' : Multiple actuator groups are coordinated and dynamical interactions between actuator groups are taken into account.

* At the ''Subsystem level'' : All the components within an entire subsystem are coordinated, and planning takes into consideration issues such as obstacle avoidance and gaze control.

* At the ''Vehicle level'' : All the subsystems within an entire vehicle are coordinated to generate tactical behaviors.

* At the ''Section level'' : Multiple vehicles are coordinated to generate joint tactical behaviors.

* At the ''Platoon level'' : Multiple sections containing a total of 10 or more vehicles of different types are coordinated to generate platoon tactics.

* At the ''Company level'' : Multiple platoons containing a total of 40 or more vehicles of different types are coordinated to generate company tactics.

* At the ''Battalion level'' : Multiple companies containing a total of 160 or more vehicles of different types are coordinated to generate battalion tactics.

At all levels, task commands are decomposed into jobs for lower level units and coordinated schedules for subordinates are generated. At all levels, communication between peers enables coordinated actions. At all levels, feedback from lower levels is used to cycle subtasks and to compensate for deviations from the planned situations.<ref name="Albus02"/>

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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"/>

=== Computional nodes ===

[[File:RCS NODE Internal structure.jpg|thumb|360px|RCS NODE Internal structure.]]

The 4D/RCS nodes have internal structure such as shown in the figure. Within each node there typically are four functional elements or processes<ref name="Albus02"/> :

# behavior generation,

# world modeling,

# sensory processing, and

# value judgment.

There is also a [[knowledge base|knowledge database]] that represents the node’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 ==

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

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