4D-RCS Reference Model Architecture: Difference between revisions

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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]] information, or a combination of the two in order to create a picture or snapshot of the world that is used to upfate 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 1586033700. 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 attemts to compute a path through a multi-dimensional space contained in the real world".<ref name="SBB03"/>

The 4D/RCS is a hieracrchicalhierarchical deliverative 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 tractories known as an [[ego-graph]] as well as as inormation from the real-time sensor processing. The tractories are computed based on a discrete set of possible vehicle velocities and starting steering agles. All of the trajectories are garantueed 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 verhicle status".<ref name="SBB03"/>

== History ==

The National Institute of Standards and Technology’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 (Universitat der Bundeswehr Munchen) [[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 web |url=http://www.isd.mel.nist.gov/documents/albus/4DRCS.pdf |format=PDF|title=4-D/RCS reference model architecture for unmanned ground vehicles}}</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-namic perturbations in the world.<ref name="Albus06"/>

=== 4D/RCS Hierarchy ===

[[File:4D-RCS reference model architecture typical high level block diagram.jpg|thumb|360px|A high level block diagram of a typical 4D/RCS reference model architecture. UAV = Unmanned Air Vehicle, UARV = Unmanned Armed Reconnissance Vehicle, UGS = Unattended Ground Sensors.]]

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.