4D-RCS Reference Model Architecture: Difference between revisions

Revision as of 22:51, 18 August 2009

Revision as of 23:48, 30 August 2009

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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 hierarchical 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 ==

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=== 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 ReconnissanceReconnaissance 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"/>:

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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"/>

=== 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-ing and behavior generation.<ref name="Albus06"/>

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In the center of the figure, each map has a range and resolution that is appropriate for path planning at its level. At each level, there are symbolic data structures and segmented images with labeled regions that describe entities, events, and situations that are relevant to decisions that must be made at that level. On the left is a sensory processing hierarchy that extracts information from the sensory data stream that is needed to keep the world model knowledge database current and accurate.<ref name="Albus02"/>

The bottom (Servo) level has no map representation. The Servo level deals with actuator dynamics and reacts to sensory feedback from actuator sensors. The Primitive level map has range of 5 m with resolution of 4 &nbsp;cm. This enables the vehicle to make small path corrections to avoid bumps and ruts during the 500 ms planning horizon of the Primitive level. The Primitive level also uses accelerometer data to control vehicle dynamics and prevent rollover during high speed driving.<ref name="Albus02"/>

At all levels, 4D/RCS planners are designed to generate new plans well before current plans become obsolete. Thus, action always takes place in the context of a recent plan, and feedback through the executors closes reactive control loops using recently selected control parameters. To meet the demands of dynamic battlefield environments, the 4D/RCS architecture specifies that replanning should occur within about one-tenth of the planning horizon at each level.<ref name="Albus02"/>

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== Criticisms ==

There have been mayor critisismcriticism of this rchitectural form, according to Balakirsky (2003) due to the fact that "the planning is performed on a model of the world rather than on the actual world, and the complexity of the computing large plans... Since the world is not static, and may change during this time delay that occurs between sensing, plan conception, and final execution, the validation of the computed plans have been called into question".<ref name="SBB03"/>

== References ==

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== External links ==

{{commonscat|4D-RCS Reference Model Architecture}}

* [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]]