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Real-time Control System
On March 6, , Discovery Communications completed its acquisition of Scripps Networks Interactive, changing its name to Discovery and creating the new global leader in real life entertainment across all screens. Watch video. Read more. Watch Video. Back to our beginning Continue to connect with Discovery Communications online:. RCS prescribes a hierarchical control model based on a set of well-founded engineering principles to organize system complexity.
All the control nodes at all levels share a generic node model. Also RCS provides a comprehensive methodology for designing, engineering, integrating, and testing control systems. Architects iteratively partition system tasks and information into finer, finite subsets that are controllable and efficient. RCS focuses on intelligent control that adapts to uncertain and unstructured operating environments.
The key concerns are sensing, perception, knowledge, costs, learning, planning, and execution. A reference model architecture is a canonical form, not a system design specification. The RCS reference model architecture combines real-time motion planning and control with high level task planning, problem solving , world modeling , recursive state estimation, tactile and visual image processing , and acoustic signature analysis. In fact, the evolution of the RCS concept has been driven by an effort to include the best properties and capabilities of most, if not all, the intelligent control systems currently known in the literature, from subsumption to SOAR, from blackboards to object-oriented programming.
RCS real-time control system is developed into an intelligent agent architecture designed to enable any level of intelligent behavior, up to and including human levels of performance. RCS was inspired by a theoretical model of the cerebellum, the portion of the brain responsible for fine motor coordination and control of conscious motions.
It was originally designed for sensory-interactive goal-directed control of laboratory manipulators. Over three decades, it has evolved into a real-time control architecture for intelligent machine tools, factory automation systems, and intelligent autonomous vehicles.
RCS applies to many problem domains including manufacturing examples and vehicle systems examples. Systems based on the RCS architecture have been designed and implemented to varying degrees for a wide variety of applications that include loading and unloading of parts and tools in machine tools, controlling machining workstations, performing robotic deburring and chamfering, and controlling space station telerobots, multiple autonomous undersea vehicles, unmanned land vehicles, coal mining automation systems, postal service mail handling systems, and submarine operational automation systems.
RCS has evolved through a variety of versions over a number of years as understanding of the complexity and sophistication of intelligent behavior has increased. The first implementation was designed for sensory-interactive robotics by Barbera in the mid s. In RCS-1, the emphasis was on combining commands with sensory feedback so as to compute the proper response to every combination of goals and states. The application was to control a robot arm with a structured light vision system in visual pursuit tasks. CMAC becomes a state machine when some of its outputs are fed directly back to the input, so RCS-1 was implemented as a set of state-machines arranged in a hierarchy of control levels.
At each level, the input command effectively selects a behavior that is driven by feedback in stimulus-response fashion. A hierarchy of these building blocks was used to implement a hierarchy of behaviors such as observed by Tinbergen  and others.
RCS-1 is similar in many respects to Brooks ' subsumption architecture,  except that RCS selects behaviors before the fact through goals expressed in commands, rather than after the fact through subsumption. The H function remained a finite state machine state-table executor. The new feature of RCS-2 was the inclusion of the G function consisting of a number of sensory processing algorithms including structured light and blob analysis algorithms.
Only the first six levels were actually built. The inclusion of the World Model provides the basis for task planning and for model-based sensory processing. This led to refinement of the task decomposition TD modules so that each have a job assigner, and planner and executor for each of the subsystems assigned a job. It is nice to be able to get directions and restaurant reviews right in your hand, but being always connected, always accessible or interruptible, and with music blaring most of the time, gets in the way of thinking.
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I need time for quiet reflection, so I probably don't appreciate those gadgets as much as others. What impact are they having on users, programmers, creativity, the commerce of software? Stroustrup: I don't know. I think the issue is what becomes important in the long run: the developer's use of massive proprietary libraries and run-time environments or the opportunities for innovative applications offered by the ubiquitous presence of the gadgets. The former channels efforts into the conventional, and the latter opens new scope for imagination.
Stroustrup: Infrequently, and mostly very light literature. I find the screens too small for technical work and the facilities for scribbling and annotation still too weak. Stroustrup: I guess giving advice is easy compared to taking it. Know your fundamentals algorithms, data structures, machine architecture, systems and know several programming languages to the point where you can use them idiomatically.
Know some non-computer field of study well — math, biology, history, optics, whatever. Learn to communicate effectively in speech and in writing. Spend an unreasonable amount of time on some difficult topic to really master it. Try to do something that might make a difference in the world.
Real Time Programming
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