VJ modules provide to the RCS-4 control system the type of functions provided to the biological brain by the limbic system. The principal new feature in RCS-4 is the explicit representation of the Value Judgment (VJ) system. The basic building block is shown in the figure). RCS-4 is developed since the 1990s by the NIST Robot Systems Division. 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. CMAC thus became the reference model building block of RCS-1, as shown in the figure.Ī hierarchy of these building blocks was used to implement a hierarchy of behaviors such as observed by Tinbergen and others. At each level, the input command effectively selects a behavior that is driven by feedback in stimulus-response fashion. ĬMAC 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. RCS-1 was heavily influenced by biological models such as the Marr-Albus model, and the Cerebellar Model Arithmetic Computer (CMAC). The application was to control a robot arm with a structured light vision system in visual pursuit tasks. 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 first implementation was designed for sensory-interactive robotics by Barbera in the mid 1970s. 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. 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 applies to many problem domains including manufacturing examples and vehicle systems examples. Over three decades, it has evolved into a real-time control architecture for intelligent machine tools, factory automation systems, and intelligent autonomous vehicles. It was originally designed for sensory-interactive goal-directed control of laboratory manipulators. 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. 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. 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. 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. Ī reference model architecture is a canonical form, not a system design specification. The key concerns are sensing, perception, knowledge, costs, learning, planning, and execution. RCS focuses on intelligent control that adapts to uncertain and unstructured operating environments.
Architects iteratively partition system tasks and information into finer, finite subsets that are controllable and efficient. Īlso RCS provides a comprehensive methodology for designing, engineering, integrating, and testing control systems. All the control nodes at all levels share a generic node model. RCS prescribes a hierarchical control model based on a set of well-founded engineering principles to organize system complexity.
RCS is not a system design, nor is it a specification of how to implement specific systems. These modules are richly interconnected to each other by a communications system. RCS-3 produces a layered graph of processing nodes, each of which contains a task decomposition (TD), world modeling (WM), and sensory processing (SP) module. Example of a RCS-3 application of a machining workstation containing a machine tool, part buffer, and robot with vision system.