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General-purpose microprocessors are increasingly being used for control applications due to their widespread availability and software support for non-control functions like networking and operator interfaces. Two classes of real-time operating systems (RTOS) exist for these systems. The traditional RTOS serves as the sole operating system, and provides all OS services. Examples 1 include ETS, LynxOS, QNX, Windows CE and VxWorks. RTOS extensions add real-time scheduling capabilities to non-real-time OSes, and provide minimal services needed for the time-critical portions of an application. Examples include RTAI and RTL for Linux, and HyperKernel, OnTime and RTX for Windows NT. Timing jitter is an issue in these systems, due to hardware effects such as bus locking, caches and pipelines, and software effects from mutual exclusion resource locks, non-preemtible critical sections, disabled interrupts, and multiple code paths in the scheduler. Jitter is typically on the order of a microsecond to a few tens of microseconds for hard real-time operating systems, and ranges from milliseconds to seconds in the worst case for soft real-time operating systems. The question of its significance on the performance of a controller arises. Naturally, the smaller the scheduling period required for a control task, the more significant is the impact of timing jitter. Aside from this intuitive relationship is the greater significance of timing on openloop control, such as for stepper motors, than for closed-loop control, such as for servo motors. Techniques for measuring timing jitter are discussed, and comparisons between various platforms are presented. Techniques to reduce jitter or mitigate its effects are presented. The impact of jitter on stepper motor control is analyzed.

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The Defense Applied Research Projects Agency (DARPA) Learning Applied to Ground Vehicles (LAGR) program aims to develop algorithms for autonomous vehicle navigation that learn how to operate in complex terrain. Over many years, the National Institute of Standards and Technology (NIST) has developed a reference model control system architecture called 4D/RCS that has been applied to many kinds of robot control, including autonomous vehicle control. For the LAGR program, NIST has embedded learning into a 4D/RCS controller to enable the small robot used in the program to learn to navigate through a range of terrain types. The vehicle learns in several ways. These include learning by example, learning by experience, and learning how to optimize traversal. Learning takes place in the sensory processing, world modeling, and behavior generation parts of the control system. The 4D/RCS architecture is explained in the paper, its application to LAGR is described, and the learning algorithms are discussed. Results are shown of the performance of the NIST control system on independently‐conducted tests. Further work on the system and its learning capabilities is discussed. © 2007 Wiley Periodicals, Inc.

Industrial robots can perform motion with sub-millimeter repeatability when programmed using the teach-and-playback method. While effective, this method requires significant up-front time, tying up the robot and a person during the teaching phase. Off-line programming can be used to generate robot programs, but the accuracy of this method is poor unless supplemented with good calibration to remove systematic errors, feed-forward models to anticipate robot response to loads, and sensing to compensate for unmodeled errors. These increase the complexity and up-front cost of the system, but the payback in the reduction of recurring teach programming time can be worth the effort. This payback especially benefits small-batch, short-turnaround applications typical of small-to-medium enterprises, who need the agility afforded by off-line application development to be competitive against low-cost manual labor. To fully benefit from this agile application tasking model, a common representation of tasks should be used that is understood by all of the resources required for the job: robots, tooling, sensors, and people. This paper describes an information model, the Canonical Robot Command Language (CRCL), which provides a high-level description of robot tasks and associated control and status information.