This paper presents a method for determining the feasible processing window for a leader robot that controls a group of follower robots. A processing window is defined as the time interval from the instant the platform starts collecting data to the moment the robot leader finishes planning and communicates with follower robots. The method determines the minimum processing window after considering the different bounds affecting the control of the robot, such as of robot’s processing power, robot sensors and the motion of the robots in the group. We present a derivation of the bounds that affect the processing window of the leader robot and an algorithm for determining the feasible window or adjusting the parameters that affect the processing window in case a feasible processing window cannot be achieved because of an overload condition. The application considered in this paper is a group of mobile robots that self deploy, retrieve, and reconfigure barrels to improve the safety of highway construction/maintenance workers.

Mobile robotic systems must sense constraints imposed by a dynamically changing environment and predictably react to those changes in real-time. Complexity arises in mobile robotic systems because the computing platform travels through the environment with which the system is interacting. These systems have spatio-temporal requirements in the sense that correct behavior is defined in terms of both space and time. The focus of this paper is mobile robotic platforms that must sense their environment and avoid obstacles as they navigate from one point to another. We present a design and analysis methodology for these platforms that integrates spatio-temporal attributes with fixed priority realtime scheduling through the use of zone and processing window abstractions. We propose an algorithm for adjustment of the processing window in order maximize the speed of the robot as it faces obstacles in the environment.

We present an autonomous, mobile, robotics application that requires dynamic adjustments of task execution rates to meet the demands of an unpredictable environment. The Robotic Safety Marker (RSM) system consists of one lead robot, the foreman, and a group of guided robots, called robotic safety markers (a.k.a., barrels). An extensive analysis is conducted of two applications running on the foreman. Both applications require adjusting task periods to achieve desired performance metrics with respect to the speed at which a system task is completed, the accuracy of RSM placement, or the number of RSMs controlled by the foreman. A static priority scheduling solution is proposed that takes into consideration the strict deadline requirements of some of the tasks and their dynamic periods. Finally, a schedulability analysis is developed that can be executed online to accommodate the dynamic performance requirements and to distinguish between safe operating points and potentially unsafe operating points.

Dynamic voltage scaling (DVS) algorithms save energy by scaling down the processor frequency when the processor is not fully loaded. Many algorithms have been proposed for periodic and aperiodic task models but none support the canonical sporadic task model. A DVS algorithm, called DVSST, is presented that can be used with sporadic tasks in conjunction with preemptive EDF scheduling. The algorithm is proven to guarantee each tasks meets its deadline while saving the maximum amount of energy possible with processor frequency scaling. DVSST was implemented in the /spl mu/C/OS-II real-time operating system for embedded systems and its overhead was measured using a stand-alone Rabbit 2000 test board. Though theoretically optimal, the actual power savings realized with DVSST is a function of the sporadic task set and the processor's DVS support. It is shown that the DVSST algorithm achieves 83% of the theoretical power savings for a robotic highway safety marker real-time application. The difference between the theoretical power savings and the actual power savings is due to the limited number of frequency levels the Rabbit 2000 processor supports.