Dynamic Speed and Sensor Rate Adjustment for Mobile Robotic Systems

Abstract

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.

Modelling computational requirements of mobile robotic systems using zones and processing windows

“…Table 2 shows a comparison between both cases in terms of total time t total needed to traverse the path, average speed v, the ratio of average speed to the desired speedv/v desired , total slack time t slacktotal over the whole path, t slack average slack time over the whole path, and t slacktotal /t total is the ratio of the total slack time to the total time needed to complete the path. The result shows that the processing window adjustment algorithm improved the average speed for the robot over the whole path by 16.74% relative to the desired speed assuming a linear for approximating speed transitions and 19.96% assuming ideal speed transitions [20]. The simulation result also shows that there is more slack time gained by using the processing window adjustment algorithm.…”

“…The simulation assumes the linear speed transition model presented in 5.2.2, earlier simulation results that assumes ideal speed transition conditions (i.e., no speed transition time when switching between speeds) have been published in [20]. The simulation uses the parameters of an autonomous mobile robot that was used as the lead robot in the Robotic Safety Marker project [8,21,19].…”