Real-time dynamic power management based on Pearson's Correlation Coefficient

An autonomous robotic platform should be able to perform long-range and long-endurance missions, which energy limitation is one of the most important challenges. Studies show that motion is not the only power consumer. Management of all power resources is therefore important for these systems. Moreover, many applications for control of autonomous platform are being developed and one important aspect is the excess of information, frequently redundant, that imposes a great computational cost in data processing and consequently power consummation. In our previous works, we have proposed a visual-perception system based on an automatic image discarding method as a simple solution to improve the performance of a real-time navigation system. In this paper, we propose a new environment observer method based on Pearson's Correlation Coefficient. This monocular-vision system permits that some logical components may be shut down to save processor energy consumption, and/or to make the CPU available for running concurrent processes. Nevertheless, this method may be extended to other sensors and components. Our real-time perception system has been evaluated from real data obtained by our intelligent vehicle. It is not based on previous knowledge of the environment neither camera calibration.