Intelligent Data Selection in Autonomous Robot Movement

Autonomous mobile robots have been discovering recently a wide range of applications in various areas of human activities. A robot uses many data sources (sensors) to recognize its current situation, including video, inertial, acoustic, mechanical stress. The data flows from such sources are redundant, error prone, delivered with a rather high rate, and contain considerable information as for the ongoing events so present measurement noise, errors and insignificant data about minor fluctuations of the environment and the device. They present a source information for the inference modules such as navigation, localization, path planning, scheduling etc. The total number of the ongoing events can be large. They have different importance for the movement control. In this paper, we consider a method for intelligent data selection. The method is based on the Additive Increase Multiplicative Decrease (AIMD) algorithm. The effect is in filtering the most significant information to forward to the robot movement control.