Communication-Aware Motion Planning in Mobile Networks

Abstract

In this technical note, we propose a communication-aware motion planning framework to increase the probability that a robot maintains its connectivity to a fixed station, while accomplishing a sensing task, in realistic communication environments. We use a probabilistic multi-scale model for channel characterization. Using this model, we propose a probabilistic framework for assessing the spatial variations of a wireless channel, based on a small number of measurements. We then show how our channel learning framework can be utilized for devising communication-aware motion planning strategies. We first present communication-aware objective functions that can plan the trajectory of the robot in order to improve its online channel assessment in an environment. We then propose a communication-aware target tracking approach for the case where a fixed station utilizes a robot (or a number of them) to keep track of the position of a moving target. In this approach, probabilistic channel assessment metrics are combined with sensing goals, when controlling the motion, in order to increase the amount of information that the fixed station receives about the target. Finally, we show the performance of our framework, using both real and simulated channel measurements. Overall, our results indicate that networked robotic operations can benefit considerably from our probabilistic channel assessment and its integration with sensing/motion planning.