Robotic Ad-hoc Networks Connectivity Maintenance based on RF Signal Strength Mapping

Abstract

Network connectivity preservation is one of the substantial factors in achieving efficient mobile robot teams' maneuverability. We present a connectivity maintenance method for a robot team's communication. The proposed approach augments the Radio Frequency Mapping Recognition (RFMR) method and the signal strength gradient decent approach for an overall goal to create a Proactive Motion Control Algorithm (PMCA). The PMCA algorithm controls and helps strengthen mobile communicating robots' connectivity in the existent Radio Frequency (RF) obstacles. The RFMR method takes advantage of Hidden Markov Models (HMMs) results, which assist in learning electromagnetic environments depending on measurements of RF signal strength. The classification results of HMM lead the robots to resolve whether to continue the current trajectory for avoiding the obstacle shadow or move back to desirable robust Signal Strength (SS) positions. In both cases, the robot will run the gradient approach to determine the signal change trend and drive the robot toward the strong SS direction for maintaining link connectivity. The PMCA, depending on the results of RFMR and gradient approaches, promises to preserve robots' motion control and link connectivity maintenance.