Mobile robots, be it autonomous or teleoperated, require stable communicationwith the base station to exchange valuable information. Given the stochasticelements in radio signal propagation, such as shadowing and fading, and thepossibilities of unpredictable events or hardware failures, communication lossoften presents a significant mission risk, both in terms of probability andimpact, especially in Urban Search and Rescue (USAR) operations. Depending onthe circumstances, disconnected robots are either abandoned or attempt toautonomously back-trace their way to the base station. Although recent resultsin Communication-Aware Motion Planning can be used to effectively manageconnectivity with robots, there are no results focusing on autonomouslyre-establishing the wireless connectivity of a mobile robot withoutback-tracking or using detailed a priori information of the network. In this paper, we present a robust and online radio signal mapping methodusing Gaussian Random Fields and propose a Resilient Communication-Aware MotionPlanner (RCAMP) that integrates the above signal mapping framework with amotion planner. RCAMP considers both the environment and the physicalconstraints of the robot, based on the available sensory information. We alsopropose a self-repair strategy using RCMAP, that takes both connectivity andthe goal position into account when driving to a connection-safe position inthe event of a communication loss. We demonstrate the proposed planner in a setof realistic simulations of an exploration task in single or multi-channelcommunication scenarios.
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