RCAMP: A Resilient Communication-Aware Motion Planner for Mobile Robots with Autonomous Repair of Wireless Connectivity

TL;DR

This paper introduces RCAMP, a novel motion planning approach that enables mobile robots to autonomously repair wireless connectivity in complex environments, enhancing mission resilience in scenarios like USAR operations.

Contribution

The paper presents a resilient, online radio signal mapping method and integrates it into a motion planner that autonomously re-establishes connectivity without back-tracking or detailed network knowledge.

Findings

Effective in simulation for reconnecting robots in complex environments.

Handles stochastic radio signal variations with Gaussian Random Fields.

Demonstrates robustness in multi-channel communication scenarios.

Abstract

Mobile robots, be it autonomous or teleoperated, require stable communication with the base station to exchange valuable information. Given the stochastic elements in radio signal propagation, such as shadowing and fading, and the possibilities of unpredictable events or hardware failures, communication loss often presents a significant mission risk, both in terms of probability and impact, especially in Urban Search and Rescue (USAR) operations. Depending on the circumstances, disconnected robots are either abandoned or attempt to autonomously back-trace their way to the base station. Although recent results in Communication-Aware Motion Planning can be used to effectively manage connectivity with robots, there are no results focusing on autonomously re-establishing the wireless connectivity of a mobile robot without back-tracking or using detailed a priori information of the network. In this paper, we present a robust and online radio signal mapping method using Gaussian Random Fields and propose a Resilient Communication-Aware Motion Planner (RCAMP) that integrates the above signal mapping framework with a motion planner. RCAMP considers both the environment and the physical constraints of the robot, based on the available sensory information. We also propose a self-repair strategy using RCMAP, that takes both connectivity and the goal position into account when driving to a connection-safe position in the event of a communication loss. We demonstrate the proposed planner in a set of realistic simulations of an exploration task in single or multi-channel communication scenarios.