CHORD: Distributed Data-Sharing via Hybrid ROS 1 and 2 for Multi-Robot Exploration of Large-Scale Complex Environments

Ginting, Muhammad Fadhil; Otsu, Kyohei; Edlund, Jeffrey A.; Gao, Jinshan; Agha–mohammadi, Ali–akbar

doi:10.1109/lra.2021.3061393

Cited by 36 publications

(21 citation statements)

References 24 publications

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“…We evaluate the RSSI results separately from the other modules due to its requirement of deploying radio beacons. We evaluate the RSSI loop closure mechanism based on the one-robot dataset from the Finals Dataset, where the robot drops the beacons as described in [26]. Table II shows the max and mean error for rotational and translational components for three robots: Spot1, Spot2, and Spot3.…”

Section: Rssi Resultsmentioning

confidence: 99%

Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM

Denniston¹,

Chang²,

Reinke³

et al. 2022

Preprint

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Multi-robot SLAM systems in GPS-denied environments require loop closures to maintain a drift-free centralized map. With increasing number of robots and size of the environment, checking and computing the transformation for all the loop closure candidates becomes computationally infeasible. In this work, we describe a loop closure module that is able to prioritize which loop closures to compute based on the underlying pose graph, the proximity to known beacons, and the characteristics of the point clouds. We validate this system in the context of the DARPA Subterranean Challenge and on numerous challenging underground datasets and demonstrate the ability of this system to generate and maintain a map with low error. We find that our proposed techniques are able to select effective loop closures which results in 51% mean reduction in median error when compared to an odometric solution and 75% mean reduction in median error when compared to a baseline version of this system with no prioritization. We also find our proposed system is able to find a lower error in the mission time of one hour when compared to a system which processes every possible loop closure in four and a half hours.

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Section: Rssi Resultsmentioning

confidence: 99%

Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM

Denniston¹,

Chang²,

Reinke³

et al. 2022

Preprint

Self Cite

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“…We performed extensive field testing in an underground limestone mine in Kentucky and captured about 80 minutes of autonomous exploration with 1 base station radio, 6 ground robots (3 legged Boston Dynamics Spot robots and 3 wheeled Clearpath Robotics Husky robots), and 13 communication relay radios which were autonomously deployed by the ground robots when signal-to-noise ratio (SNR) fell below a desired threshold. All radios were MIMO StreamCaster 4000-series radios from Silvus Technologies [15,28]. Exploration spanned about 300 meters in each of the x and y directions, and we collected 1.3 million datapoints capturing transmitter position, receiver position, frequency, noise, transmitter power, RSS, SNR, loss rate, etc [1].…”

Section: A Field Test Datamentioning

confidence: 99%

PropEM-L: Radio Propagation Environment Modeling and Learning for Communication-Aware Multi-Robot Exploration

Clark¹,

Edlund²,

Net³

et al. 2022

Preprint

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Multi-robot exploration of complex, unknown environments benefits from the collaboration and cooperation offered by inter-robot communication. Accurate radio signal strength prediction enables communication-aware exploration. Models which ignore the effect of the environment on signal propagation or rely on a priori maps suffer in unknown, communication-restricted (e.g. subterranean) environments. In this work, we present Propagation Environment Modeling and Learning (PropEM-L), a framework which leverages real-time sensor-derived 3D geometric representations of an environment to extract information about line of sight between radios and attenuating walls/obstacles in order to accurately predict received signal strength (RSS). Our data-driven approach combines the strengths of well-known models of signal propagation phenomena (e.g. shadowing, reflection, diffraction) and machine learning, and can adapt online to new environments. We demonstrate the performance of PropEM-L on a six-robot team in a communicationrestricted environment with subway-like, mine-like, and cave-like characteristics, constructed for the 2021 DARPA Subterranean Challenge. Our findings indicate that PropEM-L can improve signal strength prediction accuracy by up to 44% over a logdistance path loss model.

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“…The objective of CHORD is to maintain high-bandwidth links to multiple robots for efficient commanding, autonomous operation, and data gathering in complex unknown environments. For more details on the development of NeBula's networking solutions, see (Otsu et al, 2020;Ginting et al, 2021).…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Each agent communicates by means of a wireless mesh network using commercial off-the-shelf radios. We use a hybrid of ROS 1 and ROS 2 for the communication middleware (Ginting et al, 2021;Ginting et al, 2020). We use ROS 1 for intra-robot communications, and ROS 2 for inter-robot communications.…”

Section: Chord System Designmentioning

confidence: 99%

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

Agha–mohammadi¹,

Otsu²,

Morrell³

et al. 2021

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This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.

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CHORD: Distributed Data-Sharing via Hybrid ROS 1 and 2 for Multi-Robot Exploration of Large-Scale Complex Environments

Cited by 36 publications

References 24 publications

Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM

Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM

PropEM-L: Radio Propagation Environment Modeling and Learning for Communication-Aware Multi-Robot Exploration

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

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