ROS-NetSim: A Framework for the Integration of Robotic and Network Simulators

Calvo-Fullana, Miguel; Mox, Daniel; Pyattaev, Alexander; Fink, Jonathan; Kumar, Vijay; Ribeiro, Alejandro

doi:10.1109/lra.2021.3056347

Cited by 30 publications

(14 citation statements)

References 22 publications

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“…There has been relatively little development of simulation tools at the intersection of robotic control and accurate network modelling. RoboNetSim [40] and ROS-NetSim [41] are two frameworks designed to mediate external simulators and synchronize data for accurate communications in robotics. RoboNetSim acts as an integration tool between any two robotics and networking simulators.…”

Section: Simulating Networked Swarm Roboticsmentioning

confidence: 99%

BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-agent and Swarm Control

Selden¹,

Zhou²,

Campos³

et al. 2021

Preprint

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Decentralized control in multi-robot systems is dependent on accurate and reliable communication between agents. Important communication factors, such as latency and packet delivery ratio, are strong functions of the number of agents in the network. Findings from studies of mobile and high node-count radio-frequency (RF) mesh networks have only been transferred to the domain of multi-robot systems to a limited extent, and typical multi-agent robotic simulators often depend on simple propagation models that do not reflect the behavior of realistic RF networks. In this paper, we present a new open source swarm robotics simulator, BotNet, with an embedded standards-compliant time-synchronized channel hopping (6TiSCH) RF mesh network simulator. Using this simulator we show how more accurate communications models can limit even simple multi-robot control tasks such as flocking and formation control, with agent counts ranging from 10 up to 2500 agents. The experimental results are used to motivate changes to the inter-robot communication propagation models and other networking components currently used in practice in order to bridge the sim-to-real gap.

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Section: Simulating Networked Swarm Roboticsmentioning

confidence: 99%

BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-agent and Swarm Control

Selden¹,

Zhou²,

Campos³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…On the other hand, some frameworks like RoboNetSim [12] and RosNetSim [6] are designed with general-purpose robotics in mind. RoboNetSim integrates ARGoS with NS2 and provides a synchronization mechanism bridging the physics and network simulators' continuous-time and discrete-event natures.…”

Section: Related Workmentioning

confidence: 99%

CORNET: A Co-Simulation Middleware for Robot Networks

Acharya

Bharadwaj

Simmhan³

et al. 2020

2020 International Conference on COMmunication Systems &Amp; NETworkS (COMSNETS)

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We present a networked co-simulation framework for multi-robot systems applications. We require a simulation framework that captures both physical interactions and communications aspects to effectively design such complex systems. This is necessary to co-design the multi-robots' autonomy logic and the communication protocols. The proposed framework extends existing tools to simulate the robot's autonomy and network-related aspects. We have used Gazebo with ROS/ROS2 to develop the autonomy logic for robots and mininet-WiFi as the network simulator to capture the cyber-physical systems properties of the multi-robot system. This framework addresses the need to seamlessly integrate the two simulation environments by synchronizing mobility and time, allowing for easy migration of the algorithms to real platforms.

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“…For practical reasons, decentralized execution is often unavoidable in the real-world, but it is currently unknown whether this contributes to a shift of domains, and how resulting policies are affected. Multi-robot GNNs require inter-robot communication, but real-world wireless communication is noisy, and messages can be lost or delayed, leading to significant performance loss-this is exemplified in prior work that demonstrates the need for appropriate models to overcome these challenges [15], [16], [17], [18]. Further compounding these issues, decentralized policies are typically executed asynchronously, resulting in system states not previously encountered during training.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies

Blumenkamp¹,

Morad²,

Gielis³

et al. 2021

Preprint

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Graph Neural Networks (GNNs) are a paradigmshifting neural architecture to facilitate the learning of complex multi-agent behaviors. Recent work has demonstrated remarkable performance in tasks such as flocking, multi-agent path planning and cooperative coverage. However, the policies derived through GNN-based learning schemes have not yet been deployed to the real-world on physical multi-robot systems. In this work, we present the design of a system that allows for fully decentralized execution of GNN-based policies. We create a framework based on ROS2 and elaborate its details in this paper. We demonstrate our framework on a case-study that requires tight coordination between robots, and present firstof-a-kind results that show successful real-world deployment of GNN-based policies on a decentralized multi-robot system relying on Adhoc communication. A video demonstration of this case-study can be found online 1 .

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ROS-NetSim: A Framework for the Integration of Robotic and Network Simulators

Cited by 30 publications

References 22 publications

BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-agent and Swarm Control

BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-agent and Swarm Control

CORNET: A Co-Simulation Middleware for Robot Networks

A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies

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