Pound: A multi-master ROS node for reducing delay and jitter in wireless multi-robot networks

Tardioli, Danilo; Parasuraman, Ramviyas; Ögren, Petter

doi:10.1016/j.robot.2018.10.009

Cited by 39 publications

(24 citation statements)

References 41 publications

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“…Preliminary development steps, detailed in Section 4.2 and shown in Figure 6 , are the reason for the following changes compared to SITL. First, the image type is changed to a compressed representation due to networking effects [ 21 , 22 ], which causes an increase in CPU load. Second, the MAVROS node responsible for translating between ROS and Mavlink is required to run on the onboard computer, increasing its load.…”

Section: Resultsmentioning

confidence: 99%

“…Using the maximum computation available by offloading parts of the perception—planning—control loop over a network connection is the most common solution [ 6 , 16 , 17 , 18 , 19 , 20 ]. This method is highly effective, however it introduces networking effects and drop-outs, with potentially detrimental consequences when used in real-time cycles [ 21 , 22 ]. Furthermore, several remote-sensing applications cannot rely on network connections, making onboard computation the final goal.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs

Mandel

Milford

Gonzalez

2020

Sensors

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The use of UAVs for remote sensing is increasing. In this paper, we demonstrate a method for evaluating and selecting suitable hardware to be used for deployment of algorithms for UAV-based remote sensing under considerations of Size, Weight, Power, and Computational constraints. These constraints hinder the deployment of rapidly evolving computer vision and robotics algorithms on UAVs, because they require intricate knowledge about the system and architecture to allow for effective implementation. We propose integrating computational monitoring techniques—profiling—with an industry standard specifying software quality—ISO 25000—and fusing both in a decision-making model—the analytic hierarchy process—to provide an informed decision basis for deploying embedded systems in the context of UAV-based remote sensing. One software package is combined in three software–hardware alternatives, which are profiled in hardware-in-the-loop simulations. Three objectives are used as inputs for the decision-making process. A Monte Carlo simulation provides insights into which decision-making parameters lead to which preferred alternative. Results indicate that local weights significantly influence the preference of an alternative. The approach enables relating complex parameters, leading to informed decisions about which hardware is deemed suitable for deployment in which case.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs

Mandel

Milford

Gonzalez

2020

Sensors

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“…The cause of this result is the difference between the communication middleware used in ROS 1.0 and ROS 2.0. ROS 1.0 is not suitable for networks with loss, such as wireless networks, because it uses the TCP protocol-based TCPROS as the middleware for its communications [23].…”

Section: B Latencymentioning

confidence: 99%

Real-Time Characteristics of ROS 2.0 in Multiagent Robot Systems: An Empirical Study

2020

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Due to its ease of use and flexibility, the robot operating system (ROS) is increasingly becoming the most popular middleware for robot applications, even in multiagent systems. Since ROS 1.0 does not satisfy real-time requirements, ROS 2.0 was developed, and it improved the communication stack with the real-time data distribution service (DDS) protocol. However, the actual performance level to be expected is still unknown and can largely depend on the operating system and the kernel being used, the DDS distribution, and the overall software load of the system. In this paper, we present an empirical study that evaluates the real-time performance of ROS 2.0 in both the system and communication software layers. In the system layer, the deterministic behavior of the ROS 2.0 nodes is thoroughly observed with regard to whether the tasks are schedulable and can function within the specified temporal deadline. In the communication layer, special attention is devoted to the rate of data loss and the overall latency of messages between nodes. Experiments are performed in various working conditions; for example, the system load is increased to define the real-time performance of the tasks. For reference, the results are compared with the those from the traditional ROS variation. Moreover, we implement a multiagent service robot system to verify the suitability of ROS 2.0 for real-world applications. Our results show that the application of ROS 2.0 is more suitable than that of ROS 1.0 in terms of real-time performance.

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“…Instead, multihop communication, where intermediate robots act as relays to transmit the information from the source to the destination, is necessary. However, multihop communication has several drawbacks: (a) In the worst‐case, the available bandwidth is reduced to roughly

1 ∕ (n - 1)

, where

n

is the number of robots involved in the communication (Ng & Liew, ); (b) the routing protocol must be able to manage fast topology changes due to the robots' displacements; and (c) every additional hop increases the delay and jitter associated with the information transmitted, making control inefficient and inaccurate (Tardioli, Parasuraman, & Ögren, ). While the first issue can be taken into account beforehand, planning the bandwidth demand to always be below the system capability, and the second can be solved using specific routing protocols, the third requires further consideration.…”

Section: Challengesmentioning

confidence: 99%

“…Unfortunately, multihop paths intrinsically introduce delay as the information must be relayed one or more times before reaching the destination. These relays also contribute to incrementing the jitter (Tardioli, Parasuraman, et al, ) given the nondeterministic scheme used by common wireless protocols (like 802.11) to access the medium. The intrinsic low reliability of the wireless medium (at least compared with its wired counterpart) worsens the situation.…”

Section: Setting Up An Intervention Teammentioning

confidence: 99%

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

Tardioli

Riazuelo

Sicignano

et al. 2019

Journal of Field Robotics

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The work reported in this article describes the research advances and the lessons learned by the Robotics, Perception and Real-Time group over a decade of research in the field of ground robotics in confined environments. This study has primarily focused on localization, navigation, and communications in tunnel-like environments. As will be discussed, this type of environment presents several special characteristics that often make well-established techniques fail. The aim is to share, in an open way, the experience, errors, and successes of this group with the robotics community so that those that work in such environments can avoid (some of) the errors made. At the very least, these findings can be readily taken into account when designing a solution, without needing to sift through the technical details found in the papers cited within this text.

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Pound: A multi-master ROS node for reducing delay and jitter in wireless multi-robot networks

Cited by 39 publications

References 41 publications

A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs

A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs

Real-Time Characteristics of ROS 2.0 in Multiagent Robot Systems: An Empirical Study

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

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