Latency Reduction and Packet Synchronization in Low-Resource Devices Connected by DDS Networks in Autonomous UAVs
<p>MicroXRCE-DDS architecture [<a href="#B15-sensors-23-09269" class="html-bibr">15</a>].</p> "> Figure 2
<p>PX4 EKF2 architecture [<a href="#B18-sensors-23-09269" class="html-bibr">18</a>].</p> "> Figure 3
<p>ROS and FCU communications for latency assessment using MAVROS/MAVlink bridge.</p> "> Figure 4
<p>ROS 2 and FCU communications for latency assessment using the MicroXRCE–DDS bridge.</p> "> Figure 5
<p>Hardware-in-the-loop setup for end-to-end latency measurements.</p> "> Figure 6
<p>Visual odometry message: latency comparison, FCU.</p> "> Figure 7
<p>Visual odometry message: comparison of latency probability distribution, FCU.</p> "> Figure 8
<p>Visual odometry message: latency when using MicroXRCE-DDS–ROS 2.</p> "> Figure 9
<p>IMU message: latency comparison, companion computer.</p> "> Figure 10
<p>IMU message: comparison of latency probability distribution, companion computer.</p> "> Figure 11
<p>Pixhawk 4 FMU-V5: CPU and RAM load comparison during latency testing.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Flight Controller Unit
2.2. PX4 Firmware
2.3. ROS/ROS 2
2.4. MAVlink
2.5. MAVROS
2.6. MicroXRCE-DDS
2.7. Companion Computer
2.8. Host Computer
2.9. Latency Assessment
Algorithm 1 Latency Test Node in rospy (ROS) |
|
Algorithm 2 Latency Test Node in RCLPY (ROS 2) |
|
2.10. Experimental Setup
3. Results
3.1. Latency Comparison in the Flight Control Unit
- Reliability: BEST_EFFORT; the publisher attempts to deliver the maximum number of samples possible.
- History and Queue Size: KEEP_LAST; only one message is stored in the processing queue.
- Durability: TRANSIENT_LOCAL; the publishers are responsible for sending the last available message to newly discovered subscribers.
Algorithm 3 Time Synchronization Algorithm (PX4 uORB/MAVlink [12,18]) |
|
3.2. Latency Comparison at the Companion Computer
3.3. Flight Controller CPU and RAM Utilization
4. Discussion
4.1. Latency Reduction and Time Synchronization for Enhanced GNC in UAVs
4.2. Time Synchronization Effects in UAV High-Level Layer (Companion Computer)
4.3. Event-Driven Communication between Flight Controller and Companion Computer
4.4. Trade-Offs of Using DDS Networks in UAV Systems
4.5. Current Limitations of MAVlink and MAVROS
5. Conclusions
Future Work
- Assessment of the effect of high-level latency correction in the performance of GNC algorithms, in particular motion control.
- Assessment of scalability effects on latency and latency correction when using denser ROS 2 routines, i.e., when more DDS topics are shared between the FCU and companion computer.
- A latency comparison between the MicroXRCE-DDS–ROS 2 bridge and other emerging technologies, such as Zenoh-Pico–ROS 2 [26].
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AAUVs | Autonomous Aerial and Underwater Vehicles |
DDS | Data Distribution Service |
EKF | Extended Kalman Filter |
FCU | Flight Controller Unit |
IMU | Inertial Measurement Unit |
RTPS | Real-Time Publish–Subscribe |
ROS | Robot Operating System |
SLAM | Simultaneous Localization and Mapping |
UAV | Unmanned Aerial Vehicles |
HIL | Hardware-in-the-loop |
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MAVROS Topic | ROS Message | Rate (Hz) |
---|---|---|
mavros/vision_pose/pose 1 | PoseStamped | 200 |
mavros/imu/data_raw 2 | Imu | 200 |
Topic | uORB | Rate (Hz) |
---|---|---|
vehicle_visual_odometry 1 | VehicleOdometry | 200 |
vehicle_imu 2 | VehicleImu | 200 |
Method | Path | FCU Resource Utilization (%) 1 | Average Latency and Standard Deviation (Microsec) |
---|---|---|---|
MicroXRCE-DDS–ROS 2 | Companion Computer → Flight Controller | CPU: 28.5 | 1329 ± 162 |
Flight Controller → Companion Computer | RAM: 57.6 | 1752 ± 315 | |
MAVlink–MAVROS (ROS) | Companion Computer → Flight Controller | CPU: 59.1 | 1678 ± 560 |
Flight Controller → Companion Computer | RAM: 64.9 | 2133 ± 1020 |
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Silva Cotta, J.L.; Agar, D.; Bertaska, I.R.; Inness, J.P.; Gutierrez, H. Latency Reduction and Packet Synchronization in Low-Resource Devices Connected by DDS Networks in Autonomous UAVs. Sensors 2023, 23, 9269. https://doi.org/10.3390/s23229269
Silva Cotta JL, Agar D, Bertaska IR, Inness JP, Gutierrez H. Latency Reduction and Packet Synchronization in Low-Resource Devices Connected by DDS Networks in Autonomous UAVs. Sensors. 2023; 23(22):9269. https://doi.org/10.3390/s23229269
Chicago/Turabian StyleSilva Cotta, Joao Leonardo, Daniel Agar, Ivan R. Bertaska, John P. Inness, and Hector Gutierrez. 2023. "Latency Reduction and Packet Synchronization in Low-Resource Devices Connected by DDS Networks in Autonomous UAVs" Sensors 23, no. 22: 9269. https://doi.org/10.3390/s23229269