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Abstract
Autonomous machines are commonly subject to real-time constraints. ROS 2, a widely-used robotics framework, considers real-time capabilities as a critical factor and is constantly evolving to address these challenges, e.g., the end-to-end timing guarantee and the real-time data fusion, etc. This paper studies the ROS message synchronizer, an integral component for multi-sensor data fusion, and provides a potential direction for the synchronizer's evolution in future versions of ROS 2. For effective data fusion, input data from different sensors must be sampled at time points that align within a specific range. This paper proposes a novel message synchronization policy to meet this requirement, called the SEAM, which Synchronizes the Earliest Arrival Messages once they fall within the specified range. Unlike traditional ROS synchronizers, the SEAM does not rely on prediction information for complex optimization. Instead, it uses information from already-arrived messages to construct a feasible synchronization scheme. We demonstrate the optimality of the SEAM by proving that it always finds a feasible scheme if one indeed exists. We incorporate the SEAM into ROS 2 and conduct experiments to evaluate its effectiveness compared to traditional ROS synchronizers. © 2023 IEEE.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 44th IEEE Real-Time Systems Symposium, RTSS 2023 |
| Publisher | IEEE |
| Pages | 172-184 |
| ISBN (Electronic) | 9798350328578 |
| ISBN (Print) | 9798350328585 |
| DOIs | |
| Publication status | Published - Dec 2023 |
| Event | 44th IEEE Real-Time Systems Symposium (RTSS 2023) - Howard Plaza Hotel Taipei, Taipei, Taiwan, China Duration: 5 Dec 2023 → 8 Dec 2023 https://2023.rtss.org/ |
Publication series
| Name | Proceedings - Real-Time Systems Symposium |
|---|---|
| ISSN (Print) | 1052-8725 |
| ISSN (Electronic) | 2576-3172 |
Conference
| Conference | 44th IEEE Real-Time Systems Symposium (RTSS 2023) |
|---|---|
| Abbreviated title | RTSS ’23 |
| Place | Taiwan, China |
| City | Taipei |
| Period | 5/12/23 → 8/12/23 |
| Internet address |
Funding
This work is supported by the National Natural Science Foundation of China (No. 61972076, 62072085), Hong Kong Research Council (GRF 11208522, GRF 15206221), the Fundamental Research Funds for the Central Universities (DUT20RC(3)056).
RGC Funding Information
- RGC-funded
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GRF: Managing Information Synchronicity in Real-Time Systems
GUAN, N. (Principal Investigator / Project Coordinator)
1/01/23 → …
Project: Research
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