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DSSM: Distributed Streaming Data Sharing Manager.
Hiroaki Fukuda1, Ryota Gunji2, Tadahiro Hasegawa3
1Department of Computer Science and Engineering, Shibaura Institute of Technology, Tokyo 135-8548, Japan.
Sensors (Basel, Switzerland)
|March 6, 2021
Summary
This study introduces the Distributed Streaming data Sharing Manager (DSSM), a middleware that overcomes single-PC limitations in robot control systems. DSSM enables distributing processes across multiple computers, improving reliability and performance for complex robotic applications.
Area of Science:
- Robotics
- Computer Science
- Software Engineering
Background:
- Robot control software development faces challenges due to diverse hardware and sensor requirements.
- Middleware systems like Robot Operating System (ROS) and Streaming data Sharing Manager (SSM) address these complexities.
- SSM facilitates timestamped sensor data sharing on a single PC, crucial for synchronized robot control.
Purpose of the Study:
- To address the limitations of single-PC middleware systems in robot control.
- To propose and evaluate a novel middleware, Distributed Streaming data Sharing Manager (DSSM), for enhanced robot control.
- To enable the distribution of processes across multiple PCs for improved system performance and reliability.
Main Methods:
- Development of a prototype for the Distributed Streaming data Sharing Manager (DSSM).
- Application of DSSM to an existing autonomous unmanned vehicle robot control system.
- Experimental evaluation of DSSM by measuring resource usage and system behavior.
Main Results:
- The DSSM prototype was successfully developed and its basic functionality confirmed.
- DSSM demonstrated the ability to distribute processes across multiple PCs, mitigating single-PC bottlenecks.
- Experiments revealed the advantages and disadvantages of DSSM in a real-world autonomous robot control scenario.
Conclusions:
- DSSM offers a viable solution for overcoming the performance limitations of single-PC middleware in robot control.
- Distributing processes using DSSM can lead to more robust and predictable robot behavior.
- Further research and experimentation are needed to fully explore DSSM's capabilities and optimize its implementation.
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