UNDERLYING TECHNICAL CONCEPTS
• Relational data modeling : DDS addresses data in a manner similar to relational databases . It can manage data by both structure related topics ( using key-fields ) and allow ad-hoc queries and filters on content and time so applications can extract specific data as needed .
• Pub-sub messaging : DDS uses the publish / subscribe paradigm for dynamic discovery and primary management of data-flows between relevant DDS entities , including publishers , subscribers , durability services , recording and replay-services , and connected databases . Request-reply and other patterns are built on this powerful substrate .
• Reliable multicast : The DDS standard wire protocol implements reliable multicast over plain UDP sockets , allowing systems to efficiently benefit from modern networking infrastructures .
• Lifecycle awareness : Unlike message-centric products , DDS offers explicit application support for information lifecycle awareness . For instance , it detects , communicates , and informs applications about first and last appearances of data ( topic instance ) updates . This facilitates timely responses to new and outdated information .
• Trigger patterns : DDS offers a variety of trigger patterns that follow updates on subscribed information . Examples include polling , callbacks ( typical for GUIs ), and WaitSets ( similar to UNIX ® ‘ select ’ ) to provide full application control for prioritized handling of selective trigger events .
HOW DOES DDS WORK ?
You can find more information on DDS and how it works at portals . omg . org / dds /.
Getting four complex robots with very different designs to use a common data system was challenging . The Data Distribution Service for Real-Time Systems [ DDS ] standard supports very flexible service parameters . We found that we could adapt the middleware to the unique needs of each robotic system .”
- Terry Fong , Director of Intelligent Robotics Group , NASA Ames
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