zhangyi1357 0b2427dc75
docs: add aimrt_py channel context (#56)
* feat: set default serialization type for context reference

Ensure that the serialization type defaults to "pb" when it is not explicitly set in the context reference. This improves compatibility and prevents potential errors related to serialization handling.

* docs: enhance message publishing and context usage documentation

Clarify the usage of the `Publish` function by adding context and serialization options. Introduce `Context` and `ContextRef` details for improved understanding. Adjust subscriber callback signatures and remove deprecated notes for a cleaner documentation experience.

* feat: enhance event publishing with context and serialization options

Add support for publishing events with various configurations, including without context, with JSON serialization, and with context references. Improve logging for better debugging and clarity in event handling.

* feat: add enumeration for channel context types

Introduce enumeration for `aimrt_channel_context_type_t` to better categorize channel contexts, improving readability and usability in Python bindings.

* docs: clarify context type description

Enhance documentation by simplifying the explanation of the context type and explicitly defining the associated enumeration values for better understanding.
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AimRT

Full project details on aimrt.org

Brief

  • AimRT is a basic runtime framework for the field of modern robotics. It is developed based on modern C++, is lightweight and easy to deploy, and has more modern designs in resource management and control, asynchronous programming, deployment configuration, etc.
  • AimRT is committed to integrating the research and development of various deployment scenarios such as robot end-side, edge end, and cloud. It serves modern AI- and cloud-based robot applications and provides modern and complete debugging and performance analysis tools, as well as good observability support.
  • AimRT also provides a comprehensive plug-in development interface, which is highly scalable and compatible with ROS2, HTTP, Grpc and other traditional robot ecosystems or cloud service ecosystems, and supports progressive upgrades of your existing systems.

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