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Introduction video showing the trajectory cache working.
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Introduction video showing the trajectory cache working.
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</video>
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For more info, please see the :moveit_codedir:`package README <moveit_ros/trajectory_cache/README.md>`.
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3. Fetch from cache and execute (while still planning and caching with pruning)
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4. Fetch from cache and execute, except with large start tolerances
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Additionally, the `demo source code itself <how_to_guides/trajectory_cache/src/trajectory_cache_demo.cpp>`_ is heavily annotated with comments and tutorial notes, should you wish to inspect the code and dive deeper.
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Additionally, the :codedir:`demo source code itself <how_to_guides/trajectory_cache/src/trajectory_cache_demo.cpp>` is heavily annotated with comments and tutorial notes, should you wish to inspect the code and dive deeper.
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Pre-Requisites
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++++++++++++++
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.. note::
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Sometimes a randomly generated demo goal pose is unreachable (or the ``move_group`` fails to initialize properly).
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If this happens, the demo will halt in the first phase due to a failed planning call or other issue.
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Just restart the demo, which will generate new demo goal poses, and resolve the issue.
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Configuring the Demo
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++++++++++++++++++++
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Additionally, the demo's launch file exposes launch arguments that allows you to change many aspects of the demo.
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Look at the `demo's launch file <how_to_guides/trajectory_cache/launch/trajectory_cache_demo.launch.py>`_ for the full list of configurable launch arguments.
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Look at the :codedir:`demo's launch file <how_to_guides/trajectory_cache/launch/trajectory_cache_demo.launch.py>` for the full list of configurable launch arguments.
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For example, you can specify a disk path for the cache to be saved to and loaded from, instead of memory:
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@@ -192,7 +192,7 @@ To interpret the visualization:
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.. note::
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You may note how in the visualization that the green trajectory (the best trajectory) appears to be much more optimal than some of the other candidate plans.
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And furthermore, that most of the time, the fetch times are shorter than the planning times, even in this no-obstruction case, showing how the cache is saving the process planning time.
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The default cache insert policy inserts a cache plan only if it is the best seen (in terms of execution time) so far.
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Furthermore, notice that the set of matchable trajectories (indicated by white) is an even more constrained set from
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Furthermore, notice that the set of matchable trajectories (indicated by white) is an even more constrained set from
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This shows that the cache is partitionable and able to filter out unmatchable entries by nature of the constraints that key them.
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To interpret the visualization, with the same legend as before:
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+++++++++++++++++++++
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It is possible to extend the cache to key on custom user-defined features, and also to change the cache insertion, sorting, and pruning logic.
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You do this by implementing the features and cache insert policy interfaces, then feeding them into the
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You do this by implementing the features and cache insert policy interfaces, then feeding them into the
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For example, you may decide to write your own feature extractor to key the cache, and decide when to insert or prune a cache entry on features such as:
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@@ -322,4 +322,4 @@ For example, you may decide to write your own feature extractor to key the cache
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