v0.6.1 - Initial PyPI release & NEON bugfixes

What's Changed

• Add workflow to publish source to PyPI by @zkingston in #56
• Vamp as a C++ library by @saahu27 in #55
• Reduce warnings in compilation by @zkingston in #59
• Fix local Python installation after package renaming for PyPI by @wbthomason in #60
• Update license to Apache 2.0 by @zkingston in #63
• Fix ARM64 NEON type conversion errors for GCC 13+ compatibility by @saahu27 in #64
• fix: add cmp_greater_than to neon by @jlruan in #68
• Allow overriding VAMP_ARCH for portable builds by @jakl-bdai in #69
• NEON: replace numeric conversion with bit reinterpretation in SIMD log by @mlsdpk in #70

New Contributors

• @saahu27 made their first contribution in #55
• @jlruan made their first contribution in #68
• @jakl-bdai made their first contribution in #69
• @mlsdpk made their first contribution in #70

Full Changelog: v0.6.0...v0.6.1

v0.6.0 - Python API Update

Minor overhaul of the Python bindings to support more robots, and updated the Python API to support both lists and numpy arrays. There are also some minor fixes to AORRTC to address edge case bugs.

What's Changed

• Optionally compile the Python module by @zkingston in #49
• AORRTC Segfault and Edge Case Fixes by @zkingston in #50
• Modularized robot modules for Python bindings by @zkingston in #51
• AORRTC Clean-up + Additional Options + Handling edge cases by @zkingston in #52
• Add filter_self_from_pointcloud back to bindings by @zkingston in #53

Full Changelog: v0.5.0...v0.6.0

AORRTC Release

This release includes the implementation of Asymptotically Optimal RRT-Connect. This is an asymptotically optimal motion planner that provides state-of-the-art performance and finds initial solutions as fast as RRT-Connect. There have also been a number of fixes and API changes.

What's Changed

• Fix: matrix-vector multiplication for rotation in Attachment::pose by @agarret7 in #37
• Add basic C++ example of VAMP usage by @zkingston in #38
• fix: Check if robot configuration is in bounds in Python validate function by @zkingston in #39
• add demonstration GIF from CAPT work to README by @claytonwramsey in #42
• fix: Fixes to all_true in SIMD wrapper and handling uninitialized memory in validate by @zkingston in #44
• Remove ubuntu 20.04 workflow runner by @zkingston in #46
• Made interpolation on path more clearly defined by @zkingston in #45
• Add Asymptotically Optimal RRT-Connect (AORRTC) by @twill777 in #47

New Contributors

• @agarret7 made their first contribution in #37
• @claytonwramsey made their first contribution in #42
• @twill777 made their first contribution in #47

Full Changelog: v0.4.0...v0.5.0

v0.4.0 - FCIT* Release

This release includes the implementation of the Fully-Connected Informed Tree (FCIT*) planner from https://arxiv.org/pdf/2411.17902.
This is an asymptotically optimal motion planner that provides state-of-the-art performance on the MotionBenchMaker dataset.
There have also been changes to the RNG API to enable better randomization of multiple trials.

What's Changed

• Fix typo bug with vreinterpretq + CI improvements by @wbthomason in #21
• Add support for filtering NDArray pointclouds by @wbthomason in #23
• fix: explicitly use float32 for numpy typing by @zkingston in #27
• feat: RNG is now a planner argument, expose XORShift generation by @zkingston in #29
• Add the FCIT* asymptotically optimal motion planner by @zkingston in #35

Full Changelog: v0.3.0...v0.4.0

v0.3.0 - End-effector Attachments

What's Changed

• Add support to add custom end-effector attachments for Panda, UR5, and Fetch in #18
• Example OMPL Integration Script in #16
• Various bug fixes

Attachments

See the scripts/atttachments.py for an example of how to attach custom geometry to the end-effectors of the UR5, Panda, and Fetch robots.

Initial CAPT Release

This release contains the implementation of the Collision-Affording Point Tree (CAPT) from the forthcoming RSS 2024 paper “Collision-Affording Point Trees: SIMD-Amenable Nearest Neighbors for Fast Collision Checking” (http://arxiv.org/abs/2406.02807). The CAPT enables fast collision checking of our spherized robot models against pointcloud data, and has an average query time of less than 10 nanoseconds on 3D scenes composed of thousands of points.

Initial Release

Initial public release of VAMP

Co-authored-by: Wil Thomason <[email protected]>