OMPL 2.0.0

• Completely rewritten Python bindings.
  • Enabled by default if Python is detected at build time.
  • Prebuilt OMPL Python modules can now be installed via pip install ompl.
• VAMP Integration
  • Added VAMP (Vector-Accelerated Motion Planning) as an optional high-performance backend for collision checking and motion validation
  • VAMP leverages SIMD instructions to accelerate forward kinematics and collision detection, achieving planning speeds up to 25 kHz
  • C++ and Python demos show usage with common robots (Panda, UR5, Fetch, Baxter)
• New geometric planners:
  • AORRTC: Asymptotically Optimal RRT-Connect
  • BLIT*: Bidirectional Lazy Informed Trees
  • TRRT*, ATRRT: asymptotically optimal version of T-RRT and an anytime (optimal) version of T-RRT, respectively
• New kinodynamic planner: HySST, an adaptation of the SST planner for hybrid systems.
• New state space: ThrochoidStateSpace, an SE(2)-like state space where distance and interpolation is defined for Dubins vehicles subject to constant drift. This is useful in planning for aerial/maritime drones subject to constant wind/current.
• Planner Arena has been completely rewritten in Python. It is now distributed separately and can be installed via pip install plannerarena.
• OMPL.app has been deprecated. New demos show how to use OMPL with real robots and visualize the results, eliminating the need for OMPL.app.
• Bug fixes.

OMPL 1.7.0

OMPL Update Release Notes

• Effort Informed Trees (EIT*)
  • A new planner designed to efficiently handle kinodynamic planning problems which Leverages an informed search strategy.
• RRT-Rope
  • Rope short-cutting technique added to the PathSimplifier class.
• new 3D extensions to the Dubins model: Vana State Space, Owens State Space, Vana-Owens State Space.
• Implemented Dubins Set Classification which enables faster distance computations.
• Removed Features : ODE (Open Dynamics Engine), MORSE robot simulator.
• Python wheels now available
• Docker images now available for: OMPL, OMPL app
• Various bug fixes

OMPL 1.6.0

• A C++17 compiler is now required.
• Added new planners:
  • ST-RRT*: a bidirectional, time-optimal planner for planning in space-time.
  • Multi-level planners: Planning algorithms which can exploit multiple levels of abstractions.
    • Rapidly-exploring Random Quotient space Trees (QRRT): A generalization of RRT to plan on different abstraction levels.
    • QRRT*: An asymptotically optimal version of QRRT.
    • Quotient-Space Roadmap Planner (QMP): A generalization of PRM to plan on different abstraction levels.
    • QMP*: An asymptotically optimal version of QMP.
• AIT* has been significantly refactored.
• SST now uses the intermediate solution callback to report new solutions.
• The kinodynamic version of SST (ompl::control::SST) now supports optimization objectives.
• New topological state spaces have been added: a torus, a sphere, a Möbius strip, and a Klein bottle.
• Updated docker images to Ubuntu Jammy.
• Several fixes for Python bindings.
• Bug fixes.

OMPL 1.5.0

• A C++14 compiler is now required. The minimum version of CMake required is now 3.5 and the minimum version of Boost supported is now 1.58.
• All development now takes place on Github. This used to be a git mirror of the mercurial repository on BitBucket, but since BitBucket is phasing out mercurial support the GitHub repo is now the main repo. All the old issues have been migrated to GitHub.
• Added build targets for easily creating Docker images for OMPL, the PlannerArena web server, and the OMPL web app. Docker images are available on Docker Hub.
• Added new planners:
  • XXL: a probabilistically complete sampling-based algorithm designed to plan the motions of high-dimensional mobile manipulators and related platforms.
  • ABIT*: an extension to BIT* that uses advanced graph-search techniques to find initial solutions faster.
  • AIT*: an anytime asymptotically optimal algorithm that simultaneously estimates and exploits problem-specific heuristics.
  • Quotient-Space RRT: a generalization of RRT to plan on different abstraction levels. The abstraction levels are represented by quotient-spaces.
  • Taskspace RRT: a variant of RRT where exploration is guided by the task space.
  • RLRT and BiRLRT: basic tree-based planners without any sophistic heuristics to guide the exploration, useful as a baseline for comparison against other tree-based planners.
• PRM, PRM*, LazyPRM, and LazyPRM* can now be initialized with an ompl::base::PlannerData instance (the generic way to represent roadmaps/trees in OMPL). This means that you seed these planners with data from a previous run from any other planner. Using the ompl::base::PlannerDataStorage functionality, this data can be saved to or loaded from disk.
• Added support for deterministic sampling. Halton sampling is included, other deterministic sampling methods can be added.
• Added a new PlannerTerminationCondition called CostConvergenceTerminationCondition, which can be used to terminate asymptotically (near-)optimal planners based on convergence.
• Clean up ompl_benchmark_script.py for Python 3.
• Updated PlannerArena again to work with latest versions of R dependencies.
• Misc. bug and documentation fixes.

OMPL 1.4.2

Added tag 1.4.2 for changeset b90610f0abe9

--HG--
branch : v1.4.2

OMPL 1.4.1

Added tag 1.4.1 for changeset 520cd0bae59b

OMPL 1.4.0

bump version number, update release date, small python binding fix

Added tag 1.2.2 for changeset 69180a5970f9

--HG--
branch : kinetic-devel

1.3.1

release 1.3.1