- The Open Motion Planning Library
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April 15, 2011
- Added ability to generate random paths
April 5, 2011
- Added bi-directional version of KPIECE (BKPIECE)
- Removed use of hill climbing in KPIECE
- Added a common Discretization to be used by all KPIECE-type planners
- bugfix for SO2StateManifold
March 21, 2011
- Some work on automatic computation of cell sizes for projections
March 17, 2011
- Path smoothing with B-Splines
March 10, 2011
February 15, 2011
- StateValidityChecker allows for distances to invalid states to be specified
- Improvements to GAIK and Profiler
February 10, 2011
- Function to visualize the structure of a manifold (diagram())
- Additions and bugfixes for state operators
February 5, 2011
- Bugfix for planning with controls
January 14, 2011
- Convenience operators on scoped states: state[index] will return a double value, if one exists, state[manifold] will return the part of the state that corresponds to the specified manifold. manifold1 + manifold2 yields the Cartesian product of the two. A corresponding - operator exists as well.
December 31, 2010
- Added copyToReals/copyFromReals for states & controls
November 15, 2010
- Improved ODE bindings
- Added support for sampling controls based on the previous control and/or the state at which the control will be executed.
November 10, 2010
- Added a new function for shortening computed solution paths: collapseCloseVertices()
- Added a repair() function to PathGeometric.
- Added a representation of time as a state manifold (TimeStateManifold)
November 3, 2010
- Added support for ODE bindings
- Added support for compound projections
October 7, 2010
- Added the concept of MotionValidator for checking validity of path segments.
- The collision checking resolution is a percentage of the extent of the entire space
- Generic termination conditions for planners
- Added benchmakring for planning under geometric constraints
- Added registerDefaultProjection() for StateManifold.
August 9, 2010
- Including the following sampling-based motion planning algorithms (inherit from ompl::base::Planner):
- planning under geometric constraints: KPIECE, LBKPIECE, SBL, pSBL, EST, RRT, pRRT, RRTConnect, LazyRRT, PRM
- planning under differential constraints: KPIECE, RRT
- The representation of goals is abstract. In the most general case, a goal is a predicate function that states whether the goal has been reached or not (ompl::base::Goal). More specifically, the goal can designate a region and can compute an approximate distance to this region (ompl::base::GoalRegion). This is useful when biasing planners is desirable. A further layer of abstraction allows sampling these regions (ompl::base::GoalSampleableRegion). Instantiations of this latter abstraction for a goal state (ompl::base::GoalState) and a set of states (ompl::base::GoalStates) are provided.
- Support for Python bindings