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Changelog

August 10, 2011

  • Replaced the implementation of BasicPRM with PRM. Thanks to James Marble, the new implementation uses BGL.

July 21, 2011

  • Added isStraightLinePathValid() to PlannerDefinition

July 12, 2011

  • Using boost ublas for real vector projections

July 9, 2011

  • Benchmarked planners are now run in a separate thread (and termination conditions are evaluated separately, to detect crashes)

July 5, 2011

  • Added GNAT nearest neighbors datastructure

June 29, 2011

  • Moved state propagation functionality from ControlSpace to a separate StatePropagator class

June 22, 2011

  • Added representation of a discrete state space

June 15, 2011

  • Improvements to control::KPIECE1, so that it considers goal biasing more appropriately
  • Move code for extracting machine properties from util/ to benchmark/
  • Add sanity checks for state spaces

June 8, 2011

  • Added representation of probability density functions (PDF)
  • Added getType() for Goal and replaced getType() for planners by getSpecs()
  • Documentation fixes

June 3, 2011

  • Added notion of probability of success for goals
  • Added initial version of RRTstar contribution

May 20, 2011

  • Generalized planner termination conditions. The user can now call terminate() at any time to signal a planner it should stop its computation

May 19, 2011

  • Renamed StateManifold to StateSpace and ControlManifold to ControlSpace

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 SO2StateSpace

March 21, 2011

  • Some work on automatic computation of cell sizes for projections

March 17, 2011

  • Path smoothing with B-Splines

March 10, 2011

  • Expose magic constants

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 state space (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[space] will return the part of the state that corresponds to the specified state space. space1 + space2 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 space (TimeStateSpace)

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 StateSpace.

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