Changes since 0.14.2
--------------------

- Added many new planners:
	- Linear Temporal Logical Planner (LTLPlanner): a planner that finds
	  solutions for kinodynamic motion planning problems where the goal is specified
	  by a Linear Temporal Logic (LTL) specification.
	- Fast Marching Tree algorithm (FMT∗): a new asymptotically optimal
	  algorithm contributed by Marco Pavone's Autonomous Systems Laboratory at
	  Stanford.
	- Coupled Forest of Random Engrafting Search Trees (CForest): a
	  meta-planner that runs several instances of asymptotically optimal planners in
	  different threads. When one thread finds a better solution path, the states
	  along the path are passed on to the other threads.
	- Anytime Path Shortening: a generic wrapper around one or more
	  geometric motion planners that repeatedly applies shortcutting and
	  hybridization to a set of solution paths. Any number and combination of
	  planners can be specified, each is run in a separate thread.
        - LazyPRM / LazyPRMstar: not entirely new, but completely re-implemented.

- RRT* has a new option to periodically prune parts of the tree that are
  guaranteed not to contain the optimal solution. This idea was introduced in
  CForest, but it useful independently of the CForest parallelization. Although
  pruning is almost always useful, it is disabled by default to preserve the
  original behavior.

- Created consistent behavior across all planners that can optimize paths.
  Calls to the solve method of RRT*, PRM*, SPARS, SPARStwo, and LBTRRT will
  terminate when (1) the planner termination condition is true or (2) the
  optimization objective is satisfied. To make these planners terminate when any
  solution is found, you can set the cost threshold for the optimization
  objective to, e.g., OptimizationObjective::infiniteCost(). For most of these
  planners, asymptotic (approximate) optimality is only guaranteed when using the
  PathLengthOptimizationObjective class.

- Most control-based planners can now use steering functions. The user simply
  needs to override ompl::control::StatePropagator::steer() and
  ompl::control::StatePropagator::canSteer() in a derived class.

- Several improvements to benchmarking functionality:
	- Planner Arena has been relaunched and can be used to interactively
	  visualize benchmark results.
	- ompl_benchmark_statistics.py can now also parse MoveIt! benchmark log
	  files using the flag --moveit.

- Added ompl::tools::PlannerMonitor class, which periodically prints planner
  progress properties in a separate thread. Useful for developing / debugging new
  planners.

- Updated Py++ toolchain. If you previously installed Py++ and have trouble
  generating the OMPL Python bindings, you may need to run "make
  installpyplusplus" again.

- Minimum Boost version is now 1.48 and minimum CMake version is now 2.8.7.
- Bug fixes.