Todo List
- Class ompl::base::GoalLazySamples
- The Python bindings for GoalLazySamples class are still broken. The OMPL C++ code creates a new thread from which you should be able to call a python Goal sampling function. Acquiring the right threads and locks and messing around with the Python Global Interpreter Lock (GIL) is very tricky. See ompl/py-bindings/generate_bindings.py for an initial attempt to make this work.
- Member ompl::base::InformedSampler::heuristicSolnCost (const State *statePtr) const
- With the future invention of a heuristic class, this should move.
- Member ompl::base::PathLengthDirectInfSampler::heuristicSolnCost (const State *statePtr) const
- Use a heuristic function for the full solution cost defined in OptimizationObjective or some new Heuristic class once said function is defined.
- Class ompl::control::LTLPlanner
- cite papers
- Member ompl::geometric::BFMT::plan (BiDirMotion *x_init, BiDirMotion *x_goal, BiDirMotion *&z, const base::PlannerTerminationCondition &ptc)
- This precomputation is useful only if the same planner is used many times. otherwise is probably a waste of time. Do a real precomputation before calling solve().
- Class ompl::geometric::BITstar
- Implement approximate solution support.
- Make the k-nearest variant correct. Right now the search considers the k-nearest samples and the k-nearest vertices. It should find the combined k-nearest "samples & vertices".
- Member ompl::geometric::FMT::solve (const base::PlannerTerminationCondition &ptc)
- Create a PRM-like connection strategy