Even though bad solve statuses are usual because sometimes it is hard to
solve for the waypoint, there could be cases where it is impossible to
solve for a waypoint for a certain transition list (although we try to
ensure that the solver should have a positive probability of solving
it, by analysing the optimization problem in an earlier step). Adding a
threshold to it would ensure that the solving step does not take an
exponential time to decide that a transition list is not suitable.

Previously, `solveOptimizationProblem()` sometimes returns configuration
list that has collision in at least one configuration. This was possibly
because the collision validation is not working properly. Fixed by using
the new collision validation provided by hpp::core.

Some refactoring is also done to make the return of the function a bit
clearer (instead of simple int, use enum).

Make graph search data part of the path planner object, so that we don't
have to search the graph from scratch everytime we want to compute a new
list of configurations.

Remove nested if and add more comments to explain GraphSearchData

From the list of goal constraints, we can find a list of states that
contain the maximum number of goal constraints. We choose to compute
only state sequences that end in a state that can potentially be a goal
state. However, future work needs to assess/prove that this heuristic
covers all the possible goal states.

Previously the code has segmentation fault during one of the solutions
(not every one). This was because when an item is inserted into the
deque, iterators are invalidated. Incrementing the iterator causes
undefined behavior, which somehow worked until that point in the program
but not afterwards. We have changed to use index instead of iterator on
the deque.

When goal is defined as set of constraints, every state sequence is a
possible solution. The current implementation grows the list of possible
state sequences faster than they are attempted: for every time we add all
neighbors of the front node to queue, we only try out one sequences.
This could be too space consuming if the graph has large branching factor.

Non-static data members should be initialized in the order they were declared in
the class definition. For good practice, we should keep all the data
members in good order.

Before adding another constraint to a solver, check if it already has
the stricter version of the constraint. `constraint/hold` is stricter
than `constraint` and stricter than `constraint/complement`.

The expected behavior of solveOptimizationProblem () function is that,
if it manages to compute configurations for all waypoints, it will
return that sequence of configurations. In case when it fails, it
should return the longest sequence of valid configurations.

The previous implementation saves each configuration computed, even if
it is invalid. Also, when backtracking to a previous waypoint, the new
points computed will overwrite some of the waypoint in the longest
sequence. So indeed, the final sequence kept is not valid, and not
consistent (not generated in successful consecutive solves).

This has now been fixed by saving the longest sequence of valid
configurations in a separate variable, and only update it when a longer
sequence is found.

For the parametrizable constraints, we should initialize them before using
the solver to check if the initial configuration satisfies all the
propagated constraints.

Propagate the constraints from intermediate nodes as well, so that we
have a higher chance of filtering out invalid state sequences.
Direction of propagation should be from the final node to the first
node, so that we don't miss out the constraints that don't reach the
final node but are still relevant for the first portion of the
transitions.

This is currently done by propagating the constraints from the last
state to the initial state if the joints involved in each constraint are
constrained during all transitions between the initial state and the
last state. Checking the initial configuration to see if it
satisfies these constraints will help to eliminate some state sequences.

Able to build now, but code is probably useless

Currently for most of the problems we look at, loops (an edge going into
the same state) are not useful. However, these loops appear in a lot of
state sequences considered by the algorithm and slow it down
significantly. Until we encounter a problem where loops are necessary to
accomplish the task, we should just ignore them.

fix submodule url

  - Use parent implementation for method solve,
  - reimplement tryToConnectInitAndGoals,
  - remove InStatePath class and call core::DiffusingPlanner instead.
  - optimize paths on sub-manifolds with random shortcut and insert path
    in roadmap.

  when calling a path planner in hpp-core.
  Throw core::path_planning_failed when interrupted.

Fix steeringMethod::CrossStateOptimization

  - do not copy constraints from graph solvers,
  - in solver construction, use target constraints of transitions instead of
    merging solvers of edge, graph and target state. In target constraints,
    constraints and complements are merged when necessary.

  Do not clear constraintsAndComplements_ after graph initialization,
  Add method clearConstraintsAndComplement(),
  call this method in ProblemSolver::initConstraintGraph().

  - add parameter "CrossStateOptimization/nRandomConfig" to specify the number
    of trials to generate each waypoint configuration.