Planning algorithms can be constructed from RDTs in the same way as in Section 5.5. Forward, backward, and bidirectional versions can be made. The main new complication is the familiar BVP that the other sampling-based methods of this section have also suffered from. If it is expensive or even impossible to connect nearby states, then the usual complications arise. If contains a sizable open set, then a forward, single-tree planner with a gentle bias toward the goal could perform well while avoiding the BVP. However, if is a point, then a tolerance must be set on how close the RDT must get to the goal before it can declare that it has a solution. For systems with drift, the search time often increases dramatically as this tolerance decreases.
Bidirectional search offers great performance advantages in many cases, but the BVP exists when attempting connections between the two trees. One possibility is to set the tolerance very small and then concatenate the two action trajectories, as described in Section 14.3.4. If it succeeds, then the planning algorithm successfully terminates. Unfortunately, the performance once again depends greatly on the tolerance, particularly if the drift is substantial. Recent studies have shown that using a bidirectional RDT with a large connection tolerance and then closing the gap by using efficient variational techniques provides dramatic improvement in performance [198,576]. Unfortunately, variational techniques are not efficient for all systems because they must essentially solve the BVP by performing a gradient descent in the trajectory space; see Section 14.7.
Steven M LaValle 2020-08-14