  Rapidly-exploring Random Trees (RRT) and its variations have emerged as a
robust and efficient tool for finding collision-free paths in robotic systems.
However, adding dynamic constraints makes the motion planning problem
significantly harder, as it requires solving two-value boundary problems
(computationally expensive) or propagating random control inputs
(uninformative). Alternatively, Iterative Discontinuity Bounded A* (iDb-A*),
introduced in our previous study, combines search and optimization iteratively.
The search step connects short trajectories (motion primitives) while allowing
a bounded discontinuity between the motion primitives, which is later repaired
in the trajectory optimization step.
  Building upon these foundations, in this paper, we present iDb-RRT, a
sampling-based kinodynamic motion planning algorithm that combines motion
primitives and trajectory optimization within the RRT framework. iDb-RRT is
probabilistically complete and can be implemented in forward or bidirectional
mode. We have tested our algorithm across a benchmark suite comprising 30
problems, spanning 8 different systems, and shown that iDb-RRT can find
solutions up to 10x faster than previous methods, especially in complex
scenarios that require long trajectories or involve navigating through narrow
passages.
