Accelerating Kinodynamic RRT* Through Dimensionality Reduction
This work addresses motion planning efficiency for dynamical systems, offering an incremental improvement over existing kinodynamic RRT* methods.
The paper tackles the slow convergence of kinodynamic RRT* in high-dimensional state spaces by introducing Kino-RRT*, which uses a partial-final-state-free optimal controller to reduce sampling dimensionality and intermittent updates to lower computational complexity, resulting in much faster convergence for 4-D and 10-D linear systems.
Sampling-based motion planning algorithms such as RRT* are well-known for their ability to quickly find an initial solution and then converge to the optimal solution asymptotically. However, the convergence rate can be slow for highdimensional planning problems, particularly for dynamical systems where the sampling space is not just the configuration space but the full state space. In this paper, we introduce the idea of using a partial-final-state-free (PFF) optimal controller in kinodynamic RRT* [1] to reduce the dimensionality of the sampling space. Instead of sampling the full state space, the proposed accelerated kinodynamic RRT*, called Kino-RRT*, only samples part of the state space, while the rest of the states are selected by the PFF optimal controller. We also propose a delayed and intermittent update of the optimal arrival time of all the edges in the RRT* tree to decrease the computation complexity of the algorithm. We tested the proposed algorithm using 4-D and 10-D state-space linear systems and showed that Kino-RRT* converges much faster than the kinodynamic RRT* algorithm.