Accelerating Kinodynamic RRT* Through Dimensionality Reduction

07/02/2021
by   Dongliang Zheng, et al.
0

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.

READ FULL TEXT

page 1

page 6

research
06/05/2023

Kinodynamic FMT* with Dimensionality Reduction Heuristics and Neural Network Controllers

This paper proposes a new sampling-based kinodynamic motion planning alg...
research
10/17/2017

Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo

Asymptotically-optimal motion planners such as RRT* have been shown to i...
research
10/31/2021

Relevant Region Sampling Strategy with Adaptive Heuristic Estimation for Asymptotically Optimal Motion Planning

The sampling-based motion planning algorithms can solve the motion plann...
research
10/08/2019

Learned Critical Probabilistic Roadmaps for Robotic Motion Planning

Sampling-based motion planning techniques have emerged as an efficient a...
research
09/06/2021

Optimal Prediction of Unmeasured Output from Measurable Outputs In LTI Systems

In this short article, we showcase the derivation of an optimal predicto...
research
04/02/2017

Potential Functions based Sampling Heuristic For Optimal Path Planning

Rapidly-exploring Random Tree Star(RRT*) is a recently proposed extensio...
research
01/30/2018

Analysis of Motion Planning by Sampling in Subspaces of Progressively Increasing Dimension

Despite the performance advantages of modern sampling-based motion plann...

Please sign up or login with your details

Forgot password? Click here to reset