Kinodynamic Motion Planning With Continuous-Time Q-Learning: An Online, Model-Free, and Safe Navigation Framework.

Clicks: 359
ID: 96692
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #16 of 56 articles by views in IEEE Transactions on Neural Networks and Learning Systems

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
This paper presents an online kinodynamic motion planning algorithmic framework using asymptotically optimal rapidly-exploring random tree (RRT*) and continuous-time Q-learning, which we term as RRT-Q. We formulate a model-free Q-based advantage function and we utilize integral reinforcement learning to develop tuning laws for the online approximation of the optimal cost and the optimal policy of continuous-time linear systems. Moreover, we provide rigorous Lyapunov-based proofs for the stability of the equilibrium point, which results in asymptotic convergence properties. A terminal state evaluation procedure is introduced to facilitate the online implementation. We propose a static obstacle augmentation and a local replanning framework, which are based on topological connectedness, to locally recompute the robot's path and ensure collision-free navigation. We perform simulations and a qualitative comparison to evaluate the efficacy of the proposed methodology.
Reference Key
kontoudis2019kinodynamicieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kontoudis, George P;Vamvoudakis, Kyriakos G;
Journal IEEE Transactions on Neural Networks and Learning Systems
Year 2019
DOI
10.1109/TNNLS.2019.2899311
URL
Keywords
interprofessional education experiential education pharmacy education teaching competency development

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.