Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales

Terms and Conditions

Twitter GitHub LinkedIn Bluesky Youtube

© 2026 ResearchTrend.AI, All rights reserved.

Home
Papers
1609.05960
Cited By

Incremental Sampling-based Motion Planners Using Policy Iteration
Methods

Incremental Sampling-based Motion Planners Using Policy Iteration Methods

19 September 2016

Panagiotis Tsiotras

ArXiv (abs)PDF HTML

Papers citing "Incremental Sampling-based Motion Planners Using Policy Iteration Methods"

6 / 6 papers shown

The Value of Planning for Infinite-Horizon Model Predictive Control

The Value of Planning for Infinite-Horizon Model Predictive ControlIEEE International Conference on Robotics and Automation (ICRA), 2021

232

14

0

07 Apr 2021

LES: Locally Exploitative Sampling for Robot Path Planning

LES: Locally Exploitative Sampling for Robot Path PlanningIEEE International Conference on Robotics and Automation (ICRA), 2021

Panagiotis Tsiotras

124

0

0

25 Feb 2021

Asymptotically Optimal Sampling-Based Motion Planning Methods

Asymptotically Optimal Sampling-Based Motion Planning Methods

Jonathan Gammell

Marlin P. Strub

431

6

0

22 Sep 2020

GPU Parallelization of Policy Iteration RRT#

GPU Parallelization of Policy Iteration RRT#IEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2020

Richard Connor Lawson

Panagiotis Tsiotras

98

6

0

10 Mar 2020

State-Continuity Approximation of Markov Decision Processes via Finite
Element Methods for Autonomous System Planning

State-Continuity Approximation of Markov Decision Processes via Finite Element Methods for Autonomous System Planning

154

1

0

03 Mar 2019

Batch Informed Trees (BIT*): Informed Asymptotically Optimal Anytime
Search

Batch Informed Trees (BIT*): Informed Asymptotically Optimal Anytime Search

Jonathan Gammell

Timothy D. Barfoot

368

214

0

06 Jul 2017

Page 1 of 1