| IEICE Technical Committee Submission System Conference Paper's Information |
Online Proceedings [Sign in] Tech. Rep. Archives |
| Go Top Page | Go Previous | [Japanese] / [English] |
| Paper Abstract and Keywords | ||
| Presentation | 2022-09-14 10:00 Sampling-based path planning using trajectories obtained by reinforcement learning Keigo Kamiyama, Kei Ota, Ryosuke Takanami, Asako Kanezaki (Tokyo Tech)  PRMU2022-10 |
|
| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We propose a path planning method that considers dynamics constraints by using the past trajectories obtained through reinforcement learning. First, reinforcement learning of an agent is performed in an obstacle-free environment. Next, various trajectories are generated using the learned policies. The generated trajectories take robot-specific kinematics constraints into account. Sample-based path planning is then performed over points on the generated trajectories. Experiments confirmed that the generated paths are easier to follow compared to A* search, which is a conventional shortest-path finding method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | deep learning / trajectory generation / navigation / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 181, PRMU2022-10, pp. 1-6, Sept. 2022. | |
| Paper # | PRMU2022-10 | |
| Date of Issue | 2022-09-07 (PRMU) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | PRMU2022-10 |
|
| Conference Information | |
| Committee | PRMU |
| Conference Date | 2022-09-14 - 2022-09-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Deep generative model |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2022-09-PRMU |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Sampling-based path planning using trajectories obtained by reinforcement learning |
| Sub Title (in English) | |
| Keyword(1) | deep learning |
| Keyword(2) | trajectory generation |
| Keyword(3) | navigation |
| Keyword(4) | |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Keigo Kamiyama |
| 1st Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 2nd Author's Name | Kei Ota |
| 2nd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 3rd Author's Name | Ryosuke Takanami |
| 3rd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 4th Author's Name | Asako Kanezaki |
| 4th Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 5th Author's Name | |
| 5th Author's Affiliation | () |
| 6th Author's Name | |
| 6th Author's Affiliation | () |
| 7th Author's Name | |
| 7th Author's Affiliation | () |
| 8th Author's Name | |
| 8th Author's Affiliation | () |
| 9th Author's Name | |
| 9th Author's Affiliation | () |
| 10th Author's Name | |
| 10th Author's Affiliation | () |
| 11th Author's Name | |
| 11th Author's Affiliation | () |
| 12th Author's Name | |
| 12th Author's Affiliation | () |
| 13th Author's Name | |
| 13th Author's Affiliation | () |
| 14th Author's Name | |
| 14th Author's Affiliation | () |
| 15th Author's Name | |
| 15th Author's Affiliation | () |
| 16th Author's Name | |
| 16th Author's Affiliation | () |
| 17th Author's Name | |
| 17th Author's Affiliation | () |
| 18th Author's Name | |
| 18th Author's Affiliation | () |
| Speaker | Author-4 |
| Date Time | 2022-09-14 10:00:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2022-10 |
| Volume (vol) | vol.122 |
| Number (no) | no.181 |
| Page | pp.1-6 |
| #Pages | 6 |
| Date of Issue | 2022-09-07 (PRMU) |
[Return to Top Page]