| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2023-04-21 |
| タイトル |
|
|
タイトル |
On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源 |
http://purl.org/coar/resource_type/c_6501 |
|
タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
Abhijeet Ravankar
Ankit A. Ravankar
Yohei Hoshino
Yukinori Kobayashi
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| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
nformation sharing is a powerful feature of multi-robot systems. Sharing information precisely and accurately is important and has many benefits. Particularly, smart information sharing can improve robot path planning. If a robot finds a new obstacle or blocked path, it can share this information with other remote robots allowing them to plan better paths. However, there are two problems with such information sharing. First, the maps of the robots may be different in nature (e.g., 2D grid-map, 3D semantic map, feature map etc.) as the sensors used by the robots for mapping and localization may be different. Even the maps generated using the same sensor (e.g., Lidar) can vary in scale or rotation and the sensors used might have different specifications like resolution or range. In such scenarios, the ‘correspondence problem’ in different maps is a critical bottleneck in information sharing. Second, the transience of the obstacles has to be considered while also considering the positional uncertainty of the new obstacles while sharing information. In our previous work, we proposed a ‘node-map’ with a confidence decay mechanism to solve this problem. However, the previous work had many limitations due to the decoupling of new obstacle’s positional uncertainty and confidence decay. Moreover, the previous work applied only to homogeneous maps. In addition, the previous model worked only with static obstacles in the environment. The current work extends our previous work in three main ways: (1) we extend the previous work by integrating positional uncertainty in the confidence decay mechanism and mathematically model the transience of newly added or removed obstacles and discuss its merits; (2) we extend the previous work by considering information sharing in heterogeneous maps build using different sensors; and (3) we consider dynamic obstacles like moving people in the environment and test the proposed method in complex scenarios. All the experiments are performed in real environments and with actual robots and results are discussed. |
|
言語 |
en |
| 書誌情報 |
en : applied sciences
巻 9,
号 13,
p. 2753,
発行日 2019-07
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| DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3390/app9132753 |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| 著者版フラグ |
|
|
言語 |
en |
|
値 |
publisher |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
applsci-09-02753.pdf (13.8 MB)
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