Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm
Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot's search behavior among four strategies: crosswind maneuver, Obstacle-Avoid Navigation, Vision-Based Navigation, and Olfaction-Based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-Based Navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms vision-only and olfaction-only methods, reducing average search time by 54% and 30%, respectively.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
|---|---|
| Enthalten in: |
Sensors (Basel, Switzerland) - 24(2024), 7 vom: 05. Apr. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Hassan, Sunzid [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Computer vision-based navigation |
|---|
| Anmerkungen: |
Date Revised 25.04.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3390/s24072309 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM370998316 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM370998316 | ||
| 003 | DE-627 | ||
| 005 | 20240425234201.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240413s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3390/s24072309 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1386.xml |
| 035 | |a (DE-627)NLM370998316 | ||
| 035 | |a (NLM)38610520 | ||
| 035 | |a (PII)2309 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Hassan, Sunzid |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 25.04.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot's search behavior among four strategies: crosswind maneuver, Obstacle-Avoid Navigation, Vision-Based Navigation, and Olfaction-Based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-Based Navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms vision-only and olfaction-only methods, reducing average search time by 54% and 30%, respectively | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a computer vision-based navigation | |
| 650 | 4 | |a moth-inspired algorithm | |
| 650 | 4 | |a multi-modal robotics | |
| 650 | 4 | |a odor source localization | |
| 650 | 4 | |a robot operating system | |
| 700 | 1 | |a Wang, Lingxiao |e verfasserin |4 aut | |
| 700 | 1 | |a Mahmud, Khan Raqib |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Sensors (Basel, Switzerland) |d 2007 |g 24(2024), 7 vom: 05. Apr. |w (DE-627)NLM187985170 |x 1424-8220 |7 nnns |
| 773 | 1 | 8 | |g volume:24 |g year:2024 |g number:7 |g day:05 |g month:04 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3390/s24072309 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 24 |j 2024 |e 7 |b 05 |c 04 | ||