Unmanned aerial vehicle navigation and path planning method based on deep learning
The invention relates to the technical field of unmanned aerial vehicle navigation and path planning, and discloses an unmanned aerial vehicle navigation and path planning method based on deep learning, and the method comprises the following steps: S1, introducing a virtual obstacle in an unmanned a...
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Patent |
| Sprache: | chi ; eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | MO QIONGYU HUANG ZAN XIAO SHUIQING TAN DAWANG ZHANG LEYI XIA XIAOQUN SHI XUDONG GONG MANFENG |
| description | The invention relates to the technical field of unmanned aerial vehicle navigation and path planning, and discloses an unmanned aerial vehicle navigation and path planning method based on deep learning, and the method comprises the following steps: S1, introducing a virtual obstacle in an unmanned aerial vehicle flight environment, and considering the dynamics, diversity and complexity of the obstacle; s2, generating an enhanced data set according to the virtual obstacle, and considering the diversity, balance and robustness of the data set; and S3, collecting sensor data of the flight environment of the unmanned aerial vehicle, including image data, laser radar data, inertial measurement unit data and global positioning system data, and considering fusion of multi-source data. By introducing the virtual obstacle and considering the dynamic nature, diversity and complexity of the virtual obstacle, a real flight environment can be simulated more accurately, and the robustness and safety of path planning are im |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN118565476A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN118565476A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN118565476A3</originalsourceid><addsrcrecordid>eNqNy7EOgjAUheEuDgZ9h-sDMBAFXQ3RODkYncmVHmiTctvQhue3Jj6A0xn-76zV4yUTi0ATY7bsaIGxvQMJL3bkZL0Qi6bAyVBwmVoZaUIyXtObYz5moYFADjx_60atBnYR298Wane9PNtbieA7xMA9BKlr71V1qpv6cGzO-3_MBwGGN-U</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Unmanned aerial vehicle navigation and path planning method based on deep learning</title><source>esp@cenet</source><creator>MO QIONGYU ; HUANG ZAN ; XIAO SHUIQING ; TAN DAWANG ; ZHANG LEYI ; XIA XIAOQUN ; SHI XUDONG ; GONG MANFENG</creator><creatorcontrib>MO QIONGYU ; HUANG ZAN ; XIAO SHUIQING ; TAN DAWANG ; ZHANG LEYI ; XIA XIAOQUN ; SHI XUDONG ; GONG MANFENG</creatorcontrib><description>The invention relates to the technical field of unmanned aerial vehicle navigation and path planning, and discloses an unmanned aerial vehicle navigation and path planning method based on deep learning, and the method comprises the following steps: S1, introducing a virtual obstacle in an unmanned aerial vehicle flight environment, and considering the dynamics, diversity and complexity of the obstacle; s2, generating an enhanced data set according to the virtual obstacle, and considering the diversity, balance and robustness of the data set; and S3, collecting sensor data of the flight environment of the unmanned aerial vehicle, including image data, laser radar data, inertial measurement unit data and global positioning system data, and considering fusion of multi-source data. By introducing the virtual obstacle and considering the dynamic nature, diversity and complexity of the virtual obstacle, a real flight environment can be simulated more accurately, and the robustness and safety of path planning are im</description><language>chi ; eng</language><subject>ANALOGOUS ARRANGEMENTS USING OTHER WAVES ; CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES ; ELECTRIC DIGITAL DATA PROCESSING ; GYROSCOPIC INSTRUMENTS ; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION ORRERADIATION OF RADIO WAVES ; MEASURING ; MEASURING DISTANCES, LEVELS OR BEARINGS ; NAVIGATION ; PHOTOGRAMMETRY OR VIDEOGRAMMETRY ; PHYSICS ; RADIO DIRECTION-FINDING ; RADIO NAVIGATION ; SURVEYING ; TESTING</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240830&DB=EPODOC&CC=CN&NR=118565476A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240830&DB=EPODOC&CC=CN&NR=118565476A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>MO QIONGYU</creatorcontrib><creatorcontrib>HUANG ZAN</creatorcontrib><creatorcontrib>XIAO SHUIQING</creatorcontrib><creatorcontrib>TAN DAWANG</creatorcontrib><creatorcontrib>ZHANG LEYI</creatorcontrib><creatorcontrib>XIA XIAOQUN</creatorcontrib><creatorcontrib>SHI XUDONG</creatorcontrib><creatorcontrib>GONG MANFENG</creatorcontrib><title>Unmanned aerial vehicle navigation and path planning method based on deep learning</title><description>The invention relates to the technical field of unmanned aerial vehicle navigation and path planning, and discloses an unmanned aerial vehicle navigation and path planning method based on deep learning, and the method comprises the following steps: S1, introducing a virtual obstacle in an unmanned aerial vehicle flight environment, and considering the dynamics, diversity and complexity of the obstacle; s2, generating an enhanced data set according to the virtual obstacle, and considering the diversity, balance and robustness of the data set; and S3, collecting sensor data of the flight environment of the unmanned aerial vehicle, including image data, laser radar data, inertial measurement unit data and global positioning system data, and considering fusion of multi-source data. By introducing the virtual obstacle and considering the dynamic nature, diversity and complexity of the virtual obstacle, a real flight environment can be simulated more accurately, and the robustness and safety of path planning are im</description><subject>ANALOGOUS ARRANGEMENTS USING OTHER WAVES</subject><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>GYROSCOPIC INSTRUMENTS</subject><subject>LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION ORRERADIATION OF RADIO WAVES</subject><subject>MEASURING</subject><subject>MEASURING DISTANCES, LEVELS OR BEARINGS</subject><subject>NAVIGATION</subject><subject>PHOTOGRAMMETRY OR VIDEOGRAMMETRY</subject><subject>PHYSICS</subject><subject>RADIO DIRECTION-FINDING</subject><subject>RADIO NAVIGATION</subject><subject>SURVEYING</subject><subject>TESTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNy7EOgjAUheEuDgZ9h-sDMBAFXQ3RODkYncmVHmiTctvQhue3Jj6A0xn-76zV4yUTi0ATY7bsaIGxvQMJL3bkZL0Qi6bAyVBwmVoZaUIyXtObYz5moYFADjx_60atBnYR298Wane9PNtbieA7xMA9BKlr71V1qpv6cGzO-3_MBwGGN-U</recordid><startdate>20240830</startdate><enddate>20240830</enddate><creator>MO QIONGYU</creator><creator>HUANG ZAN</creator><creator>XIAO SHUIQING</creator><creator>TAN DAWANG</creator><creator>ZHANG LEYI</creator><creator>XIA XIAOQUN</creator><creator>SHI XUDONG</creator><creator>GONG MANFENG</creator><scope>EVB</scope></search><sort><creationdate>20240830</creationdate><title>Unmanned aerial vehicle navigation and path planning method based on deep learning</title><author>MO QIONGYU ; HUANG ZAN ; XIAO SHUIQING ; TAN DAWANG ; ZHANG LEYI ; XIA XIAOQUN ; SHI XUDONG ; GONG MANFENG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118565476A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>ANALOGOUS ARRANGEMENTS USING OTHER WAVES</topic><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>GYROSCOPIC INSTRUMENTS</topic><topic>LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION ORRERADIATION OF RADIO WAVES</topic><topic>MEASURING</topic><topic>MEASURING DISTANCES, LEVELS OR BEARINGS</topic><topic>NAVIGATION</topic><topic>PHOTOGRAMMETRY OR VIDEOGRAMMETRY</topic><topic>PHYSICS</topic><topic>RADIO DIRECTION-FINDING</topic><topic>RADIO NAVIGATION</topic><topic>SURVEYING</topic><topic>TESTING</topic><toplevel>online_resources</toplevel><creatorcontrib>MO QIONGYU</creatorcontrib><creatorcontrib>HUANG ZAN</creatorcontrib><creatorcontrib>XIAO SHUIQING</creatorcontrib><creatorcontrib>TAN DAWANG</creatorcontrib><creatorcontrib>ZHANG LEYI</creatorcontrib><creatorcontrib>XIA XIAOQUN</creatorcontrib><creatorcontrib>SHI XUDONG</creatorcontrib><creatorcontrib>GONG MANFENG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>MO QIONGYU</au><au>HUANG ZAN</au><au>XIAO SHUIQING</au><au>TAN DAWANG</au><au>ZHANG LEYI</au><au>XIA XIAOQUN</au><au>SHI XUDONG</au><au>GONG MANFENG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Unmanned aerial vehicle navigation and path planning method based on deep learning</title><date>2024-08-30</date><risdate>2024</risdate><abstract>The invention relates to the technical field of unmanned aerial vehicle navigation and path planning, and discloses an unmanned aerial vehicle navigation and path planning method based on deep learning, and the method comprises the following steps: S1, introducing a virtual obstacle in an unmanned aerial vehicle flight environment, and considering the dynamics, diversity and complexity of the obstacle; s2, generating an enhanced data set according to the virtual obstacle, and considering the diversity, balance and robustness of the data set; and S3, collecting sensor data of the flight environment of the unmanned aerial vehicle, including image data, laser radar data, inertial measurement unit data and global positioning system data, and considering fusion of multi-source data. By introducing the virtual obstacle and considering the dynamic nature, diversity and complexity of the virtual obstacle, a real flight environment can be simulated more accurately, and the robustness and safety of path planning are im</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN118565476A |
| source | esp@cenet |
| subjects | ANALOGOUS ARRANGEMENTS USING OTHER WAVES CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES ELECTRIC DIGITAL DATA PROCESSING GYROSCOPIC INSTRUMENTS LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION ORRERADIATION OF RADIO WAVES MEASURING MEASURING DISTANCES, LEVELS OR BEARINGS NAVIGATION PHOTOGRAMMETRY OR VIDEOGRAMMETRY PHYSICS RADIO DIRECTION-FINDING RADIO NAVIGATION SURVEYING TESTING |
| title | Unmanned aerial vehicle navigation and path planning method based on deep learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T21%3A27%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=MO%20QIONGYU&rft.date=2024-08-30&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN118565476A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |