Single unmanned aerial vehicle autonomous motion planning method based on imitation learning

The invention provides a single unmanned aerial vehicle autonomous motion planning method based on imitation learning. A simulation environment is constructed, and a corresponding virtual unmanned aerial vehicle model is constructed based on a real unmanned aerial vehicle in the simulation environme...

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Hauptverfasser:	HOU ZIYUE, ZHANG LONGYUAN, LI WEI, LIU ZI'ANG, WANG JI
Format:	Patent
Sprache:	chi ; eng
Schlagworte:	CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING GYROSCOPIC INSTRUMENTS MEASURING MEASURING DISTANCES, LEVELS OR BEARINGS NAVIGATION PHOTOGRAMMETRY OR VIDEOGRAMMETRY PHYSICS SURVEYING TESTING
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creator	HOU ZIYUE ZHANG LONGYUAN LI WEI LIU ZI'ANG WANG JI
description	The invention provides a single unmanned aerial vehicle autonomous motion planning method based on imitation learning. A simulation environment is constructed, and a corresponding virtual unmanned aerial vehicle model is constructed based on a real unmanned aerial vehicle in the simulation environment, and the method comprises the following steps: S1, obtaining a training sample for trajectory planning of the virtual unmanned aerial vehicle based on the simulation environment; s2, training a neural network of the real unmanned aerial vehicle through the training sample; s3, collecting real-time environment sensing data and state data of the real unmanned aerial vehicle through a sensor of the real unmanned aerial vehicle; s4, generating a real-time prediction track point sequence based on the real-time environment sensing data and the real unmanned aerial vehicle state data by adopting a neural network in combination with the target point data; s5, converting the real-time prediction track point sequence into
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A simulation environment is constructed, and a corresponding virtual unmanned aerial vehicle model is constructed based on a real unmanned aerial vehicle in the simulation environment, and the method comprises the following steps: S1, obtaining a training sample for trajectory planning of the virtual unmanned aerial vehicle based on the simulation environment; s2, training a neural network of the real unmanned aerial vehicle through the training sample; s3, collecting real-time environment sensing data and state data of the real unmanned aerial vehicle through a sensor of the real unmanned aerial vehicle; s4, generating a real-time prediction track point sequence based on the real-time environment sensing data and the real unmanned aerial vehicle state data by adopting a neural network in combination with the target point data; s5, converting the real-time prediction track point sequence into</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; GYROSCOPIC INSTRUMENTS ; MEASURING ; MEASURING DISTANCES, LEVELS OR BEARINGS ; NAVIGATION ; PHOTOGRAMMETRY OR VIDEOGRAMMETRY ; PHYSICS ; SURVEYING ; TESTING</subject><creationdate>2023</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=20230725&DB=EPODOC&CC=CN&NR=116481532A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230725&DB=EPODOC&CC=CN&NR=116481532A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HOU ZIYUE</creatorcontrib><creatorcontrib>ZHANG LONGYUAN</creatorcontrib><creatorcontrib>LI WEI</creatorcontrib><creatorcontrib>LIU ZI'ANG</creatorcontrib><creatorcontrib>WANG JI</creatorcontrib><title>Single unmanned aerial vehicle autonomous motion planning method based on imitation learning</title><description>The invention provides a single unmanned aerial vehicle autonomous motion planning method based on imitation learning. 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subjects	CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING GYROSCOPIC INSTRUMENTS MEASURING MEASURING DISTANCES, LEVELS OR BEARINGS NAVIGATION PHOTOGRAMMETRY OR VIDEOGRAMMETRY PHYSICS SURVEYING TESTING
title	Single unmanned aerial vehicle autonomous motion planning method based on imitation learning
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