Real-Time Perception-Limited Motion Planning Using Sampling-Based MPC
Motion planning with visual perception is a hot topic for autonomous flight of micro aerial vehicles (MAVs). However, many existing works fail to be implemented in realistic scenarios in real time due to practical constraints, such as the limited field of view (FOV) of the onboard camera and the lim...
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| Veröffentlicht in: | IEEE transactions on industrial electronics (1982) 2022-12, Vol.69 (12), p.13182-13191 |
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| container_title | IEEE transactions on industrial electronics (1982) |
| container_volume | 69 |
| creator | Lu, Hanchen Zong, Qun Lai, Shupeng Tian, Bailing Xie, Lihua |
| description | Motion planning with visual perception is a hot topic for autonomous flight of micro aerial vehicles (MAVs). However, many existing works fail to be implemented in realistic scenarios in real time due to practical constraints, such as the limited field of view (FOV) of the onboard camera and the limited computational capability. Compared to the existing methods, the proposed approach solves the optimization of motion and perception at the same time. A sampling-based model-predictive control framework is explored as a local planner to generate trajectories, which are dynamically feasible and collision-free with limited perception . The sampling-based local planning framework is extended to two independent scenarios for MAVs: 1) planning safe trajectories with limited FOV constraint and 2) planning trajectories with effective perception of the point of interest. The effectiveness of the proposed method is demonstrated through both simulation and real-flight experiments. |
| doi_str_mv | 10.1109/TIE.2022.3140533 |
| format | Article |
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(IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-f0f22ceccad84b75f9d56519d07a1a2762cb4c7469ac34e7b1ee4b7cd7043b683</citedby><cites>FETCH-LOGICAL-c291t-f0f22ceccad84b75f9d56519d07a1a2762cb4c7469ac34e7b1ee4b7cd7043b683</cites><orcidid>0000-0003-1004-8350 ; 0000-0002-7137-4136 ; 0000-0003-2597-5392 ; 0000-0002-2941-2135</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9677992$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9677992$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lu, Hanchen</creatorcontrib><creatorcontrib>Zong, Qun</creatorcontrib><creatorcontrib>Lai, Shupeng</creatorcontrib><creatorcontrib>Tian, Bailing</creatorcontrib><creatorcontrib>Xie, Lihua</creatorcontrib><title>Real-Time Perception-Limited Motion Planning Using Sampling-Based MPC</title><title>IEEE transactions on industrial electronics (1982)</title><addtitle>TIE</addtitle><description>Motion planning with visual perception is a hot topic for autonomous flight of micro aerial vehicles (MAVs). However, many existing works fail to be implemented in realistic scenarios in real time due to practical constraints, such as the limited field of view (FOV) of the onboard camera and the limited computational capability. Compared to the existing methods, the proposed approach solves the optimization of motion and perception at the same time. A sampling-based model-predictive control framework is explored as a local planner to generate trajectories, which are dynamically feasible and collision-free with limited perception . The sampling-based local planning framework is extended to two independent scenarios for MAVs: 1) planning safe trajectories with limited FOV constraint and 2) planning trajectories with effective perception of the point of interest. The effectiveness of the proposed method is demonstrated through both simulation and real-flight experiments.</description><subject>Aerial systems: perception and autonomy</subject><subject>Aerospace electronics</subject><subject>Collision avoidance</subject><subject>Collision dynamics</subject><subject>Costs</subject><subject>Field of view</subject><subject>Micro air vehicles (MAV)</subject><subject>motion and path planning</subject><subject>Motion planning</subject><subject>Optimal control</subject><subject>Optimization</subject><subject>optimization and optimal control</subject><subject>Planning</subject><subject>Predictive control</subject><subject>Real time</subject><subject>Sampling</subject><subject>Sensors</subject><subject>Stochastic processes</subject><subject>Trajectory</subject><subject>Trajectory planning</subject><subject>vision-based navigation</subject><subject>Visual flight</subject><subject>Visual perception</subject><issn>0278-0046</issn><issn>1557-9948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtLAzEQgIMoWKt3wcuC59RJNo_NUUvVQsWi7Tlks7OSsi8368F_7y4tXuYB38wwHyG3DBaMgXnYrVcLDpwvUiZApukZmTEpNTVGZOdkBlxnFECoS3IV4wGACcnkjKw-0FV0F2pMtth77IbQNnQT6jBgkby1U5tsK9c0oflK9nGKn67uqrGgTy5O0HZ5TS5KV0W8OeU52T-vdstXunl_WS8fN9RzwwZaQsm5R-9dkYlcy9IUUklmCtCOOa4V97nwWijjfCpQ5wxx5HyhQaS5ytI5uT_u7fr2-wfjYA_tT9-MJy1XWaaBjT-PFBwp37cx9ljarg-1638tAzvJsqMsO8myJ1njyN1xJCDiP26U1sbw9A96QWRc</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Lu, Hanchen</creator><creator>Zong, Qun</creator><creator>Lai, Shupeng</creator><creator>Tian, Bailing</creator><creator>Xie, Lihua</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, many existing works fail to be implemented in realistic scenarios in real time due to practical constraints, such as the limited field of view (FOV) of the onboard camera and the limited computational capability. Compared to the existing methods, the proposed approach solves the optimization of motion and perception at the same time. A sampling-based model-predictive control framework is explored as a local planner to generate trajectories, which are dynamically feasible and collision-free with limited perception . The sampling-based local planning framework is extended to two independent scenarios for MAVs: 1) planning safe trajectories with limited FOV constraint and 2) planning trajectories with effective perception of the point of interest. 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| ispartof | IEEE transactions on industrial electronics (1982), 2022-12, Vol.69 (12), p.13182-13191 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Aerial systems: perception and autonomy Aerospace electronics Collision avoidance Collision dynamics Costs Field of view Micro air vehicles (MAV) motion and path planning Motion planning Optimal control Optimization optimization and optimal control Planning Predictive control Real time Sampling Sensors Stochastic processes Trajectory Trajectory planning vision-based navigation Visual flight Visual perception |
| title | Real-Time Perception-Limited Motion Planning Using Sampling-Based MPC |
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