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Adaptive-Repetitive Visual-Servo Control of Low-Flying Aerial Robots via Uncalibrated High-Flying Cameras
This paper presents the design and implementation of an adaptive-repetitive visual-servo control system for a moving high-flying vehicle (HFV) with an uncalibrated camera to monitor, track, and precisely control the movements of a low-flying vehicle (LFV) or mobile ground robot. Applications of this...
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Published in: | Journal of nonlinear science 2017-08, Vol.27 (4), p.1235-1256 |
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description | This paper presents the design and implementation of an adaptive-repetitive visual-servo control system for a moving high-flying vehicle (HFV) with an uncalibrated camera to monitor, track, and precisely control the movements of a low-flying vehicle (LFV) or mobile ground robot. Applications of this control strategy include the use of high-flying unmanned aerial vehicles (UAVs) with computer vision for monitoring, controlling, and coordinating the movements of lower altitude agents in areas, for example, where GPS signals may be unreliable or nonexistent. When deployed, a remote operator of the HFV defines the desired trajectory for the LFV in the HFV’s camera frame. Due to the circular motion of the HFV, the resulting motion trajectory of the LFV in the image frame can be periodic in time, thus an adaptive-repetitive control system is exploited for regulation and/or trajectory tracking. The adaptive control law is able to handle uncertainties in the camera’s intrinsic and extrinsic parameters. The design and stability analysis of the closed-loop control system is presented, where Lyapunov stability is shown. Simulation and experimental results are presented to demonstrate the effectiveness of the method for controlling the movement of a low-flying quadcopter, demonstrating the capabilities of the visual-servo control system for localization (i.e.,, motion capturing) and trajectory tracking control. In fact, results show that the LFV can be commanded to hover in place as well as track a user-defined flower-shaped closed trajectory, while the HFV and camera system circulates above with constant angular velocity. On average, the proposed adaptive-repetitive visual-servo control system reduces the average RMS tracking error by over 77% in the image plane and over 71% in the world frame compared to using just the adaptive visual-servo control law. |
doi_str_mv | 10.1007/s00332-017-9377-2 |
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Applications of this control strategy include the use of high-flying unmanned aerial vehicles (UAVs) with computer vision for monitoring, controlling, and coordinating the movements of lower altitude agents in areas, for example, where GPS signals may be unreliable or nonexistent. When deployed, a remote operator of the HFV defines the desired trajectory for the LFV in the HFV’s camera frame. Due to the circular motion of the HFV, the resulting motion trajectory of the LFV in the image frame can be periodic in time, thus an adaptive-repetitive control system is exploited for regulation and/or trajectory tracking. The adaptive control law is able to handle uncertainties in the camera’s intrinsic and extrinsic parameters. The design and stability analysis of the closed-loop control system is presented, where Lyapunov stability is shown. Simulation and experimental results are presented to demonstrate the effectiveness of the method for controlling the movement of a low-flying quadcopter, demonstrating the capabilities of the visual-servo control system for localization (i.e.,, motion capturing) and trajectory tracking control. In fact, results show that the LFV can be commanded to hover in place as well as track a user-defined flower-shaped closed trajectory, while the HFV and camera system circulates above with constant angular velocity. On average, the proposed adaptive-repetitive visual-servo control system reduces the average RMS tracking error by over 77% in the image plane and over 71% in the world frame compared to using just the adaptive visual-servo control law.</description><identifier>ISSN: 0938-8974</identifier><identifier>EISSN: 1432-1467</identifier><identifier>DOI: 10.1007/s00332-017-9377-2</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Adaptive control ; Altitude ; Analysis ; Angular velocity ; Cameras ; Circularity ; Classical Mechanics ; Computer simulation ; Computer vision ; Control stability ; Control systems ; Design engineering ; Design parameters ; Economic Theory/Quantitative Economics/Mathematical Methods ; Flight ; Laws ; Mathematical and Computational Engineering ; Mathematical and Computational Physics ; Mathematics ; Mathematics and Statistics ; Movement ; Parameter uncertainty ; Position (location) ; Repetitive control ; Robot control ; Servocontrol ; Stability analysis ; Theoretical ; Tracking ; Trajectories ; Unmanned aerial vehicles ; Visual flight</subject><ispartof>Journal of nonlinear science, 2017-08, Vol.27 (4), p.1235-1256</ispartof><rights>Springer Science+Business Media New York 2017</rights><rights>Copyright Springer Science & Business Media 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-5601ae2312dccdb3d0a498ec3516c1d15e8b27e271f77b31fd28d8edb1853fc23</citedby><cites>FETCH-LOGICAL-c316t-5601ae2312dccdb3d0a498ec3516c1d15e8b27e271f77b31fd28d8edb1853fc23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Guo, Dejun</creatorcontrib><creatorcontrib>Bourne, Joseph R.</creatorcontrib><creatorcontrib>Wang, Hesheng</creatorcontrib><creatorcontrib>Yim, Woosoon</creatorcontrib><creatorcontrib>Leang, Kam K.</creatorcontrib><title>Adaptive-Repetitive Visual-Servo Control of Low-Flying Aerial Robots via Uncalibrated High-Flying Cameras</title><title>Journal of nonlinear science</title><addtitle>J Nonlinear Sci</addtitle><description>This paper presents the design and implementation of an adaptive-repetitive visual-servo control system for a moving high-flying vehicle (HFV) with an uncalibrated camera to monitor, track, and precisely control the movements of a low-flying vehicle (LFV) or mobile ground robot. Applications of this control strategy include the use of high-flying unmanned aerial vehicles (UAVs) with computer vision for monitoring, controlling, and coordinating the movements of lower altitude agents in areas, for example, where GPS signals may be unreliable or nonexistent. When deployed, a remote operator of the HFV defines the desired trajectory for the LFV in the HFV’s camera frame. Due to the circular motion of the HFV, the resulting motion trajectory of the LFV in the image frame can be periodic in time, thus an adaptive-repetitive control system is exploited for regulation and/or trajectory tracking. The adaptive control law is able to handle uncertainties in the camera’s intrinsic and extrinsic parameters. The design and stability analysis of the closed-loop control system is presented, where Lyapunov stability is shown. Simulation and experimental results are presented to demonstrate the effectiveness of the method for controlling the movement of a low-flying quadcopter, demonstrating the capabilities of the visual-servo control system for localization (i.e.,, motion capturing) and trajectory tracking control. In fact, results show that the LFV can be commanded to hover in place as well as track a user-defined flower-shaped closed trajectory, while the HFV and camera system circulates above with constant angular velocity. On average, the proposed adaptive-repetitive visual-servo control system reduces the average RMS tracking error by over 77% in the image plane and over 71% in the world frame compared to using just the adaptive visual-servo control law.</description><subject>Adaptive control</subject><subject>Altitude</subject><subject>Analysis</subject><subject>Angular velocity</subject><subject>Cameras</subject><subject>Circularity</subject><subject>Classical Mechanics</subject><subject>Computer simulation</subject><subject>Computer vision</subject><subject>Control stability</subject><subject>Control systems</subject><subject>Design engineering</subject><subject>Design parameters</subject><subject>Economic Theory/Quantitative Economics/Mathematical Methods</subject><subject>Flight</subject><subject>Laws</subject><subject>Mathematical and Computational Engineering</subject><subject>Mathematical and Computational Physics</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Movement</subject><subject>Parameter uncertainty</subject><subject>Position (location)</subject><subject>Repetitive control</subject><subject>Robot control</subject><subject>Servocontrol</subject><subject>Stability analysis</subject><subject>Theoretical</subject><subject>Tracking</subject><subject>Trajectories</subject><subject>Unmanned aerial vehicles</subject><subject>Visual flight</subject><issn>0938-8974</issn><issn>1432-1467</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp1kNFKwzAUhoMoOKcP4F3A62hO0jbt5RjOCQNhOm9DmqQzo2tq0k329rZUwRuvzuHw_f-BD6FboPdAqXiIlHLOCAVBCi4EYWdoAkl_gSQT52hCC56TvBDJJbqKcUd7MOVsgtzMqLZzR0vWtrWdG1b87uJB1eTVhqPHc990wdfYV3jlv8iiPrlmi2c2OFXjtS99F_HRKbxptKpdGVRnDV667ccvOld7G1S8RheVqqO9-ZlTtFk8vs2XZPXy9DyfrYjmkHUkzSgoyzgwo7UpuaEqKXKreQqZBgOpzUsmLBNQCVFyqAzLTW5NCXnKK834FN2NvW3wnwcbO7nzh9D0LyUUkPYiWDFQMFI6-BiDrWQb3F6FkwQqB6NyNCp7UXIwKocMGzOxZ5utDX-a_w19AwlgeVo</recordid><startdate>20170801</startdate><enddate>20170801</enddate><creator>Guo, Dejun</creator><creator>Bourne, Joseph R.</creator><creator>Wang, Hesheng</creator><creator>Yim, Woosoon</creator><creator>Leang, Kam K.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20170801</creationdate><title>Adaptive-Repetitive Visual-Servo Control of Low-Flying Aerial Robots via Uncalibrated High-Flying Cameras</title><author>Guo, Dejun ; Bourne, Joseph R. ; Wang, Hesheng ; Yim, Woosoon ; Leang, Kam K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-5601ae2312dccdb3d0a498ec3516c1d15e8b27e271f77b31fd28d8edb1853fc23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adaptive control</topic><topic>Altitude</topic><topic>Analysis</topic><topic>Angular velocity</topic><topic>Cameras</topic><topic>Circularity</topic><topic>Classical Mechanics</topic><topic>Computer simulation</topic><topic>Computer vision</topic><topic>Control stability</topic><topic>Control systems</topic><topic>Design engineering</topic><topic>Design parameters</topic><topic>Economic Theory/Quantitative Economics/Mathematical Methods</topic><topic>Flight</topic><topic>Laws</topic><topic>Mathematical and Computational Engineering</topic><topic>Mathematical and Computational Physics</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Movement</topic><topic>Parameter uncertainty</topic><topic>Position (location)</topic><topic>Repetitive control</topic><topic>Robot control</topic><topic>Servocontrol</topic><topic>Stability analysis</topic><topic>Theoretical</topic><topic>Tracking</topic><topic>Trajectories</topic><topic>Unmanned aerial vehicles</topic><topic>Visual flight</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guo, Dejun</creatorcontrib><creatorcontrib>Bourne, Joseph R.</creatorcontrib><creatorcontrib>Wang, Hesheng</creatorcontrib><creatorcontrib>Yim, Woosoon</creatorcontrib><creatorcontrib>Leang, Kam K.</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of nonlinear science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guo, Dejun</au><au>Bourne, Joseph R.</au><au>Wang, Hesheng</au><au>Yim, Woosoon</au><au>Leang, Kam K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive-Repetitive Visual-Servo Control of Low-Flying Aerial Robots via Uncalibrated High-Flying Cameras</atitle><jtitle>Journal of nonlinear science</jtitle><stitle>J Nonlinear Sci</stitle><date>2017-08-01</date><risdate>2017</risdate><volume>27</volume><issue>4</issue><spage>1235</spage><epage>1256</epage><pages>1235-1256</pages><issn>0938-8974</issn><eissn>1432-1467</eissn><abstract>This paper presents the design and implementation of an adaptive-repetitive visual-servo control system for a moving high-flying vehicle (HFV) with an uncalibrated camera to monitor, track, and precisely control the movements of a low-flying vehicle (LFV) or mobile ground robot. Applications of this control strategy include the use of high-flying unmanned aerial vehicles (UAVs) with computer vision for monitoring, controlling, and coordinating the movements of lower altitude agents in areas, for example, where GPS signals may be unreliable or nonexistent. When deployed, a remote operator of the HFV defines the desired trajectory for the LFV in the HFV’s camera frame. Due to the circular motion of the HFV, the resulting motion trajectory of the LFV in the image frame can be periodic in time, thus an adaptive-repetitive control system is exploited for regulation and/or trajectory tracking. The adaptive control law is able to handle uncertainties in the camera’s intrinsic and extrinsic parameters. The design and stability analysis of the closed-loop control system is presented, where Lyapunov stability is shown. Simulation and experimental results are presented to demonstrate the effectiveness of the method for controlling the movement of a low-flying quadcopter, demonstrating the capabilities of the visual-servo control system for localization (i.e.,, motion capturing) and trajectory tracking control. In fact, results show that the LFV can be commanded to hover in place as well as track a user-defined flower-shaped closed trajectory, while the HFV and camera system circulates above with constant angular velocity. On average, the proposed adaptive-repetitive visual-servo control system reduces the average RMS tracking error by over 77% in the image plane and over 71% in the world frame compared to using just the adaptive visual-servo control law.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s00332-017-9377-2</doi><tpages>22</tpages></addata></record> |
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subjects | Adaptive control Altitude Analysis Angular velocity Cameras Circularity Classical Mechanics Computer simulation Computer vision Control stability Control systems Design engineering Design parameters Economic Theory/Quantitative Economics/Mathematical Methods Flight Laws Mathematical and Computational Engineering Mathematical and Computational Physics Mathematics Mathematics and Statistics Movement Parameter uncertainty Position (location) Repetitive control Robot control Servocontrol Stability analysis Theoretical Tracking Trajectories Unmanned aerial vehicles Visual flight |
title | Adaptive-Repetitive Visual-Servo Control of Low-Flying Aerial Robots via Uncalibrated High-Flying Cameras |
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