Motor-Level N-MPC for Cooperative Active Perception With Multiple Heterogeneous UAVs
This letter introduces a cooperative control framework based on Nonlinear Model Predictive Control (NMPC) for solving an Active Information Acquisition problem (AIA) using a system of multiple multirotor UAVs equipped with onboard sensors. The observation task of the NMPC is a minimum-uncertainty po...
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| Veröffentlicht in: | IEEE robotics and automation letters 2022-04, Vol.7 (2), p.2063-2070 |
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| container_title | IEEE robotics and automation letters |
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| creator | Jacquet, Martin Kivits, Max Das, Hemjyoti Franchi, Antonio |
| description | This letter introduces a cooperative control framework based on Nonlinear Model Predictive Control (NMPC) for solving an Active Information Acquisition problem (AIA) using a system of multiple multirotor UAVs equipped with onboard sensors. The observation task of the NMPC is a minimum-uncertainty pose estimation of a moving feature which is observed by the multi-UAV system, using a cooperative Kalman filter. The controller considers a full nonlinear model of the multirotors - including the motor-level actuation units and their real constraints in terms of maximum torque - and embeds the Kalman filter estimation uncertainty in its prediction. The framework allows and exploits heterogeneity in the actuation and sensing systems by considering a generic model of UAV - including both quadrotors and tilted-propeller multirotors - and a generic model of range-and-bearing sensor with arbitrary rate and field of view. The capability of the proposed framework to reduce the cooperative estimation uncertainty of a static or a moving feature, thus leading the system to optimal sensing configurations, is demonstrated through Gazebo simulations and real experiments. The software is provided open-source. |
| doi_str_mv | 10.1109/LRA.2022.3143218 |
| format | Article |
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The observation task of the NMPC is a minimum-uncertainty pose estimation of a moving feature which is observed by the multi-UAV system, using a cooperative Kalman filter. The controller considers a full nonlinear model of the multirotors - including the motor-level actuation units and their real constraints in terms of maximum torque - and embeds the Kalman filter estimation uncertainty in its prediction. The framework allows and exploits heterogeneity in the actuation and sensing systems by considering a generic model of UAV - including both quadrotors and tilted-propeller multirotors - and a generic model of range-and-bearing sensor with arbitrary rate and field of view. The capability of the proposed framework to reduce the cooperative estimation uncertainty of a static or a moving feature, thus leading the system to optimal sensing configurations, is demonstrated through Gazebo simulations and real experiments. The software is provided open-source.</description><identifier>ISSN: 2377-3766</identifier><identifier>EISSN: 2377-3766</identifier><identifier>DOI: 10.1109/LRA.2022.3143218</identifier><identifier>CODEN: IRALC6</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Actuation ; aerial systems: applications ; aerial systems: mechanics and control ; Aerial systems: perception and autonomy ; Computer Science ; Cooperative control ; Data processing ; Estimation ; Field of view ; Heterogeneity ; Image Processing ; Kalman filters ; Measurement uncertainty ; Nonlinear control ; Pose estimation ; Predictive control ; Robot sensing systems ; Robotics ; Rotary wing aircraft ; Sensors ; Systems and Control ; Task analysis ; Uncertainty ; Unmanned aerial vehicles</subject><ispartof>IEEE robotics and automation letters, 2022-04, Vol.7 (2), p.2063-2070</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c367t-efd6231c66241ad0b5ae019b2651b7e20b9caa82c6de9112186d407a878475f93</citedby><cites>FETCH-LOGICAL-c367t-efd6231c66241ad0b5ae019b2651b7e20b9caa82c6de9112186d407a878475f93</cites><orcidid>0000-0002-7016-4735 ; 0000-0001-9438-4356 ; 0000-0002-1075-3546 ; 0000-0002-5670-1282</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9682606$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,780,784,796,885,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9682606$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://laas.hal.science/hal-03482081$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Jacquet, Martin</creatorcontrib><creatorcontrib>Kivits, Max</creatorcontrib><creatorcontrib>Das, Hemjyoti</creatorcontrib><creatorcontrib>Franchi, Antonio</creatorcontrib><title>Motor-Level N-MPC for Cooperative Active Perception With Multiple Heterogeneous UAVs</title><title>IEEE robotics and automation letters</title><addtitle>LRA</addtitle><description>This letter introduces a cooperative control framework based on Nonlinear Model Predictive Control (NMPC) for solving an Active Information Acquisition problem (AIA) using a system of multiple multirotor UAVs equipped with onboard sensors. The observation task of the NMPC is a minimum-uncertainty pose estimation of a moving feature which is observed by the multi-UAV system, using a cooperative Kalman filter. The controller considers a full nonlinear model of the multirotors - including the motor-level actuation units and their real constraints in terms of maximum torque - and embeds the Kalman filter estimation uncertainty in its prediction. The framework allows and exploits heterogeneity in the actuation and sensing systems by considering a generic model of UAV - including both quadrotors and tilted-propeller multirotors - and a generic model of range-and-bearing sensor with arbitrary rate and field of view. The capability of the proposed framework to reduce the cooperative estimation uncertainty of a static or a moving feature, thus leading the system to optimal sensing configurations, is demonstrated through Gazebo simulations and real experiments. The software is provided open-source.</description><subject>Actuation</subject><subject>aerial systems: applications</subject><subject>aerial systems: mechanics and control</subject><subject>Aerial systems: perception and autonomy</subject><subject>Computer Science</subject><subject>Cooperative control</subject><subject>Data processing</subject><subject>Estimation</subject><subject>Field of view</subject><subject>Heterogeneity</subject><subject>Image Processing</subject><subject>Kalman filters</subject><subject>Measurement uncertainty</subject><subject>Nonlinear control</subject><subject>Pose estimation</subject><subject>Predictive control</subject><subject>Robot sensing systems</subject><subject>Robotics</subject><subject>Rotary wing aircraft</subject><subject>Sensors</subject><subject>Systems and Control</subject><subject>Task analysis</subject><subject>Uncertainty</subject><subject>Unmanned aerial vehicles</subject><issn>2377-3766</issn><issn>2377-3766</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkM1Lw0AQxRdRsNTeBS8BTx5S9yPZ3RxDUCukWqTV47JJJjYlduPupuB_b2pL8fSG4fceMw-ha4KnhODkPn9LpxRTOmUkYpTIMzSiTIiQCc7P_82XaOLcBmNMYipYEo_Qcm68sWEOO2iDl3C-yILa2CAzpgOrfbODIC3_ZAG2hM43Zht8NH4dzPvWN10LwQw8WPMJWzC9C1bpu7tCF7VuHUyOOkarx4dlNgvz16fnLM3DknHhQ6grThkpOacR0RUuYg2YJAXlMSkEUFwkpdaSlryChJDhL15FWGgpZCTiOmFjdHfIXetWdbb50vZHGd2oWZqr_Q6zSFIsyY4N7O2B7az57sF5tTG93Q7nKcoplQwLLgYKH6jSGucs1KdYgtW-ajVUrfZVq2PVg-XmYGkA4IQnXFKOOfsFte93VQ</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Jacquet, Martin</creator><creator>Kivits, Max</creator><creator>Das, Hemjyoti</creator><creator>Franchi, Antonio</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-7016-4735</orcidid><orcidid>https://orcid.org/0000-0001-9438-4356</orcidid><orcidid>https://orcid.org/0000-0002-1075-3546</orcidid><orcidid>https://orcid.org/0000-0002-5670-1282</orcidid></search><sort><creationdate>20220401</creationdate><title>Motor-Level N-MPC for Cooperative Active Perception With Multiple Heterogeneous UAVs</title><author>Jacquet, Martin ; Kivits, Max ; Das, Hemjyoti ; Franchi, Antonio</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-efd6231c66241ad0b5ae019b2651b7e20b9caa82c6de9112186d407a878475f93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Actuation</topic><topic>aerial systems: applications</topic><topic>aerial systems: mechanics and control</topic><topic>Aerial systems: perception and autonomy</topic><topic>Computer Science</topic><topic>Cooperative control</topic><topic>Data processing</topic><topic>Estimation</topic><topic>Field of view</topic><topic>Heterogeneity</topic><topic>Image Processing</topic><topic>Kalman filters</topic><topic>Measurement uncertainty</topic><topic>Nonlinear control</topic><topic>Pose estimation</topic><topic>Predictive control</topic><topic>Robot sensing systems</topic><topic>Robotics</topic><topic>Rotary wing aircraft</topic><topic>Sensors</topic><topic>Systems and Control</topic><topic>Task analysis</topic><topic>Uncertainty</topic><topic>Unmanned aerial vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jacquet, Martin</creatorcontrib><creatorcontrib>Kivits, Max</creatorcontrib><creatorcontrib>Das, Hemjyoti</creatorcontrib><creatorcontrib>Franchi, Antonio</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>IEEE robotics and automation letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jacquet, Martin</au><au>Kivits, Max</au><au>Das, Hemjyoti</au><au>Franchi, Antonio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Motor-Level N-MPC for Cooperative Active Perception With Multiple Heterogeneous UAVs</atitle><jtitle>IEEE robotics and automation letters</jtitle><stitle>LRA</stitle><date>2022-04-01</date><risdate>2022</risdate><volume>7</volume><issue>2</issue><spage>2063</spage><epage>2070</epage><pages>2063-2070</pages><issn>2377-3766</issn><eissn>2377-3766</eissn><coden>IRALC6</coden><abstract>This letter introduces a cooperative control framework based on Nonlinear Model Predictive Control (NMPC) for solving an Active Information Acquisition problem (AIA) using a system of multiple multirotor UAVs equipped with onboard sensors. 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| subjects | Actuation aerial systems: applications aerial systems: mechanics and control Aerial systems: perception and autonomy Computer Science Cooperative control Data processing Estimation Field of view Heterogeneity Image Processing Kalman filters Measurement uncertainty Nonlinear control Pose estimation Predictive control Robot sensing systems Robotics Rotary wing aircraft Sensors Systems and Control Task analysis Uncertainty Unmanned aerial vehicles |
| title | Motor-Level N-MPC for Cooperative Active Perception With Multiple Heterogeneous UAVs |
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