Enhanced geometry control Powered by AI for UAVS with a robotic arm for compensating for disturbances

Enhanced geometry control Powered by AI for UAVS with a robotic arm for compensating for disturbances | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enhanced geometry control Powered by AI for UAVS with a robotic arm for compensating for disturbances Khaled Oqda, Eman M. El-Gendy, Hanaa Salem Marie, Mohamed Akalla This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9097438/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Unmanned Aerial Vehicles (UAVs) equipped with robotic manipulators have emerged as a powerful paradigm for advanced aerial manipulation tasks, including infrastructure inspection, emergency response, and precise object handling in hazardous or inaccessible environments. Despite their potential, integrating robotic arms introduces significant challenges due to strong dynamic coupling, time-varying payloads, and external disturbances such as wind and aerodynamic turbulence, which can severely compromise flight stability and control performance. Conventional linear and nonlinear control strategies often struggle to cope with these highly nonlinear dynamics, particularly during aggressive manipulator motions. This paper proposes an AI-enhanced geometric control framework for stabilising UAV–manipulator systems under high-manoeuvrability conditions. By operating directly on the nonlinear configuration space, geometric control provides robust attitude and position stabilization while avoiding singularities and ensuring global stability. To overcome the limited adaptability of purely geometric controllers, artificial intelligence algorithms are integrated to predict and compensate for manipulator-induced disturbances, optimize thrust distribution, and dynamically adjust BLDC motor currents in real time. A Long Short-Term Memory (LSTM) network predicts disturbances that will next occur based on previous values and early information about the commanded trajectory. A Kalman Filter–Neural Fusion layer enforces probabilistic behaviours on the outputs of the LSTM to preserve stability in the control loop, combine filtered and prediction information, and minimise uncertainty on the overall estimation process. A quadrotor UAV equipped with a Cartesian robotic arm employs the proposed framework for compensation of disturbances affecting both position and orientation. Simulation results obtained using a high-fidelity CoppeliaSim–MATLAB environment demonstrate the effectiveness of the proposed approach. The UAV maintains stable flight and accurate attitude regulation while carrying relatively high payloads and executing rapid robotic arm maneuvers. Notably, the proposed controller successfully preserves the UAV’s tilt angle within safe limits during aggressive manipulation scenarios, significantly reducing attitude oscillations and stabilization time compared to conventional PID and standard geometric control schemes. These results confirm that the proposed (AI)-driven geometric control strategy achieves robust dynamic balance and enhanced disturbance rejection, enabling reliable and intelligent aerial manipulation in complex and disturbance-rich operational environments. Physical sciences/Engineering Physical sciences/Mathematics and computing UAVs geometry control robotic arm machine learning CoppeliaSim compensating Disturbance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor invited by journal 18 Mar, 2026 Editor assigned by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9097438","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":609672270,"identity":"ba518c81-528d-4c4b-a778-b348336b6ff2","order_by":0,"name":"Khaled Oqda","email":"data:image/png;base64,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","orcid":"","institution":"Mansoura University","correspondingAuthor":true,"prefix":"","firstName":"Khaled","middleName":"","lastName":"Oqda","suffix":""},{"id":609672271,"identity":"f38804a7-15a2-4443-8cba-f1648f9937b9","order_by":1,"name":"Eman M. 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Despite their potential, integrating robotic arms introduces significant challenges due to strong dynamic coupling, time-varying payloads, and external disturbances such as wind and aerodynamic turbulence, which can severely compromise flight stability and control performance. Conventional linear and nonlinear control strategies often struggle to cope with these highly nonlinear dynamics, particularly during aggressive manipulator motions.\u003c/p\u003e \u003cp\u003eThis paper proposes an AI-enhanced geometric control framework for stabilising UAV\u0026ndash;manipulator systems under high-manoeuvrability conditions. By operating directly on the nonlinear configuration space, geometric control provides robust attitude and position stabilization while avoiding singularities and ensuring global stability. To overcome the limited adaptability of purely geometric controllers, artificial intelligence algorithms are integrated to predict and compensate for manipulator-induced disturbances, optimize thrust distribution, and dynamically adjust BLDC motor currents in real time. A Long Short-Term Memory (LSTM) network predicts disturbances that will next occur based on previous values and early information about the commanded trajectory. A Kalman Filter\u0026ndash;Neural Fusion layer enforces probabilistic behaviours on the outputs of the LSTM to preserve stability in the control loop, combine filtered and prediction information, and minimise uncertainty on the overall estimation process.\u003c/p\u003e \u003cp\u003eA quadrotor UAV equipped with a Cartesian robotic arm employs the proposed framework for compensation of disturbances affecting both position and orientation. Simulation results obtained using a high-fidelity CoppeliaSim\u0026ndash;MATLAB environment demonstrate the effectiveness of the proposed approach. The UAV maintains stable flight and accurate attitude regulation while carrying relatively high payloads and executing rapid robotic arm maneuvers. Notably, the proposed controller successfully preserves the UAV\u0026rsquo;s tilt angle within safe limits during aggressive manipulation scenarios, significantly reducing attitude oscillations and stabilization time compared to conventional PID and standard geometric control schemes. These results confirm that the proposed (AI)-driven geometric control strategy achieves robust dynamic balance and enhanced disturbance rejection, enabling reliable and intelligent aerial manipulation in complex and disturbance-rich operational environments.\u003c/p\u003e","manuscriptTitle":"Enhanced geometry control Powered by AI for UAVS with a robotic arm for compensating for disturbances","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 04:38:27","doi":"10.21203/rs.3.rs-9097438/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-20T21:49:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63826597661896634863782592500797657107","date":"2026-03-19T11:37:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T11:08:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-18T15:12:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-12T14:12:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T14:12:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-11T18:21:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"77bc5068-db38-4ad3-bbf1-55a28c3e998e","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64876957,"name":"Physical sciences/Engineering"},{"id":64876958,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-30T18:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 04:38:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9097438","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9097438","identity":"rs-9097438","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}