Intelligent Path and Task Planning in Robotic Manufacturing: Leveraging Artificial Intelligence for Industry 4.0

Intelligent Path and Task Planning in Robotic Manufacturing: Leveraging Artificial Intelligence for Industry 4.0 | 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 Research Article Intelligent Path and Task Planning in Robotic Manufacturing: Leveraging Artificial Intelligence for Industry 4.0 Emeka Akaezuwa, Selvaramalakshmi (alias) Prof. Lakshmi Bhabuu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8506371/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Apr, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract The smart manufacturing system enables a rapid evolution of Industry 4.0 in which the safety and efficiency are collaboratively enhanced in terms of intelligent controllers, sensors, and autonomous robots. Because of high-dimensional data and uncertainties in real-time decision making, the major challenges are to achieve an adaptive and robust path and task coordination. For autonomous robotic manufacturing, advanced Artificial Intelligence models of TemTransGIN-Planner and CQ-PathNet are leveraged to propose an intelligent task and path planning framework in this study. Initialize the proposed framework with data acquisition from the sources of EDAT24 and BridgeData V2 datasets. Ensures consistent data scaling and noise reduction in the data pre-processing module. The feature extraction module derives the features of Task, Dynamic, Kinematics, and Environmental features, which can build the robot environment interactions with its comprehensive representations. For task planning and scheduling, it proposes the Temporal Transformer Graph Isomorphism Network (TemTransGIN-Planner) for intelligent decision making. Next, incorporate Parameterized Graph Explainer (PGExplainer) to enhance interpretability and explainability, thereby elucidating the decision rationale of the graph-based planner. Under dynamic and complex manufacturing conditions, the precise and collision free trajectories are achieved by proposing CQ-PathNet. To enhance the performance over time, both TemTransGIN and CQ-PathNet, through feedback-driven learning, are continuously driven by the learning and adaptation module. An accurate and stable actuation of robotic systems is ensured via Proportional Integral Derivatives (PID). Over the traditional methods, the proposed model describes 95% resource utilization, 18.5s makespan, 0.5% collision rate, 95 m/s 2 path smoothness and 8.2J energy consumption. Robotic Manufacturing Temporal Transformer Graph Isomorphism Network Quantum Residual Neural Network Proportional Integral Derivatives and Chaotic Domestic Cattle Optimization Full Text Cite Share Download PDF Status: Published Journal Publication published 23 Apr, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 08 Feb, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers invited by journal 06 Jan, 2026 Editor assigned by journal 06 Jan, 2026 First submitted to journal 03 Jan, 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-8506371","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570241259,"identity":"ab2479c9-7941-483b-b19d-57fcfe93f90b","order_by":0,"name":"Emeka Akaezuwa","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Emeka","middleName":"","lastName":"Akaezuwa","suffix":""},{"id":570241260,"identity":"ead70cfb-0592-4002-b49e-1e308580cd86","order_by":1,"name":"Selvaramalakshmi (alias) Prof. Lakshmi Bhabuu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie3QsUrEMBjA8UAgWaJdA1WfIVCoPXyZK4W4JAW5xUG6CDcddvXAh+jU1cphJqVr9oMKUkQoOIpJz1ukPblNJH9ISaA/8rUAuFx/sEOAAASAThkg9kzNgpXdHI0RNEDQ1G7ILwRsiY2w/jlKsEq6CxGlp/hZ+QREcY6fuld9FRGAV4/FECFc+cuSziaLlBtC4+VClmdCmcEI53pwsPM5PChpXFQi7ElRyTIQyBBKwkHivVx3PanbDbmv2yYQnzsI5ZXfE729hQq4lvNdpOGWzJh+SyZ3jAa3ugmhvKEEjXyL5/HADJalrJYPur3MjvM8WXfiIzvx8EoNkR+x76s3f2aP4Ps+b7tcLte/7wtr5lpH2mzhsAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-1489-4554","institution":"International University of East Africa","correspondingAuthor":true,"prefix":"","firstName":"Selvaramalakshmi","middleName":"(alias) Prof. Lakshmi","lastName":"Bhabuu","suffix":""},{"id":570241261,"identity":"88aab759-cc3d-4691-aaab-68ecc1fd6b12","order_by":2,"name":"Barnabus Asingya","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Barnabus","middleName":"","lastName":"Asingya","suffix":""},{"id":570241262,"identity":"cff0a02d-6757-492a-a35f-dd7d5fb0507f","order_by":3,"name":"Petr Ilich Smirnov (alias) Prof. Peter I. 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Because of high-dimensional data and uncertainties in real-time decision making, the major challenges are to achieve an adaptive and robust path and task coordination. For autonomous robotic manufacturing, advanced Artificial Intelligence models of TemTransGIN-Planner and CQ-PathNet are leveraged to propose an intelligent task and path planning framework in this study. Initialize the proposed framework with data acquisition from the sources of EDAT24 and BridgeData V2 datasets. Ensures consistent data scaling and noise reduction in the data pre-processing module. The feature extraction module derives the features of Task, Dynamic, Kinematics, and Environmental features, which can build the robot environment interactions with its comprehensive representations. For task planning and scheduling, it proposes the Temporal Transformer Graph Isomorphism Network (TemTransGIN-Planner) for intelligent decision making. Next, incorporate Parameterized Graph Explainer (PGExplainer) to enhance interpretability and explainability, thereby elucidating the decision rationale of the graph-based planner. Under dynamic and complex manufacturing conditions, the precise and collision free trajectories are achieved by proposing CQ-PathNet. To enhance the performance over time, both TemTransGIN and CQ-PathNet, through feedback-driven learning, are continuously driven by the learning and adaptation module. An accurate and stable actuation of robotic systems is ensured via Proportional Integral Derivatives (PID). Over the traditional methods, the proposed model describes 95% resource utilization, 18.5s makespan, 0.5% collision rate, 95 m/s\u003csup\u003e2\u003c/sup\u003e path smoothness and 8.2J energy consumption.\u003c/p\u003e","manuscriptTitle":"Intelligent Path and Task Planning in Robotic Manufacturing: Leveraging Artificial Intelligence for Industry 4.0","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 11:01:05","doi":"10.21203/rs.3.rs-8506371/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2026-02-08T08:59:42+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-01-08T09:07:20+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-06T13:43:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-06T06:35:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2026-01-03T06:00:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3b0dc79b-20cd-48f4-ae40-9fc7b0a3eacf","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:11:51+00:00","versionOfRecord":{"articleIdentity":"rs-8506371","link":"https://doi.org/10.1007/s00170-026-17848-6","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2026-04-23 15:58:50","publishedOnDateReadable":"April 23rd, 2026"},"versionCreatedAt":"2026-01-08 11:01:05","video":"","vorDoi":"10.1007/s00170-026-17848-6","vorDoiUrl":"https://doi.org/10.1007/s00170-026-17848-6","workflowStages":[]},"version":"v1","identity":"rs-8506371","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8506371","identity":"rs-8506371","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}