Transformer-Based Path Planning for Single-Arm and Dual-Arm Robots in Dynamic Environments

Transformer-Based Path Planning for Single-Arm and Dual-Arm Robots in Dynamic Environments | 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 Transformer-Based Path Planning for Single-Arm and Dual-Arm Robots in Dynamic Environments Pengkai Wang, Jonghoek Kim, Mitra Ghergherehchi, MingXuan Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6237834/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jul, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract As the manufacturing industry progresses towards more automated and intelligent systems, manipulators are increasingly employed in tasks such as assembly and logistics handling. Traditional path planning algorithms like RRT and RRT* often result in inefficient robot arm operations, generating excessive joint revolutions and large travel distances for the end-effector. Additionally, the sharp turns produced by these algorithms are not suitable for smooth execution of manipulators. This research introduces a novel approach called T-ABA*, which combines the Adaptive Bidirectional A* (ABA*) algorithm with Transformer models to optimize both path planning and obstacle avoidance for robotic arms. The Transformer enhances the path planning process by dynamically adjusting the heuristic functions, improving the identification of efficient routes that avoid obstacles and ensure smooth motion. Furthermore, the Transformer is used to handle dynamic obstacle avoidance by predicting potential collisions and adjusting the path accordingly, reducing computational effort. Additionally, the method is extended to dual-arm control, addressing the complex challenge of collision avoidance between two cooperative robot arms. The proposed approach is validated through computer-based simulations, demonstrating its superiority in optimizing path planning, overcoming obstacles, and preventing inter-arm collisions in both single-arm and dual-arm robotic tasks. Path planning Transformer T-ABA* Manipulator Obstacle avoidance Dual-arm manipulations Full Text Cite Share Download PDF Status: Published Journal Publication published 23 Jul, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 16 May, 2025 Reviewers agreed at journal 23 Mar, 2025 Reviewers invited by journal 23 Mar, 2025 Editor assigned by journal 20 Mar, 2025 First submitted to journal 19 Mar, 2025 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. 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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-6237834","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432781864,"identity":"6e3c85a8-ad8d-43d7-a576-374ab3ba094b","order_by":0,"name":"Pengkai 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