Multi-area Collision-free Path Planning and Efficient Task Scheduling Optimization for Autonomous Agricultural Robots

Multi-area Collision-free Path Planning and Efficient Task Scheduling Optimization for Autonomous Agricultural Robots | 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 Multi-area Collision-free Path Planning and Efficient Task Scheduling Optimization for Autonomous Agricultural Robots Liwei Yang, Ping Li, Tao Wang, Jiya Tian, Chuangye Chen, Jie Tan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4038385/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access routes in diverse and complex multi-region planning scenarios. Physical sciences/Mathematics and computing/Computer science Physical sciences/Engineering/Mechanical engineering Autonomous agricultural robots Path planning Task scheduling A* algorithm Genetic algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 May, 2024 Reviews received at journal 02 May, 2024 Reviews received at journal 26 Apr, 2024 Reviewers agreed at journal 26 Apr, 2024 Reviews received at journal 26 Apr, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviews received at journal 16 Apr, 2024 Reviewers agreed at journal 16 Apr, 2024 Reviewers agreed at journal 10 Apr, 2024 Reviewers invited by journal 08 Apr, 2024 Editor assigned by journal 05 Apr, 2024 Editor invited by journal 23 Feb, 2024 Submission checks completed at journal 23 Feb, 2024 First submitted to journal 11 Feb, 2024 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. 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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-4038385","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":291926749,"identity":"09b0a696-43c5-4080-b8bd-dd1803ef63b3","order_by":0,"name":"Liwei Yang","email":"","orcid":"","institution":"Xinjiang Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Liwei","middleName":"","lastName":"Yang","suffix":""},{"id":291926750,"identity":"ef26e1b1-a0c8-4cad-a196-5f7e3681ad95","order_by":1,"name":"Ping 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In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. 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