Enhancing Autonomous Navigation in GNSS-Denied Environments: Integrating Collision Avoidance with Observability-Based Motion Planning

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Abstract This paper introduces a collision avoidance (CA) method integrated into the Observability Based Motion Planning (OBMP) framework. OBMP facilitates autonomous robot navigation in GNSS-denied outdoor environments, leveraging the concept of observability degree. The CA-OBMP method proposed here is an extension of the OBMP algorithm, which constructs paths based on terrestrial landmarks considering the obstacles. The CA approach is developed by redefining a dataset of in range landmarks while all of the landmark in vicinity of the obstacles are removed from the dataset. To evaluate the performance of the proposed method, simulations of the CA-OBMP algorithm are conducted for a 6-Degree of Freedom (DOF) quadrotor using MATLAB. The simulations encompass various arrangements of obstacles and diverse initial positions to assess the efficacy of CA method in different scenarios.
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Enhancing Autonomous Navigation in GNSS-Denied Environments: Integrating Collision Avoidance with Observability-Based Motion Planning | 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 Enhancing Autonomous Navigation in GNSS-Denied Environments: Integrating Collision Avoidance with Observability-Based Motion Planning samaneh elahian, M. A. Amiri Atashgah, Bahram Tarverdizadeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4407556/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract This paper introduces a collision avoidance (CA) method integrated into the Observability Based Motion Planning (OBMP) framework. OBMP facilitates autonomous robot navigation in GNSS-denied outdoor environments, leveraging the concept of observability degree. The CA-OBMP method proposed here is an extension of the OBMP algorithm, which constructs paths based on terrestrial landmarks considering the obstacles. The CA approach is developed by redefining a dataset of in range landmarks while all of the landmark in vicinity of the obstacles are removed from the dataset. To evaluate the performance of the proposed method, simulations of the CA-OBMP algorithm are conducted for a 6-Degree of Freedom (DOF) quadrotor using MATLAB. The simulations encompass various arrangements of obstacles and diverse initial positions to assess the efficacy of CA method in different scenarios. collision avoidance real-time autonomous motion planning observability degree EKF-ASLAM Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 28 May, 2024 Submission checks completed at journal 13 May, 2024 First submitted to journal 12 May, 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. 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. 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