{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/3d-ogse-online-safe-and-smooth-trajectory","title":"3D-OGSE: Online Safe and Smooth Trajectory Generation using Generalized Shape Expansion in Unknown 3-D Environments","arxiv_id":"2005.13229","date":"2020-05-27","proceeding":null,"authors":["Vrushabh Zinage","Senthil Hariharan Arul","Dinesh Manocha","Satadal Ghosh"],"abstract":"In this paper, we present an online motion planning algorithm (3D-OGSE) for generating smooth, collision-free trajectories over multiple planning iterations for 3-D agents operating in an unknown obstacle-cluttered 3-D environment. Our approach constructs a safe-region, termed 'generalized shape', at each planning iteration, which represents the obstacle-free region based on locally-sensed environment information. A collision-free path is computed by sampling points in the generalized shape and is used to generate a smooth, time-parametrized trajectory by minimizing snap. The generated trajectories are constrained to lie within the generalized shape, which ensures the agent maneuvers in the locally obstacle-free space. As the agent reaches boundary of 'sensing shape' in a planning iteration, a re-plan is triggered by receding horizon planning mechanism that also enables initialization of the next planning iteration. Theoretical guarantee of probabilistic completeness over the entire environment and of completely collision-free trajectory generation is provided. We evaluate the proposed method in simulation on complex 3-D environments with varied obstacle-densities. We observe that each re-planing computation takes $\\sim$1.4 milliseconds on a single thread of an Intel Core i5-8500 3.0 GHz CPU. In addition, our method is found to perform 4-10 times faster than several existing algorithms. In simulation over complex scenarios such as narrow passages also we observe less conservative behavior.","url_abs":"https://arxiv.org/abs/2005.13229v6","url_pdf":"https://arxiv.org/pdf/2005.13229v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"3d-ogse-online-safe-and-smooth-trajectory","repo_url":"https://github.com/nikhilprakash99/Generalised-Shape-Expansion-GSE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}