{"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/safe-feedback-motion-planning-a-contraction","title":"Safe Feedback Motion Planning: A Contraction Theory and $\\mathcal{L}_1$-Adaptive Control Based Approach","arxiv_id":"2004.01142","date":"2020-05-25","proceeding":null,"authors":[],"abstract":"Autonomous robots that are capable of operating safely in the presence of\nimperfect model knowledge or external disturbances are vital in safety-critical\napplications. In this paper, we present a planner-agnostic framework to design\nand certify safe tubes around desired trajectories that the robot is always\nguaranteed to remain inside of. By leveraging recent results in contraction\nanalysis and $\\mathcal{L}_1$-adaptive control we synthesize an architecture\nthat induces safe tubes for nonlinear systems with state and time-varying\nuncertainties. We demonstrate with a few illustrative examples how contraction\ntheory-based $\\mathcal{L}_1$-adaptive control can be used in conjunction with\ntraditional motion planning algorithms to obtain provably safe trajectories.","url_abs":"http://arxiv.org/abs/2004.01142v2","url_pdf":"http://arxiv.org/pdf/2004.01142v2.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":"abstracts"},"code_links":[{"paper_slug":"safe-feedback-motion-planning-a-contraction","repo_url":"https://github.com/arlk/SafeFeedbackMotionPlanning.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"motion-planning","task_name":"Motion Planning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}