{"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/the-barn-challenge-2023-autonomous-navigation","title":"The BARN Challenge 2023 -- Autonomous Navigation in Highly Constrained Spaces -- Inventec Team","arxiv_id":"2307.14580","date":"2023-07-27","proceeding":null,"authors":["Hanjaya Mandala","Guilherme Christmann"],"abstract":"Navigation in the real-world is hard and filled with complex scenarios. The Benchmark Autonomous Robot Navigation (BARN) Challenge is a competition that focuses on highly constrained spaces. Teams compete using a standard platform in a simulation and a real-world stage, with scenarios ranging from easy to challenging. This technical report presents the system and methods employed by the Inventec Team during the BARN Challenge 2023 (https://cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge23.html). At its core, our method uses the baseline learning-based controller LfLH. We developed extensions using a finite state machine to trigger recovery behaviors, and introduced two alternatives for forward safety collision checks, based on footprint inflation and model-predictive control. Moreover, we also present a backtrack safety check based on costmap region-of-interest. Compared to the original baseline, we managed a significant increase in the navigation score, from 0.2334 to 0.2445 (4.76%). Overall, our team ranked second place both in simulation and in the real-world stage. Our code is publicly available at: (https://github.com/inventec-ai-center/inventec-team-barn-challenge-2023.git)","url_abs":"https://arxiv.org/abs/2307.14580v1","url_pdf":"https://arxiv.org/pdf/2307.14580v1.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":"the-barn-challenge-2023-autonomous-navigation","repo_url":"https://github.com/inventec-ai-center/inventec-team-barn-challenge-2023","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}