{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["9"],"submitter":["Ataka A"],"pubmed_abstract":["Over the course of the past decade, we have witnessed a huge expansion in robotic applications, most notably from well-defined industrial environments into considerably more complex environments. The obstacles that these environments often contain present robotics with a new challenge - to equip robots with a real-time capability of avoiding them. In this paper, we propose a magnetic-field-inspired navigation method that significantly has several advantages over alternative systems. Most importantly, <i>1</i>) it guarantees obstacle avoidance for both convex and non-convex obstacles, <i>2</i>) goal convergence is still guaranteed for point-like robots in environments with convex obstacles and non-maze concave obstacles, <i>3</i>) no prior knowledge of the environment, such as the position "],"journal":["Frontiers in robotics and AI"],"pagination":["834177"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8894445"],"repository":["biostudies-literature"],"pubmed_title":["Magnetic-Field-Inspired Navigation for Robots in Complex and Unknown Environments."],"pmcid":["PMC8894445"],"pubmed_authors":["Lam HK","Ataka A","Althoefer K"],"additional_accession":[]},"is_claimable":false,"name":"Magnetic-Field-Inspired Navigation for Robots in Complex and Unknown Environments.","description":"Over the course of the past decade, we have witnessed a huge expansion in robotic applications, most notably from well-defined industrial environments into considerably more complex environments. The obstacles that these environments often contain present robotics with a new challenge - to equip robots with a real-time capability of avoiding them. In this paper, we propose a magnetic-field-inspired navigation method that significantly has several advantages over alternative systems. Most importantly, <i>1</i>) it guarantees obstacle avoidance for both convex and non-convex obstacles, <i>2</i>) goal convergence is still guaranteed for point-like robots in environments with convex obstacles and non-maze concave obstacles, <i>3</i>) no prior knowledge of the environment, such as the position ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-05T13:09:35.464Z","creation":"2025-04-05T13:09:35.464Z"},"accession":"S-EPMC8894445","cross_references":{"pubmed":["35252366"],"doi":["10.3389/frobt.2022.834177"]}}