000162271 001__ 162271
000162271 005__ 20251017144602.0
000162271 0247_ $$2doi$$a10.1177/02783649251344639
000162271 0248_ $$2sideral$$a144913
000162271 037__ $$aART-2025-144913
000162271 041__ $$aeng
000162271 100__ $$aMartinez-Baselga, Diego$$uUniversidad de Zaragoza
000162271 245__ $$aSHINE: Social homology identification for navigation in crowded environments
000162271 260__ $$c2025
000162271 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162271 5203_ $$aNavigating mobile robots in social environments remains a challenging task due to the intricacies of human-robot interactions. Most of the motion planners designed for crowded and dynamic environments focus on choosing the best velocity to reach the goal while avoiding collisions, but do not explicitly consider the high-level navigation behavior (avoiding through the left or right side, letting others pass or passing before others, etc.). In this work, we present a novel motion planner that incorporates topology distinct paths representing diverse navigation strategies around humans. The planner selects the topology class that imitates human behavior the best using a deep neural network model trained on real-world human motion data, ensuring socially intelligent and contextually aware navigation. Our system refines the chosen path through an optimization-based local planner in real time, ensuring seamless adherence to desired social behaviors. In this way, we decouple perception and local planning from the decision-making process. We evaluate the prediction accuracy of the network with real-world data. In addition, we assess the navigation capabilities in both simulation and a real-world platform, comparing it with other state-of-the-art planners. We demonstrate that our planner exhibits socially desirable behaviors and shows a smooth and remarkable performance.
000162271 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2022-139615OB-I00$$9info:eu-repo/grantAgreement/ES/AEI/PRE2020-094415$$9info:eu-repo/grantAgreement/ES/DGA/T45-23R$$9info:eu-repo/grantAgreement/EC/HORIZON EUROPE/101041863/EU/Intuitive interaction for robots among humans/INTERACT$$9info:eu-repo/grantAgreement/EC/H2020/101017008/EU/Enhancing Healthcare with Assistive Robotic Mobile Manipulation/HARMONY$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101017008-HARMONY
000162271 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000162271 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000162271 700__ $$aDe Groot, Oscar
000162271 700__ $$aKnoedler, Luzia
000162271 700__ $$0(orcid)0000-0002-6722-5541$$aRiazuelo, Luis$$uUniversidad de Zaragoza
000162271 700__ $$aAlonso-Mora, Javier
000162271 700__ $$0(orcid)0000-0002-0449-2300$$aMontano, Luis$$uUniversidad de Zaragoza
000162271 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000162271 773__ $$g(2025), [20 pp.]$$pInt. j. rob. res.$$tInternational Journal of Robotics Research$$x0278-3649
000162271 8564_ $$s14610170$$uhttps://zaguan.unizar.es/record/162271/files/texto_completo.pdf$$yPostprint
000162271 8564_ $$s2643456$$uhttps://zaguan.unizar.es/record/162271/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000162271 909CO $$ooai:zaguan.unizar.es:162271$$particulos$$pdriver
000162271 951__ $$a2025-10-17-14:14:27
000162271 980__ $$aARTICLE