000160908 001__ 160908
000160908 005__ 20251017144615.0
000160908 0247_ $$2doi$$a10.1016/j.robot.2025.105020
000160908 0248_ $$2sideral$$a144076
000160908 037__ $$aART-2025-144076
000160908 041__ $$aeng
000160908 100__ $$aMartinez-Baselga, Diego$$uUniversidad de Zaragoza
000160908 245__ $$aRUMOR: Reinforcement learning for understanding a model of the real world for navigation in dynamic environments
000160908 260__ $$c2025
000160908 5060_ $$aAccess copy available to the general public$$fUnrestricted
000160908 5203_ $$aAutonomous navigation in dynamic environments is a complex but essential task for autonomous robots, with recent deep reinforcement learning approaches showing promising results. However, the complexity of the real world makes it infeasible to train agents in every possible scenario configuration. Moreover, existing methods typically overlook factors such as robot kinodynamic constraints, or assume perfect knowledge of the environment. In this work, we present RUMOR, a novel planner for differential-drive robots that uses deep reinforcement learning to navigate in highly dynamic environments. Unlike other end-to-end DRL planners, it uses a descriptive robocentric velocity space model to extract the dynamic environment information, enhancing training effectiveness and scenario interpretation. Additionally, we propose an action space that inherently considers robot kinodynamics and train it in a simulator that reproduces the real world problematic aspects, reducing the gap between the reality and simulation. We extensively compare RUMOR with other state-of-the-art approaches, demonstrating a better performance, and provide a detailed analysis of the results. Finally, we validate RUMOR’s performance in real-world settings by deploying it on a ground robot. Our experiments, conducted in crowded scenarios and unseen environments, confirm the algorithm’s robustness and transferability.
000160908 536__ $$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
000160908 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttps://creativecommons.org/licenses/by-nc/4.0/deed.es
000160908 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000160908 700__ $$0(orcid)0000-0002-6722-5541$$aRiazuelo, Luis$$uUniversidad de Zaragoza
000160908 700__ $$0(orcid)0000-0002-0449-2300$$aMontano, Luis$$uUniversidad de Zaragoza
000160908 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000160908 773__ $$g191 (2025), 105020 [12 pp.]$$pRobot. auton. syst.$$tROBOTICS AND AUTONOMOUS SYSTEMS$$x0921-8890
000160908 8564_ $$s3098794$$uhttps://zaguan.unizar.es/record/160908/files/texto_completo.pdf$$yVersión publicada
000160908 8564_ $$s2699857$$uhttps://zaguan.unizar.es/record/160908/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000160908 909CO $$ooai:zaguan.unizar.es:160908$$particulos$$pdriver
000160908 951__ $$a2025-10-17-14:18:57
000160908 980__ $$aARTICLE