118215 20240319081021.0 doi 10.3390/s22103847 sideral 129612 ART-2022-129612 eng Mackay, A. K. RL-DOVS: Reinforcement Learning for Autonomous Robot Navigation in Dynamic Environments 2022 Access copy available to the general public Unrestricted Autonomous navigation in dynamic environments where people move unpredictably is an essential task for service robots in real-world populated scenarios. Recent works in reinforcement learning (RL) have been applied to autonomous vehicle driving and to navigation around pedestrians. In this paper, we present a novel planner (reinforcement learning dynamic object velocity space, RL-DOVS) based on an RL technique for dynamic environments. The method explicitly considers the robot kinodynamic constraints for selecting the actions in every control period. The main contribution of our work is to use an environment model where the dynamism is represented in the robocentric velocity space as input to the learning system. The use of this dynamic information speeds the training process with respect to other techniques that learn directly either from raw sensors (vision, lidar) or from basic information about obstacle location and kinematics. We propose two approaches using RL and dynamic obstacle velocity (DOVS), RL-DOVS-A, which automatically learns the actions having the maximum utility, and RL-DOVS-D, in which the actions are selected by a human driver. Simulation results and evaluation are presented using different numbers of active agents and static and moving passive agents with random motion directions and velocities in many different scenarios. The performance of the technique is compared with other state-of-the-art techniques for solving navigation problems in environments such as ours. info:eu-repo/grantAgreement/ES/DGA-FSE/T45-20R info:eu-repo/grantAgreement/ES/MINECO-FEDER/PID2019-105390RB-I00 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.9 2022 CHEMISTRY, ANALYTICAL 26 / 86 = 0.302 2022 Q2 T1 INSTRUMENTS & INSTRUMENTATION 19 / 63 = 0.302 2022 Q2 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 100 / 274 = 0.365 2022 Q2 T2 0.764 2022 Instrumentation 2022 Q1 Analytical Chemistry 2022 Q1 Medicine (miscellaneous) 2022 Q2 Information Systems 2022 Q2 Biochemistry 2022 Q2 Atomic and Molecular Physics, and Optics 2022 Q2 Electrical and Electronic Engineering 2022 Q2 6.8 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Riazuelo, L. Universidad de Zaragoza (orcid)0000-0002-6722-5541 Montano, L. Universidad de Zaragoza (orcid)0000-0002-0449-2300 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 22, 10 (2022), 3847 [20 pp] Sensors Sensors 1424-8220 2560053 http://zaguan.unizar.es/record/118215/files/texto_completo.pdf Versión publicada 2771240 http://zaguan.unizar.es/record/118215/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:118215 articulos driver 2024-03-18-16:14:18 ARTICLE