{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T22:19:30Z","timestamp":1768601970584,"version":"3.49.0"},"reference-count":124,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Mobile robot navigation remains a critical challenge in robotics, with applications spanning autonomous vehicles, search and rescue, and other dynamic environments. In recent years, reinforcement learning (RL) has become a powerful approach for solving complex tasks such as robotic manipulation, gameplay, and autonomous driving. By enabling robots to learn optimal navigation strategies through interaction with their environment, RL offers a promising pathway to autonomous mobility. This review presents a comprehensive overview of recent advancements in RL as applied to mobile robot navigation. We begin by outlining core RL concepts, agents, environments, rewards, and value functions, explaining their roles in navigation. Key RL techniques, including Q-learning, deep reinforcement learning, Markov Decision Processes (MDPs), and policy gradient methods, are examined to highlight their transformative impact on navigation performance. The review also explores a range of practical applications and identifies current challenges and open research directions. Critical issues such as safety, sample efficiency, and scalability to real-world scenarios are discussed in depth to ensure robust deployment of RL-based systems. Lastly, this review synthesizes the state-of-the-art in reinforcement learning for mobile robot navigation, offering readers both a foundational understanding and valuable insights into emerging trends and future opportunities in this rapidly evolving\u00a0field.<\/jats:p>","DOI":"10.1515\/jisys-2024-0547","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T11:03:55Z","timestamp":1768561435000},"source":"Crossref","is-referenced-by-count":0,"title":["Reinforcement learning for mobile robot mapless navigation: a review of recent advances"],"prefix":"10.1515","volume":"35","author":[{"given":"Vernon","family":"Kok","sequence":"first","affiliation":[{"name":"Unit for Data Science and Computing , North-West University , 11 Hoffman Street , Potchefstroom , 2520 , South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3721-3400","authenticated-orcid":false,"given":"Absalom E.","family":"Ezugwu","sequence":"additional","affiliation":[{"name":"Unit for Data Science and Computing , North-West University , 11 Hoffman Street , Potchefstroom , 2520 , South Africa"}]},{"given":"Micheal","family":"Olusanya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology , Sol Plaatje University , Kimberley 8300 , South Africa"}]}],"member":"374","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"2026011611304740982_j_jisys-2024-0547_ref_001","doi-asserted-by":"crossref","unstructured":"W. Zhang, Y. Zhang, N. Liu, K. Ren, and G. Peng, \u201cDimension-variable mapless navigation with deep reinforcement learning,\u201d IEEE ASME Trans. Mechatron., vol. 30, no. 6, 2025, https:\/\/doi.org\/10.1109\/TMECH.2025.3541797.","DOI":"10.1109\/TMECH.2025.3541797"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_002","doi-asserted-by":"crossref","unstructured":"K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, \u201cDeep reinforcement learning: A brief survey,\u201d IEEE Signal Process. Mag., vol.\u00a034, no. 6, pp.\u00a026\u201338, 2017, https:\/\/doi.org\/10.1109\/MSP.2017.2743240.","DOI":"10.1109\/MSP.2017.2743240"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_093","doi-asserted-by":"crossref","unstructured":"V. Mnih et al.., \u201cHuman-level control through deep reinforcement learning,\u201d Nature, vol.\u00a0518, no. 7540, pp.\u00a0529\u2013533, 2015, https:\/\/doi.org\/10.1038\/nature14236.","DOI":"10.1038\/nature14236"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_005","doi-asserted-by":"crossref","unstructured":"M. Kuribayashi, K. Uehara, A. Wang, S. Morishima, and C. Asakawa, \u201cWanderGuide: Indoor map-less robotic guide for exploration by blind people,\u201d arXiv preprint arXiv:2502.08906, 2025, https:\/\/doi.org\/10.1145\/3706598.3713788.","DOI":"10.1145\/3706598.3713788"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_119","doi-asserted-by":"crossref","unstructured":"H. Le, S. Saeedvand, and C. C. Hsu, \u201cA comprehensive review of mobile robot navigation using deep reinforcement learning algorithms in crowded environments,\u201d J.\u00a0Intell. Rob. Syst., vol.\u00a0110, no. 4, p.\u00a0158, 2024, https:\/\/doi.org\/10.1007\/s10846-024-02198-w.","DOI":"10.1007\/s10846-024-02198-w"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_003","doi-asserted-by":"crossref","unstructured":"X. Liu et al.., \u201cEnhancing navigation performance in unknown environments using spiking neural networks and reinforcement learning with asymptotic gradient method,\u201d Complex Intell. Syst., vol.\u00a011, no. 2, pp.\u00a01\u201313, 2025, https:\/\/doi.org\/10.1007\/s40747-024-01777-6.","DOI":"10.1007\/s40747-024-01777-6"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_115","doi-asserted-by":"crossref","unstructured":"J.\u00a0C. Andersen, O. Ravn, and N. A. Andersen, \u201cAutonomous rule-based robot navigation in orchards,\u201d IFAC Proc. Vol., vol.\u00a043, no. 16, pp.\u00a043\u201348, 2010, https:\/\/doi.org\/10.3182\/20100906-3-it-2019.00010.","DOI":"10.3182\/20100906-3-IT-2019.00010"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_004","doi-asserted-by":"crossref","unstructured":"W. Zhao, J.\u00a0P. Queralta, and T. Westerlund, \u201cSim-to-real transfer in deep reinforcement learning for robotics: A survey,\u201d in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT, Australia, IEEE, 2020, December, pp. 737\u2013744.","DOI":"10.1109\/SSCI47803.2020.9308468"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_006","doi-asserted-by":"crossref","unstructured":"J. Gao, X. Pang, Q. Liu, and Y. Li, \u201cHierarchical reinforcement learning for safe mapless navigation with congestion estimation,\u201d arXiv preprint arXiv:2503.12036, 2025, https:\/\/doi.org\/10.1109\/ICRA55743.2025.11128867.","DOI":"10.1109\/ICRA55743.2025.11128867"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_124","doi-asserted-by":"crossref","unstructured":"L. Ge, X. Zhou, Y. Li, and Y. Wang, \u201cDeep reinforcement learning navigation via decision transformer in autonomous driving,\u201d Front. Neurorob., vol.\u00a018, 2024, Art. no. 1338189, https:\/\/doi.org\/10.3389\/fnbot.2024.1338189.","DOI":"10.3389\/fnbot.2024.1338189"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_007","doi-asserted-by":"crossref","unstructured":"A. Zhang, H. Sikchi, A. Zhang, and J. Biswas, \u201cCREStE: Scalable mapless navigation with internet scale priors and counterfactual guidance,\u201d arXiv preprint arXiv:2503.03921, 2025, https:\/\/doi.org\/10.15607\/RSS.2025.XXI.136.","DOI":"10.15607\/RSS.2025.XXI.136"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_048","doi-asserted-by":"crossref","unstructured":"Q. Zhang, M. Zhu, L. Zou, M. Li, and Y. Zhang, \u201cLearning reward function with matching network for mapless navigation,\u201d Sensors, vol.\u00a020, p.\u00a03664, 2020, https:\/\/doi.org\/10.3390\/s20133664.","DOI":"10.3390\/s20133664"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_117","unstructured":"M. Vecerik et al.., \u201cLeveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards,\u201d arXiv preprint arXiv:1707.08817, 2017."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_116","doi-asserted-by":"crossref","unstructured":"C. Wang, J. Wang, J. Wang, and X. Zhang, \u201cDeep-reinforcement-learning-based autonomous UAV navigation with sparse rewards,\u201d IEEE Internet Things J., vol.\u00a07, no. 7, pp.\u00a06180\u20136190, 2020, https:\/\/doi.org\/10.1109\/JIOT.2020.2973193.","DOI":"10.1109\/JIOT.2020.2973193"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_118","unstructured":"D. Andre and S. J. Russell, \u201cState abstraction for programmable reinforcement learning agents,\u201d in Aaai\/iaai, 2002, July, pp.\u00a0119\u2013125."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_088","unstructured":"J. Liu, J. Guo, Z. Meng, and J. Xue, \u201cReVoLT: Relational reasoning and voronoi local graph planning for target-driven navigation,\u201d arXiv preprint arXiv:2301.02382, 2023."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_011","doi-asserted-by":"crossref","unstructured":"M. B. Alatise and G. P. Hancke, \u201cA review on challenges of autonomous mobile robot and sensor fusion methods,\u201d IEEE Access, vol.\u00a08, pp.\u00a039830\u201339846, 2020, https:\/\/doi.org\/10.1109\/ACCESS.2020.2975643.","DOI":"10.1109\/ACCESS.2020.2975643"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_009","doi-asserted-by":"crossref","unstructured":"F. Rubio, F. Valero, and C. Llopis-Albert, \u201cA review of mobile robots: concepts, methods, theoretical framework, and applications,\u201d Int. J.\u00a0Adv. Rob. Syst., vol.\u00a016, no. 2, 2019, Art. no. 1729881419839596, https:\/\/doi.org\/10.1177\/1729881419839596.","DOI":"10.1177\/1729881419839596"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_091","doi-asserted-by":"crossref","unstructured":"H. Li, B. Luo, W. Song, and C. Yang, \u201cPredictive hierarchical reinforcement learning for path-efficient mapless navigation with moving target,\u201d Neural Netw., vol. 165, pp. 677\u2013688, 2023, https:\/\/doi.org\/10.1016\/j.neunet.2023.06.007.","DOI":"10.1016\/j.neunet.2023.06.007"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_008","unstructured":"G. A. Rummery and M. Niranjan, On-line Q-learning Using Connectionist Systems, vol.\u00a037, Cambridge, University of Cambridge, Department of Engineering, 1994, p.\u00a014."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_010","doi-asserted-by":"crossref","unstructured":"Y. Li, Q. Lyu, J. Yang, Y. Salam, and B. Wang, \u201cVisual target-driven robot crowd navigation with limited FOV using self-attention enhanced deep reinforcement learning,\u201d Sensors, vol.\u00a025, no. 3, p.\u00a0639, 2025, https:\/\/doi.org\/10.3390\/s25030639.","DOI":"10.3390\/s25030639"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_092","doi-asserted-by":"crossref","unstructured":"W. Xiao, L. Yuan, T. Ran, L. He, J. Zhang, and J. Cui, \u201cMultimodal fusion for autonomous navigation via deep reinforcement learning with sparse rewards and hindsight experience replay,\u201d Displays, vol.\u00a078, 2023, Art. no. 102440, https:\/\/doi.org\/10.1016\/j.displa.2023.102440.","DOI":"10.1016\/j.displa.2023.102440"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_096","doi-asserted-by":"crossref","unstructured":"K. Zhu and T. Zhang, \u201cDeep reinforcement learning based mobile robot navigation: A review,\u201d Tsinghua Sci. Technol., vol.\u00a026, no. 5, pp.\u00a0674\u2013691, 2021, https:\/\/doi.org\/10.26599\/TST.2021.9010012.","DOI":"10.26599\/TST.2021.9010012"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_097","doi-asserted-by":"crossref","unstructured":"P. Goel, S. I. Roumeliotis, and G. S. Sukhatme, \u201cRobust localization using relative and absolute position estimates,\u201d in Proceedings 1999 IEEE\/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No. 99CH36289), vol. 2, Kyongju, South Korea, IEEE, 1999, October, pp. 1134\u20131140.","DOI":"10.1109\/IROS.1999.812832"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_098","doi-asserted-by":"crossref","unstructured":"S. Thrun, \u201cFinding landmarks for mobile robot navigation,\u201d in Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No. 98CH36146), vol. 2, Leuven, Belgium, IEEE, 1998, May, pp. 958\u2013963.","DOI":"10.1109\/ROBOT.1998.677210"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_099","unstructured":"D. S. Chaplot, D. P. Gandhi, A. Gupta, and R. R. Salakhutdinov, \u201cObject goal navigation using goal-oriented semantic exploration,\u201d Adv. Neural Inf. Process. Syst., vol.\u00a033, pp.\u00a04247\u20134258, 2020."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_100","doi-asserted-by":"crossref","unstructured":"A. Chatterjee, K. Pulasinghe, K. Watanabe, and K. Izumi, \u201cA particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems,\u201d IEEE Trans. Ind. Electron., vol.\u00a052, no. 6, pp.\u00a01478\u20131489, 2005, https:\/\/doi.org\/10.1109\/TIE.2005.858737.","DOI":"10.1109\/TIE.2005.858737"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_101","unstructured":"H. Sowerby, Z. Zhou, and M. L. Littman, \u201cDesigning rewards for fast learning,\u201d arXiv preprint arXiv:2205.15400, 2022."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_102","doi-asserted-by":"crossref","unstructured":"J. Eschmann, \u201cReward function design in reinforcement learning,\u201d Reinforc. Learn. Algorithm. Anal. Appl., vol. 833, pp. 25\u201333, 2021, https:\/\/doi.org\/10.1007\/978-3-030-41188-6_3.","DOI":"10.1007\/978-3-030-41188-6_3"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_103","unstructured":"R. Devidze, G. Radanovic, P. Kamalaruban, and A. Singla, \u201cExplicable reward design for reinforcement learning agents,\u201d Adv. Neural Inf. Process. Syst., vol.\u00a034, pp.\u00a020118\u201320131, 2021."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_111","doi-asserted-by":"crossref","unstructured":"M. N. Al-Hamadani, M. A. Fadhel, L. Alzubaidi, and B. Harangi, \u201cReinforcement learning algorithms and applications in healthcare and robotics: A comprehensive and systematic review,\u201d Sensors, vol.\u00a024, no. 8, p.\u00a02461, 2024, https:\/\/doi.org\/10.3390\/s24082461.","DOI":"10.3390\/s24082461"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_065","doi-asserted-by":"crossref","unstructured":"S. Gattu, \u201cAutonomous navigation and obstacle avoidance using self-guided and self-regularized actor-critic,\u201d in Proceedings of the 8th International Conference on Robotics and Artificial Intelligence, Singapore, Association for Computing Machinery (ACM), 2022, November, pp. 52\u201358.","DOI":"10.1145\/3573910.3573914"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_067","doi-asserted-by":"crossref","unstructured":"H. Jeong, H. Hassani, M. Morari, D. D. Lee, and G. J. Pappas, \u201cDeep reinforcement learning for active target tracking,\u201d in 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, IEEE, 2021, pp. 1825\u20131831.","DOI":"10.1109\/ICRA48506.2021.9561258"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_014","doi-asserted-by":"crossref","unstructured":"C. Huang, O. Mees, A. Zeng, and W. Burgard, \u201cVisual language maps for robot navigation,\u201d in 2023 IEEE International Conference on Robotics and Automation (ICRA), London, UK, IEEE, 2023, May, pp. 10608\u201310615.","DOI":"10.1109\/ICRA48891.2023.10160969"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_015","doi-asserted-by":"crossref","unstructured":"F. Gul, W. Rahiman, and S. S. Nazli Alhady, \u201cA comprehensive study for robot navigation techniques,\u201d Cogent Eng., vol.\u00a06, no. 1, 2019, Art. no. 1632046, https:\/\/doi.org\/10.1080\/23311916.2019.1632046.","DOI":"10.1080\/23311916.2019.1632046"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_016","doi-asserted-by":"crossref","unstructured":"H. Lee and J. Jeong, \u201cMobile robot path optimization technique based on reinforcement learning algorithm in warehouse environment,\u201d Appl. Sci., vol.\u00a011, no. 3, p.\u00a01209, 2021, https:\/\/doi.org\/10.3390\/app11031209.","DOI":"10.3390\/app11031209"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_017","doi-asserted-by":"crossref","unstructured":"A. D. J. Plasencia-Salgueiro, \u201cDeep reinforcement learning for autonomous mobile robot navigation,\u201d in Artificial Intelligence for Robotics and Autonomous Systems Applications, Cham, Springer International Publishing, 2023, pp.\u00a0195\u2013237.","DOI":"10.1007\/978-3-031-28715-2_7"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_018","doi-asserted-by":"crossref","unstructured":"G. S. Tewolde, \u201cSensor and network technology for intelligent transportation systems,\u201d in 2012 IEEE International Conference on Electro\/Information Technology, Indianapolis, IN, IEEE, 2012, May, pp. 1\u20137.","DOI":"10.1109\/EIT.2012.6220735"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_019","doi-asserted-by":"crossref","unstructured":"U. Nehmzow and C. Owen, \u201cRobot navigation in the real world: Experiments with Manchester\u2019s FortyTwo in unmodified, large environments,\u201d Robot. Autonom. Syst., vol.\u00a033, no. 4, pp.\u00a0223\u2013242, 2000, https:\/\/doi.org\/10.1016\/s0921-8890(00)00098-1.","DOI":"10.1016\/S0921-8890(00)00098-1"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_020","doi-asserted-by":"crossref","unstructured":"M. Aizat, A. Azmin, and W. Rahiman, \u201cA survey on navigation approaches for automated guided vehicle robots in dynamic surrounding,\u201d IEEE Access, vol.\u00a011, pp.\u00a033934\u201333955, 2023, https:\/\/doi.org\/10.1109\/ACCESS.2023.3263734.","DOI":"10.1109\/ACCESS.2023.3263734"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_021","doi-asserted-by":"crossref","unstructured":"Y. H. Kim, H. J. Kim, J.\u00a0H. Lee, E. J. Kim, and J.\u00a0W. Song, \u201cSequential batch fusion magnetic anomaly navigation for a low-cost indoor mobile robot,\u201d Measurement, vol.\u00a0213, 2023, Art. no. 112706, https:\/\/doi.org\/10.1016\/j.measurement.2023.112706.","DOI":"10.1016\/j.measurement.2023.112706"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_022","doi-asserted-by":"crossref","unstructured":"S. Im, S. Kim, J. Yun, and J. Nam, \u201cRobot-aided magnetic navigation system for wireless capsule manipulation,\u201d Micromachines, vol.\u00a014, no. 2, p.\u00a0269, 2023, https:\/\/doi.org\/10.3390\/mi14020269.","DOI":"10.3390\/mi14020269"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_023","doi-asserted-by":"crossref","unstructured":"A. Pore et al.., \u201cAutonomous navigation for robot-assisted intraluminal and endovascular procedures: A systematic review,\u201d IEEE Trans. Robot., vol. 39, no. 6, 2023, https:\/\/doi.org\/10.1109\/TRO.2023.3269384.","DOI":"10.1109\/TRO.2023.3269384"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_024","doi-asserted-by":"crossref","unstructured":"G. Loianno et al.., \u201cLocalization, grasping, and transportation of magnetic objects by a team of mavs in challenging desert-like environments,\u201d IEEE Rob. Autom. Lett., vol.\u00a03, no. 3, pp.\u00a01576\u20131583, 2018, https:\/\/doi.org\/10.1109\/LRA.2018.2800121.","DOI":"10.1109\/LRA.2018.2800121"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_025","unstructured":"M. Z. Baharuddin, I. Z. Abidin, S. S. K. Mohideen, Y. K. Siah, and J.\u00a0T. T. Chuan, \u201cAnalysis of line sensor configuration for the advanced line follower robot,\u201d University Tenaga Nasional, 2005."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_026","doi-asserted-by":"crossref","unstructured":"M. Engin and D. Engin, \u201cPath planning of line follower robot,\u201d in 2012 5th European DSP Education and Research Conference (EDERC), Amsterdam, Netherlands, IEEE, 2012, September, pp. 1\u20135.","DOI":"10.1109\/EDERC.2012.6532213"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_027","doi-asserted-by":"crossref","unstructured":"A. Siddiqui, A. Basker, M. Dias, K. Chavan, and N. Avhad, \u201cLine following cart (LFC) for office applications,\u201d in 2023 5th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), Navi Mumbai, India, IEEE, 2023, January, pp. 1\u20136.","DOI":"10.1109\/ICNTE56631.2023.10146691"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_028","doi-asserted-by":"crossref","unstructured":"J. Yu et al.., \u201cStudy of convolutional neural network-based semantic segmentation methods on edge intelligence devices for field agricultural robot navigation line extraction,\u201d Comput. Electron. Agric., vol.\u00a0209, 2023, Art. no. 107811, https:\/\/doi.org\/10.1016\/j.compag.2023.107811.","DOI":"10.1016\/j.compag.2023.107811"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_029","doi-asserted-by":"crossref","unstructured":"R. Farkh and K. Aljaloud, \u201cVision navigation based PID control for line tracking robot,\u201d Intell. Autom. Soft Comput., vol.\u00a035, no. 1, pp.\u00a0901\u2013911, 2023, https:\/\/doi.org\/10.32604\/iasc.2023.027614.","DOI":"10.32604\/iasc.2023.027614"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_030","doi-asserted-by":"crossref","unstructured":"M. Y. Arafat, M. M. Alam, and S. Moh, \u201cVision-based navigation techniques for unmanned aerial vehicles: Review and challenges,\u201d Drones, vol.\u00a07, no. 2, p.\u00a089, 2023, https:\/\/doi.org\/10.3390\/drones7020089.","DOI":"10.3390\/drones7020089"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_031","doi-asserted-by":"crossref","unstructured":"Y. Bai, B. Zhang, N. Xu, J. Zhou, J. Shi, and Z. Diao, \u201cVision-based navigation and guidance for agricultural autonomous vehicles and robots: A review,\u201d Comput. Electron. Agric., vol.\u00a0205, 2023, Art. no. 107584, https:\/\/doi.org\/10.1016\/j.compag.2022.107584.","DOI":"10.1016\/j.compag.2022.107584"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_032","doi-asserted-by":"crossref","unstructured":"S. Yan, J. Wang, Z. Wu, M. Tan, and J. Yu, \u201cAutonomous vision-based navigation and stability augmentation control of a biomimetic robotic hammerhead shark,\u201d IEEE Trans. Autom. Sci. Eng., vol. 21, no. 3, 2023, https:\/\/doi.org\/10.1109\/TASE.2023.3278740.","DOI":"10.1109\/TASE.2023.3278740"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_033","doi-asserted-by":"crossref","unstructured":"S. Tavasoli, X. Pan, and T. Y. Yang, \u201cReal-time autonomous indoor navigation and vision-based damage assessment of reinforced concrete structures using low-cost nano aerial vehicles,\u201d J.\u00a0Build. Eng., vol.\u00a068, 2023, Art. no. 106193, https:\/\/doi.org\/10.1016\/j.jobe.2023.106193.","DOI":"10.1016\/j.jobe.2023.106193"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_034","doi-asserted-by":"crossref","unstructured":"S. Kumar, M. Majeedullah, and A. B. Buriro, \u201cAutonomous navigation and real time mapping using ultrasonic sensors in NAO humanoid robot,\u201d Eng. Technol. Appl. Sci. Research, vol.\u00a012, no. 5, pp.\u00a09102\u20139107, 2022, https:\/\/doi.org\/10.48084\/etasr.5180.","DOI":"10.48084\/etasr.5180"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_035","doi-asserted-by":"crossref","unstructured":"T. R. Anusha, S. S. Kumar, and S. Panday, \u201cROS based obstacle detection robot using ultrasonic sensor and FMCW RADAR,\u201d in 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, IEEE, 2022, August, pp. 372\u2013378.","DOI":"10.1109\/ICESC54411.2022.9885346"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_036","doi-asserted-by":"crossref","unstructured":"I. Ohya, A. Kosaka, and A. Kak, \u201cVision-based navigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonic sensing,\u201d IEEE Trans. Robot. Autom., vol.\u00a014, no. 6, pp.\u00a0969\u2013978, 1998, https:\/\/doi.org\/10.1109\/70.736780.","DOI":"10.1109\/70.736780"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_037","doi-asserted-by":"crossref","unstructured":"S. Yuan, J. Wu, F. Luan, L. Zhang, and J. Lv, \u201cImprovement of strong tracking UKF-SLAM approach using three-position ultrasonic detection,\u201d Robot. Autonom. Syst., vol.\u00a0159, 2023, Art. no. 104305, https:\/\/doi.org\/10.1016\/j.robot.2022.104305.","DOI":"10.1016\/j.robot.2022.104305"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_038","doi-asserted-by":"crossref","unstructured":"G. Grisetti, R. K\u00fcmmerle, C. Stachniss, and W. Burgard, \u201cA tutorial on graph-based SLAM,\u201d IEEE Intell. Transport. Syst. Mag., vol.\u00a02, no. 4, pp.\u00a031\u201343, 2010, https:\/\/doi.org\/10.1109\/MITS.2010.939925.","DOI":"10.1109\/MITS.2010.939925"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_114","doi-asserted-by":"crossref","unstructured":"O. S. Ekundayo and A. E. Ezugwu, \u201cDeep learning: Historical overview from inception to actualization, models, applications and future trends,\u201d Appl. Soft Comput., vol. 181, no. 113378, 2025, https:\/\/doi.org\/10.1016\/j.asoc.2025.113378.","DOI":"10.1016\/j.asoc.2025.113378"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_094","doi-asserted-by":"crossref","unstructured":"J. Wang, S. Elfwing, and E. Uchibe, \u201cDeep reinforcement learning by parallelizing reward and punishment using the maxpain architecture,\u201d in 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Tokyo, Japan, IEEE, 2018, September, pp. 175\u2013180.","DOI":"10.1109\/DEVLRN.2018.8761044"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_013","unstructured":"L. Graesser and W. L. Keng, Foundations of Deep Reinforcement Learning, Columbus, Ohio, Addison-Wesley Professional, 2019."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_012","unstructured":"R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, Cambridge, MIT Press, 2018."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_089","doi-asserted-by":"crossref","unstructured":"S. Cebollada, L. Pay\u00e1, M. Flores, A. Peidr\u00f3, and O. Reinoso, \u201cA state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data,\u201d Expert Syst. Appl., vol.\u00a0167, 2021, Art. no. 114195, https:\/\/doi.org\/10.1016\/j.eswa.2020.114195.","DOI":"10.1016\/j.eswa.2020.114195"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_095","doi-asserted-by":"crossref","unstructured":"Q. Wang, Q. Lv, H. Wei, and P. Zhang, \u201cA deep reinforcement learning based mapless navigation algorithm using continuous actions,\u201d in 2019 International Conference on Robots and Intelligent System (ICRIS), Haikou, China, IEEE, 2019, June, pp. 63\u201368.","DOI":"10.1109\/ICRIS.2019.00025"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_069","doi-asserted-by":"crossref","unstructured":"E. Marchesini and A. Farinelli, \u201cDiscrete deep reinforcement learning for mapless navigation,\u201d in 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France (Virtual), IEEE, 2020, May, pp. 10688\u201310694.","DOI":"10.1109\/ICRA40945.2020.9196739"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_104","doi-asserted-by":"crossref","unstructured":"C. Y. Tsai, H. Nisar, and Y. C. Hu, \u201cMapless LiDAR navigation control of wheeled mobile robots based on deep imitation learning,\u201d IEEE Access, vol.\u00a09, pp.\u00a0117527\u2013117541, 2021, https:\/\/doi.org\/10.1109\/ACCESS.2021.3107041.","DOI":"10.1109\/ACCESS.2021.3107041"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_105","doi-asserted-by":"crossref","unstructured":"L. Dong, Z. He, C. Song, and C. Sun, \u201cA review of mobile robot motion planning methods: From classical motion planning workflows to reinforcement learning-based architectures,\u201d J.\u00a0Syst. Eng. Electron., vol.\u00a034, no. 2, pp.\u00a0439\u2013459, 2023, https:\/\/doi.org\/10.23919\/JSEE.2023.000051.","DOI":"10.23919\/JSEE.2023.000051"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_122","unstructured":"D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller, \u201cDeterministic policy gradient algorithms,\u201d in International Conference on Machine Learning, Beijing, China, Pmlr, 2014, January, pp. 387\u2013395."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_039","doi-asserted-by":"crossref","unstructured":"D. Muse and S. Wermter, \u201cActor-critic learning for platform-independent robot navigation,\u201d Cogn Comput., vol.\u00a01, pp.\u00a0203\u2013220, 2009, https:\/\/doi.org\/10.1007\/s12559-009-9021-z.","DOI":"10.1007\/s12559-009-9021-z"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_040","unstructured":"L. Xie, Reinforcement Learning Based Mapless Robot Navigation, (Doctoral dissertation, University of Oxford), 2019."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_042","doi-asserted-by":"crossref","unstructured":"M. Han, L. Zhang, J. Wang, and W. Pan, \u201cActor-critic reinforcement learning for control with stability guarantee,\u201d IEEE Rob. Autom. Lett., vol.\u00a05, no. 4, pp.\u00a06217\u20136224, 2020, https:\/\/doi.org\/10.1109\/LRA.2020.3011351.","DOI":"10.1109\/LRA.2020.3011351"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_109","unstructured":"T. P. Lillicrap et al.., \u201cContinuous control with deep reinforcement learning,\u201d arXiv preprint arXiv:1509.02971, 2015."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_120","unstructured":"S. Fujimoto, H. Hoof, and D. Meger, \u201cAddressing function approximation error in actor-critic methods,\u201d in International Conference on Machine Learning, Stockholm, Sweden, PMLR, 2018, July, pp. 1587\u20131596."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_041","doi-asserted-by":"crossref","unstructured":"J.\u00a0C. de Jesus, V. A. Kich, A. H. Kolling, R. B. Grando, M. A. D. S. L. Cuadros, and D. F. T. Gamarra, \u201cSoft actor-critic for navigation of mobile robots,\u201d J.\u00a0Intell. Rob. Syst., vol.\u00a0102, no. 2, p.\u00a031, 2021, https:\/\/doi.org\/10.1007\/s10846-021-01367-5.","DOI":"10.1007\/s10846-021-01367-5"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_121","unstructured":"T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, \u201cSoft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,\u201d in International Conference on Machine Learning, Stockholm, Sweden, Pmlr, 2018, July, pp. 1861\u20131870."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_123","doi-asserted-by":"crossref","unstructured":"D. Moher et al.., \u201cPreferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement,\u201d Syst. Rev., vol.\u00a04, no. 1, p.\u00a01, 2015, https:\/\/doi.org\/10.1186\/2046-4053-4-1.","DOI":"10.1186\/2046-4053-4-1"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_108","doi-asserted-by":"crossref","unstructured":"R. M\u00f6ller, A. Furnari, S. Battiato, A. H\u00e4rm\u00e4, and G. M. Farinella, \u201cA survey on human-aware robot navigation,\u201d Robot. Autonom. Syst., vol.\u00a0145, 2021, Art. no. 103837, https:\/\/doi.org\/10.1016\/j.robot.2021.103837.","DOI":"10.1016\/j.robot.2021.103837"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_106","doi-asserted-by":"crossref","unstructured":"Y. Chang, Y. Cheng, U. Manzoor, and J. Murray, \u201cA review of UAV autonomous navigation in GPS-denied environments,\u201d Robot. Autonom. Syst., 2023, Art. no. 104533, https:\/\/doi.org\/10.1016\/j.robot.2023.104533.","DOI":"10.1016\/j.robot.2023.104533"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_107","doi-asserted-by":"crossref","unstructured":"F. AlMahamid and K. Grolinger, \u201cAutonomous unmanned aerial vehicle navigation using reinforcement learning: A systematic review,\u201d Eng. Appl. Artif. Intell., vol.\u00a0115, 2022, Art. no. 105321, https:\/\/doi.org\/10.1016\/j.engappai.2022.105321.","DOI":"10.1016\/j.engappai.2022.105321"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_110","unstructured":"T. Yang et al.., \u201c3D ToF LiDAR in mobile robotics: A review,\u201d arXiv preprint arXiv:2202.11025, 2022."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_044","doi-asserted-by":"crossref","unstructured":"J. Jin, N. M. Nguyen, N. Sakib, D. Graves, H. Yao, and M. Jagersand, \u201cMapless navigation among dynamics with social-safety-awareness: A reinforcement learning approach from 2d laser scans,\u201d in 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France (Virtual), IEEE, 2020, May, pp. 6979\u20136985.","DOI":"10.1109\/ICRA40945.2020.9197148"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_045","doi-asserted-by":"crossref","unstructured":"R. B. Grando, J.\u00a0C. de Jesus, and P. L. Drews-Jr, \u201cDeep reinforcement learning for mapless navigation of unmanned aerial vehicles,\u201d in 2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE), Natal, Brazil (Virtual), IEEE, 2020, November, pp. 1\u20136.","DOI":"10.1109\/LARS\/SBR\/WRE51543.2020.9307015"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_070","doi-asserted-by":"crossref","unstructured":"L. D. de Moraes et al.., \u201cDouble deep reinforcement learning techniques for low dimensional sensing mapless navigation of terrestrial Mobile robots,\u201d arXiv preprint arXiv:2301.11173, 2023, https:\/\/doi.org\/10.1007\/978-3-031-35507-3_16.","DOI":"10.1007\/978-3-031-35507-3_16"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_077","doi-asserted-by":"crossref","unstructured":"J. de Heuvel, W. Shi, X. Zeng, and M. Bennewitz, \u201cSubgoal-driven navigation in dynamic environments using attention-based deep reinforcement learning,\u201d arXiv preprint arXiv:2303.01443, 2023, https:\/\/doi.org\/10.1109\/ICAR58858.2023.10406349.","DOI":"10.1109\/ICAR58858.2023.10406349"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_113","doi-asserted-by":"crossref","unstructured":"D. Dobriborsci, R. Zashchitin, M. Kakanov, W. Aumer, and P. Osinenko, \u201cPredictive reinforcement learning: Map-less navigation method for mobile robot,\u201d J.\u00a0Intell. Manuf., vol.\u00a035, no. 8, pp.\u00a04217\u20134232, 2024, https:\/\/doi.org\/10.1007\/s10845-023-02197-y.","DOI":"10.1007\/s10845-023-02197-y"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_046","doi-asserted-by":"crossref","unstructured":"J. Li, M. Ran, H. Wang, and L. Xie, \u201cA behaviour-based mobile robot navigation method with deep reinforcement learning,\u201d Unmanned Syst., vol.\u00a09, no. 03, pp.\u00a0201\u2013209, 2021.","DOI":"10.1142\/S2301385021410041"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_073","doi-asserted-by":"crossref","unstructured":"W. Brink, \u201cLearning fine-grained control for mapless navigation,\u201d in 2020 International SAUPEC\/RobMech\/PRASA Conference, Cape Town, South Africa, IEEE, 2020, January, pp. 1\u20136.","DOI":"10.1109\/SAUPEC\/RobMech\/PRASA48453.2020.9041011"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_081","doi-asserted-by":"crossref","unstructured":"W. Chen, S. Zhou, Z. Pan, H. Zheng, and Y. Liu, \u201cMapless collaborative navigation for a multi-robot system based on the deep reinforcement learning,\u201d Appl. Sci., vol.\u00a09, no. 20, p.\u00a04198, 2019, https:\/\/doi.org\/10.3390\/app9204198.","DOI":"10.3390\/app9204198"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_047","doi-asserted-by":"crossref","unstructured":"A. Kamalova, S. G. Lee, and S. H. Kwon, \u201cOccupancy reward-driven exploration with deep reinforcement learning for Mobile robot system,\u201d Appl. Sci., vol.\u00a012, no. 18, p.\u00a09249, 2022, https:\/\/doi.org\/10.3390\/app12189249.","DOI":"10.3390\/app12189249"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_050","doi-asserted-by":"crossref","unstructured":"M. Dobrevski and D. Sko\u010daj, \u201cDeep reinforcement learning for map-less goal-driven robot navigation,\u201d Int. J.\u00a0Adv. Rob. Syst., vol.\u00a018, no. 1, 2021, Art. no. 1729881421992621, https:\/\/doi.org\/10.1177\/1729881421992621.","DOI":"10.1177\/1729881421992621"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_066","doi-asserted-by":"crossref","unstructured":"R. B. Grando et al.., \u201cDeep reinforcement learning for mapless navigation of a hybrid aerial underwater vehicle with medium transition,\u201d in 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China, IEEE, 2021, May, pp. 1088\u20131094.","DOI":"10.1109\/ICRA48506.2021.9561188"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_068","doi-asserted-by":"crossref","unstructured":"R. B. Grando et al.., \u201cDeterministic and stochastic analysis of deep reinforcement learning for low dimensional sensing-based navigation of Mobile robots,\u201d in 2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE), S\u00e3o Bernardo do Campo, SP, Brazil, IEEE, 2022, October, pp. 193\u2013198.","DOI":"10.1109\/LARS\/SBR\/WRE56824.2022.9995792"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_085","doi-asserted-by":"crossref","unstructured":"R. B. Grando, J.\u00a0C. de Jesus, V. A. Kich, A. H. Kolling, and P. L. J. Drews-Jr, \u201cDouble critic deep reinforcement learning for mapless 3d navigation of unmanned aerial vehicles,\u201d J.\u00a0Intell. Rob. Syst., vol.\u00a0104, no. 2, p.\u00a029, 2022, https:\/\/doi.org\/10.1007\/s10846-021-01568-y.","DOI":"10.1007\/s10846-021-01568-y"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_072","unstructured":"M. Botteghi, M. Khaled, B. Sirmacek, and M. Poel, \u201cEntropy-based exploration for mobile robot navigation: A learning-based approach,\u201d in Planning and Robotics Workshop, PlanRob, 2020."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_078","doi-asserted-by":"crossref","unstructured":"F. Leiva and J. Ruiz-del-Solar, \u201cRobust rl-based map-less local planning: Using 2d point clouds as observations,\u201d IEEE Rob. Autom. Lett., vol.\u00a05, no. 4, pp.\u00a05787\u20135794, 2020, https:\/\/doi.org\/10.1109\/LRA.2020.3010732.","DOI":"10.1109\/LRA.2020.3010732"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_063","doi-asserted-by":"crossref","unstructured":"N. Botteghi, B. Sirmacek, R. Schulte, M. Poel, and C. Brune, \u201cReinforcement learning helps slam: Learning to build maps. International archives of the photogrammetry,\u201d Rem. Sens. Spatial Inf. Sci., vol.\u00a043, 2020, https:\/\/doi.org\/10.5194\/isprs-archives-XLIII-B4-2020-329-2020.","DOI":"10.5194\/isprs-archives-XLIII-B4-2020-329-2020"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_083","doi-asserted-by":"crossref","unstructured":"G. Tang, N. Kumar, and K. P. Michmizos, \u201cReinforcement co-learning of deep and spiking neural networks for energy-efficient mapless navigation with neuromorphic hardware,\u201d in 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, IEEE, 2020, October, pp. 6090\u20136097.","DOI":"10.1109\/IROS45743.2020.9340948"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_054","doi-asserted-by":"crossref","unstructured":"W. Zhang, N. Liu, and Y. Zhang, \u201cLearn to navigate maplessly with varied LiDAR configurations: A support point-based approach,\u201d IEEE Rob. Autom. Lett., vol.\u00a06, no. 2, pp.\u00a01918\u20131925, 2021, https:\/\/doi.org\/10.1109\/LRA.2021.3061305.","DOI":"10.1109\/LRA.2021.3061305"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_055","doi-asserted-by":"crossref","unstructured":"\u00d3. Gil, A. Garrell, and A. Sanfeliu, \u201cSocial robot navigation tasks: Combining machine learning techniques and social force model,\u201d Sensors, vol.\u00a021, no. 21, p.\u00a07087, 2021, https:\/\/doi.org\/10.3390\/s21217087.","DOI":"10.3390\/s21217087"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_056","doi-asserted-by":"crossref","unstructured":"W. Zhu and M. Hayashibe, \u201cA hierarchical deep reinforcement learning framework with high efficiency and generalization for fast and safe navigation,\u201d IEEE Trans. Ind. Electron., vol.\u00a070, no. 5, pp.\u00a04962\u20134971, 2022, https:\/\/doi.org\/10.1109\/TIE.2022.3190850.","DOI":"10.1109\/TIE.2022.3190850"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_058","doi-asserted-by":"crossref","unstructured":"H. Gong, P. Wang, C. Ni, and N. Cheng, \u201cEfficient path planning for mobile robot based on deep deterministic policy gradient,\u201d Sensors, vol.\u00a022, no. 9, p.\u00a03579, 2022, https:\/\/doi.org\/10.3390\/s22093579.","DOI":"10.3390\/s22093579"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_059","doi-asserted-by":"crossref","unstructured":"R. Cimurs and E. A. Merch\u00e1n-Cruz, \u201cLeveraging expert demonstration features for deep reinforcement learning in floor cleaning robot navigation,\u201d Sensors, vol.\u00a022, no. 20, p.\u00a07750, 2022, https:\/\/doi.org\/10.3390\/s22207750.","DOI":"10.3390\/s22207750"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_061","doi-asserted-by":"crossref","unstructured":"M. F. R. Lee and S. H. Yusuf, \u201cMobile robot navigation using deep reinforcement learning,\u201d Processes, vol.\u00a010, no. 12, p.\u00a02748, 2022, https:\/\/doi.org\/10.3390\/pr10122748.","DOI":"10.3390\/pr10122748"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_043","doi-asserted-by":"crossref","unstructured":"T. Ran, L. Yuan, and J.\u00a0B. Zhang, \u201cScene perception based visual navigation of mobile robot in indoor environment,\u201d ISA Trans., vol.\u00a0109, pp.\u00a0389\u2013400, 2021, https:\/\/doi.org\/10.1016\/j.isatra.2020.10.023.","DOI":"10.1016\/j.isatra.2020.10.023"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_079","unstructured":"P. Blum, P. Crowley, and G. Lykotrafitis, \u201cVision-based navigation and obstacle avoidance via deep reinforcement learning,\u201d arXiv preprint arXiv:2211.05243, 2022."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_080","doi-asserted-by":"crossref","unstructured":"R. Druon, Y. Yoshiyasu, A. Kanezaki, and A. Watt, \u201cVisual object search by learning spatial context,\u201d IEEE Rob. Autom. Lett., vol.\u00a05, no. 2, pp.\u00a01279\u20131286, 2020, https:\/\/doi.org\/10.1109\/LRA.2020.2967677.","DOI":"10.1109\/LRA.2020.2967677"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_049","doi-asserted-by":"crossref","unstructured":"J. Kulh\u00e1nek, E. Derner, T. De Bruin, and R. Babu\u0161ka, \u201cVision-based navigation using deep reinforcement learning,\u201d in 2019 European Conference on Mobile Robots (ECMR), Prague, Czech Republic, IEEE, 2019, September, pp. 1\u20138.","DOI":"10.1109\/ECMR.2019.8870964"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_076","doi-asserted-by":"crossref","unstructured":"B. Xihan, O. Mendez, and S. Hadfield, \u201cRobot in a China shop: Using reinforcement learning for location-specific navigation behaviour,\u201d in 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China, IEEE, 2021, May, pp. 5959\u20135965.","DOI":"10.1109\/ICRA48506.2021.9561545"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_087","doi-asserted-by":"crossref","unstructured":"Q. Wu, D. Manocha, J. Wang, and K. Xu, \u201cNeonav: Improving the generalization of visual navigation via generating next expected observations,\u201d in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, New York, NY, USA, AAAI Press, 2020, April, pp. 10001\u201310008.","DOI":"10.1609\/aaai.v34i06.6556"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_084","doi-asserted-by":"crossref","unstructured":"W. Huang, Y. Zhou, X. He, and C. Lv, \u201cGoal-guided transformer-enabled reinforcement learning for efficient autonomous navigation,\u201d arXiv preprint arXiv:2301.00362, 2023, https:\/\/doi.org\/10.1109\/TITS.2023.3312453.","DOI":"10.1109\/TITS.2023.3312453"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_051","unstructured":"Y. Qiu, A. Pal, and H. I. Christensen, \u201cTarget driven visual navigation exploiting object relationships,\u201d arXiv preprint arXiv:2003.06749, vol.\u00a02, no. 7, 2020."},{"key":"2026011611304740982_j_jisys-2024-0547_ref_112","doi-asserted-by":"crossref","unstructured":"J. Costa de Jesus, V. A. Kich, A. H. Kolling, R. B. Grando, R. da Silva Guerra, and P. L. J. Drews-Jr, \u201cImage-based mapless navigation of a hybrid aerial-underwater vehicle using prioritized deep reinforcement learning,\u201d J.\u00a0Intell. Rob. Syst., vol.\u00a0111, no. 1, p.\u00a027, 2025, https:\/\/doi.org\/10.1007\/s10846-024-02206-z.","DOI":"10.1007\/s10846-024-02206-z"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_062","doi-asserted-by":"crossref","unstructured":"Q. Wu, X. Gong, K. Xu, D. Manocha, J. Dong, and J. Wang, \u201cTowards target-driven visual navigation in indoor scenes via generative imitation learning,\u201d IEEE Rob. Autom. Lett., vol.\u00a06, no. 1, pp.\u00a0175\u2013182, 2020, https:\/\/doi.org\/10.1109\/LRA.2020.3036597.","DOI":"10.1109\/LRA.2020.3036597"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_052","doi-asserted-by":"crossref","unstructured":"F. Li, C. Guo, B. Luo, and H. Zhang, \u201cMulti goals and multi scenes visual mapless navigation in indoor using meta-learning and scene priors,\u201d Neurocomputing, vol.\u00a0449, pp.\u00a0368\u2013377, 2021, https:\/\/doi.org\/10.1016\/j.neucom.2021.03.084.","DOI":"10.1016\/j.neucom.2021.03.084"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_075","doi-asserted-by":"crossref","unstructured":"A. Brandenburger, D. Rodriguez, and S. Behnke, \u201cMapless humanoid navigation using learned latent dynamics,\u201d in 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic (Hybrid), IEEE, 2021, September, pp. 1555\u20131561.","DOI":"10.1109\/IROS51168.2021.9636593"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_064","doi-asserted-by":"crossref","unstructured":"X. Ruan, P. Li, X. Zhu, and P. Liu, \u201cA target-driven visual navigation method based on intrinsic motivation exploration and space topological cognition,\u201d Sci. Rep., vol.\u00a012, no. 1, p.\u00a03462, 2022, https:\/\/doi.org\/10.1038\/s41598-022-07264-7.","DOI":"10.1038\/s41598-022-07264-7"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_071","doi-asserted-by":"crossref","unstructured":"H. Xue, R. Song, J. Petzold, B. Hein, H. Hamann, and E. Rueckert, \u201cEnd-to-end deep reinforcement learning for first-person pedestrian visual navigation in urban environments,\u201d in 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids), Ginowan, Japan, IEEE, 2022, November, pp. 350\u2013357.","DOI":"10.1109\/Humanoids53995.2022.10000201"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_057","doi-asserted-by":"crossref","unstructured":"A. Sivaranjani and B. Vinod, \u201cArtificial potential field incorporated Deep-Q-Network algorithm for Mobile robot path prediction,\u201d Intell. Autom. Soft Comput., vol. 35, no. 1, pp. 1135\u20131135, 2023, https:\/\/doi.org\/10.32604\/iasc.2023.028126.","DOI":"10.32604\/iasc.2023.028126"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_060","doi-asserted-by":"crossref","unstructured":"T. V. Dang and N. T. Bui, \u201cMulti-scale fully convolutional network-based semantic segmentation for Mobile robot navigation,\u201d Electronics, vol.\u00a012, no. 3, p.\u00a0533, 2023, https:\/\/doi.org\/10.3390\/electronics12030533.","DOI":"10.3390\/electronics12030533"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_086","doi-asserted-by":"crossref","unstructured":"L. Wang et al.., \u201cEfficient reinforcement learning for autonomous driving with parameterized skills and priors,\u201d arXiv preprint arXiv:2305.04412, 2023, https:\/\/doi.org\/10.15607\/RSS.2023.XIX.102.","DOI":"10.15607\/RSS.2023.XIX.102"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_053","doi-asserted-by":"crossref","unstructured":"N. Wang, Y. Wang, Y. Zhao, Y. Wang, and Z. Li, \u201cSim-to-Real: Mapless navigation for USVs using deep reinforcement learning,\u201d J.\u00a0Mar. Sci. Eng., vol.\u00a010, no. 7, p.\u00a0895, 2022, https:\/\/doi.org\/10.3390\/jmse10070895.","DOI":"10.3390\/jmse10070895"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_082","doi-asserted-by":"crossref","unstructured":"P. Yang, H. Liu, M. Roznere, and A. Q. Li, \u201cMonocular camera and single-beam sonar-based underwater collision-free navigation with domain randomization,\u201d in Robotics Research, Cham, Springer Nature Switzerland, 2023, pp.\u00a085\u2013101.","DOI":"10.1007\/978-3-031-25555-7_7"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_074","doi-asserted-by":"crossref","unstructured":"A. Wahid, A. Toshev, M. Fiser, and T. W. E. Lee, \u201cLong range neural navigation policies for the real world,\u201d in 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, IEEE, 2019, November, pp. 82\u201389.","DOI":"10.1109\/IROS40897.2019.8968004"},{"key":"2026011611304740982_j_jisys-2024-0547_ref_090","doi-asserted-by":"crossref","unstructured":"J. Wang, S. Elfwing, and E. Uchibe, \u201cModular deep reinforcement learning from reward and punishment for robot navigation,\u201d Neural Netw., vol.\u00a0135, pp.\u00a0115\u2013126, 2021, https:\/\/doi.org\/10.1016\/j.neunet.2020.12.001.","DOI":"10.1016\/j.neunet.2020.12.001"}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2024-0547\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2024-0547\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T11:32:10Z","timestamp":1768563130000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2024-0547\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,1]]},"references-count":124,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1,16]]},"published-print":{"date-parts":[[2026,1,23]]}},"alternative-id":["10.1515\/jisys-2024-0547"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2024-0547","relation":{},"ISSN":["2191-026X"],"issn-type":[{"value":"2191-026X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,1]]},"article-number":"20240547"}}