{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T16:57:37Z","timestamp":1781974657225,"version":"3.54.5"},"reference-count":187,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s44443-025-00216-x","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T14:00:01Z","timestamp":1755784801000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A survey on autonomous navigation for mobile robots: From traditional techniques to deep learning and large language models"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2672-3587","authenticated-orcid":false,"given":"Abderrahim","family":"Waga","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Said","family":"Benhlima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Bekri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jawad","family":"Abdouni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatima Zahrae","family":"Saber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"216_CR1","unstructured":"A Stars - an overview | ScienceDirect Topics. https:\/\/www.sciencedirect.com\/topics\/physics-and-astronomy\/a-stars Accessed 2025-01-08"},{"key":"216_CR2","doi-asserted-by":"publisher","unstructured":"Ab Wahab MN, Nazir A, Khalil A, Ho WJ, Akbar MF, Noor MHM, Mohamed ASA (2024) Improved genetic algorithm for mobile robot path planning in static environments. Expert Syst Appl 249:123762. https:\/\/doi.org\/10.1016\/j.eswa.2024.123762. Accessed 2025-01-08","DOI":"10.1016\/j.eswa.2024.123762"},{"key":"216_CR3","doi-asserted-by":"publisher","unstructured":"Abdouni J, Jarou T, Mzili T, Waga A, Bensassi K (2025) Challenges and constraints in trajectory planning for autonomous robots. Iraqi J Comput Sci Math 6(3). https:\/\/doi.org\/10.52866\/2788-7421.1274","DOI":"10.52866\/2788-7421.1274"},{"key":"216_CR4","doi-asserted-by":"publisher","unstructured":"Abdouni J, Jarou T, Waga A, El\u00a0Idrissi S, El\u00a0mahri M, Sefrioui I (2022) A new sampling strategy to improve the performance of mobile robot path planning algorithms. In: 2022 International Conference on Intelligent Systems and Computer Vision (ISCV), pp 1\u20137. https:\/\/doi.org\/10.1109\/ISCV54655.2022.9806128 . ISSN: 2768-0754. https:\/\/ieeexplore.ieee.org\/abstract\/document\/9806128 Accessed 2025-05-03","DOI":"10.1109\/ISCV54655.2022.9806128"},{"key":"216_CR5","doi-asserted-by":"publisher","unstructured":"Ahmed A, Abdalla T, Abed A (2015) Path Planning of Mobile Robot Using Fuzzy-Potential Field Method. Iraqi J Electrical Electron Eng 11(1):32\u201341. https:\/\/doi.org\/10.37917\/ijeee.11.1.4. Accessed 2025-05-03","DOI":"10.37917\/ijeee.11.1.4"},{"key":"216_CR6","doi-asserted-by":"publisher","unstructured":"Alferov G, Korolev V, Fedorov V, Khokhriakova A (2024) RETRACTED: Correction of the movement of the mobile robot using the modified algorithm. E3S Web of Conferences 549:08005. https:\/\/doi.org\/10.1051\/e3sconf\/202454908005 . Accessed 2025-01-09","DOI":"10.1051\/e3sconf\/202454908005"},{"key":"216_CR7","doi-asserted-by":"publisher","unstructured":"Alshammrei S, Boubaker S, Kolsi L (2022) Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance. Comput, Mater Continua 72(3):5939\u20135954. https:\/\/doi.org\/10.32604\/cmc.2022.028165 . Accessed 2025-01-08","DOI":"10.32604\/cmc.2022.028165"},{"key":"216_CR8","doi-asserted-by":"publisher","unstructured":"Alyasin A, Abbas EI, Hasan SD (2019) An Efficient Optimal Path Finding for Mobile Robot Based on Dijkstra Method. In: 2019 4th Scientific International Conference Najaf (SICN), pp 11\u201314. https:\/\/doi.org\/10.1109\/SICN47020.2019.9019345. https:\/\/ieeexplore.ieee.org\/document\/9019345 Accessed 2025-01-07","DOI":"10.1109\/SICN47020.2019.9019345"},{"key":"216_CR9","doi-asserted-by":"publisher","unstructured":"Arambula Cos\u00edo F, Padilla Casta\u00f1eda MA (2004) Autonomous robot navigation using adaptive potential fields. Math Comput Modell 40(9):1141\u20131156. https:\/\/doi.org\/10.1016\/j.mcm.2004.05.001. Accessed 2025-01-08","DOI":"10.1016\/j.mcm.2004.05.001"},{"key":"216_CR10","doi-asserted-by":"publisher","unstructured":"Balado J, D\u00edaz-Vilari\u00f1o L, Arias P, Lorenzo H (2019) Point clouds for direct pedestrian pathfinding in urban environments. ISPRS J Photogrammetry Remote Sens 148:184\u2013196. https:\/\/doi.org\/10.1016\/j.isprsjprs.2019.01.004. Accessed 2025-01-07","DOI":"10.1016\/j.isprsjprs.2019.01.004"},{"key":"216_CR11","doi-asserted-by":"publisher","unstructured":"Bhattacharyya S, Karmakar M (2023) Optimal Path Planning with Smart Energy Management Techniques Using Dijkstra\u2019s Algorithm. In: Bhattacharyya S, Banerjee J.S, K\u00f6ppen M (eds.) Human-Centric Smart Computing, pp 283\u2013291. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-19-5403-0_24","DOI":"10.1007\/978-981-19-5403-0_24"},{"key":"216_CR12","doi-asserted-by":"publisher","unstructured":"Billard A, Grollman D (2012) Imitation Learning in Robots. In: Seel NM (ed.) Encyclopedia of the Sciences of Learning, pp 1494\u20131496. Springer, Boston, MA. https:\/\/doi.org\/10.1007\/978-1-4419-1428-6_758. Accessed 2025-01-09","DOI":"10.1007\/978-1-4419-1428-6_758"},{"key":"216_CR13","doi-asserted-by":"publisher","unstructured":"Botvinick M, Ritter S, Wang JX, Kurth-Nelson Z, Blundell C, Hassabis D (2019) Reinforcement Learning, Fast and Slow. Trends in Cognitive Sci 23(5):408\u2013422. https:\/\/doi.org\/10.1016\/j.tics.2019.02.006. Accessed 2025-05-03","DOI":"10.1016\/j.tics.2019.02.006"},{"key":"216_CR14","unstructured":"Bremermann HJ (1958) The Evolution of Intelligence: The Nervous System as a Model of Its Environment. University of Washington, Department of Mathematics, ???"},{"key":"216_CR15","doi-asserted-by":"publisher","unstructured":"Cao Y, Mohamad Nor N (2024) An improved dynamic window approach algorithm for dynamic obstacle avoidance in mobile robot formation. Decision Analytics J 11:100471. https:\/\/doi.org\/10.1016\/j.dajour.2024.100471. Accessed 2025-01-08","DOI":"10.1016\/j.dajour.2024.100471"},{"key":"216_CR16","doi-asserted-by":"publisher","unstructured":"\u00c7elik OM, K\u00f6seo\u011flu M (2023) A Modified Dijkstra Algorithm for ROS Based Autonomous Mobile Robots. J Adv Res Natural Appl Sci 9(1):205\u2013217. https:\/\/doi.org\/10.28979\/jarnas.1119957 . Accessed 2025-01-08","DOI":"10.28979\/jarnas.1119957"},{"key":"216_CR17","doi-asserted-by":"publisher","unstructured":"C\u00e8sar-Tondreau B, Warnell G, Stump E, Kochersberger K, Waytowich NR (2021) Improving Autonomous Robotic Navigation Using Imitation Learning. Front Robot AI 8. https:\/\/doi.org\/10.3389\/frobt.2021.627730. Accessed 2025-05-03","DOI":"10.3389\/frobt.2021.627730"},{"key":"216_CR18","doi-asserted-by":"publisher","unstructured":"Chai R, Tsourdos A, Savvaris A, Chai S, Xia Y (2021) Solving constrained trajectory planning problems using biased particle swarm optimization. IEEE Trans Aerospace Electron Syst 57(3):1685\u20131701. https:\/\/doi.org\/10.1109\/TAES.2021.3050645. Accessed 2025-01-09","DOI":"10.1109\/TAES.2021.3050645"},{"key":"216_CR19","doi-asserted-by":"publisher","unstructured":"Cheng W-C, Ni Z, Zhong X, Wei M (2024) Autonomous robot goal seeking and collision avoidance in the physical world: An automated learning and evaluation framework based on the ppo method. Appl Sci 14(23). https:\/\/doi.org\/10.3390\/app142311020","DOI":"10.3390\/app142311020"},{"key":"216_CR20","doi-asserted-by":"publisher","unstructured":"Chen G, Luo N, Liu D, Zhao Z, Liang C (2021) Path planning for manipulators based on an improved probabilistic roadmap method. Robot Comput-Integrated Manufact 72:102196. https:\/\/doi.org\/10.1016\/j.rcim.2021.102196. Accessed 2025-01-08","DOI":"10.1016\/j.rcim.2021.102196"},{"key":"216_CR21","doi-asserted-by":"publisher","unstructured":"Chentoufi A, Fatmi AE, Bekri A, Benhlima S, Sabbane M, (2017) Genetic algorithms and dynamic weighted sum method for RNA alignment. In, (2017) Intelligent Systems and Computer Vision (ISCV), pp 1\u20135. IEEE, Fez, Morocco. https:\/\/doi.org\/10.1109\/ISACV.2017.8054965. http:\/\/ieeexplore.ieee.org\/document\/8054965\/ Accessed 2025-05-03","DOI":"10.1109\/ISACV.2017.8054965"},{"key":"216_CR22","doi-asserted-by":"publisher","unstructured":"Chen Y, Zhou X (2021) Path planning of robot based on improved ant colony algorithm in computer technology. J Phys: Conference Series 1744(4):042092. https:\/\/doi.org\/10.1088\/1742-6596\/1744\/4\/042092. Accessed 2025-01-09","DOI":"10.1088\/1742-6596\/1744\/4\/042092"},{"key":"216_CR23","doi-asserted-by":"publisher","unstructured":"Dai X, Long S, Zhang Z, Gong D (2019) Mobile robot path planning based on ant colony algorithm with a* heuristic method. Front Neurorobot 13. https:\/\/doi.org\/10.3389\/fnbot.2019.00015. Accessed 2025-01-09","DOI":"10.3389\/fnbot.2019.00015"},{"key":"216_CR24","doi-asserted-by":"publisher","unstructured":"Deshpande S, Kashyap AK, Patle BK (2023) A review on path planning ai techniques for mobile robots. Robotic Systems and Applications 3(1):27\u201346. https:\/\/doi.org\/10.21595\/rsa.2023.23090. Accessed 2025-05-03","DOI":"10.21595\/rsa.2023.23090"},{"key":"216_CR25","doi-asserted-by":"publisher","unstructured":"Dewang HS, Mohanty PK, Kundu S (2018) A robust path planning for mobile robot using smart particle swarm optimization. Procedia Computer Science 133:290\u2013297. https:\/\/doi.org\/10.1016\/j.procs.2018.07.036. Accessed 2025-01-09","DOI":"10.1016\/j.procs.2018.07.036"},{"key":"216_CR26","unstructured":"Dijkstra Algorithms - an overview | ScienceDirect Topics. https:\/\/www.sciencedirect.com\/topics\/computer-science\/dijkstra-algorithms Accessed 2025-01-08"},{"key":"216_CR27","doi-asserted-by":"publisher","unstructured":"Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1(1):269\u2013271. https:\/\/doi.org\/10.1007\/BF01386390. Accessed 2025-01-07","DOI":"10.1007\/BF01386390"},{"key":"216_CR28","doi-asserted-by":"publisher","unstructured":"Dobriborsci D, Chichkanov I, Zashchitin R, Osinenko P (2024b) Model-based reinforcement learning experimental study for mobile robot navigation. In: 2024 10th International conference on control, decision and information technologies (CoDIT), pp 1825\u20131830. https:\/\/doi.org\/10.1109\/CoDIT62066.2024.10708080 . ISSN: 2576-3555. https:\/\/ieeexplore.ieee.org\/document\/10708080\/ Accessed 2025-01-09","DOI":"10.1109\/CoDIT62066.2024.10708080"},{"key":"216_CR29","doi-asserted-by":"publisher","unstructured":"Dobriborsci D, Zashchitin R, Kakanov M, Aumer W, Osinenko P (2024a) Predictive reinforcement learning: map-less navigation method for mobile robot. J Intell Manufac 35(8):4217\u20134232. https:\/\/doi.org\/10.1007\/s10845-023-02197-y. Accessed 2025-01-09","DOI":"10.1007\/s10845-023-02197-y"},{"key":"216_CR30","doi-asserted-by":"publisher","unstructured":"Doma P, Arab A, Xiao X (2024) LLM-enhanced path planning: safe and efficient autonomous navigation with instructional inputs https:\/\/doi.org\/10.48550\/arXiv.2412.02655. arXiv:2412.02655 Accessed 2025-05-03","DOI":"10.48550\/arXiv.2412.02655"},{"key":"216_CR31","doi-asserted-by":"publisher","unstructured":"Dong X, Wang Y, Fang C, Ran K, Liu G (2025) Fhq-rrt*: An improved path planning algorithm for mobile robots to acquire high-quality paths faster. Sensors 25(7). https:\/\/doi.org\/10.3390\/s25072189","DOI":"10.3390\/s25072189"},{"key":"216_CR32","unstructured":"Dynamic Path Planning Algorithm for Mobile Robots: Leveraging Reinforcement Learning for Efficient Navigation \u2013 Journal of Internet Services and Information Security. https:\/\/jisis.org\/article\/2024.I2.014\/71132\/ Accessed 2025-05-03"},{"key":"216_CR33","doi-asserted-by":"publisher","unstructured":"Elallid BB, Benamar N, Bagaa M, Hadjadj-Aoul Y (2024) Enhancing autonomous driving navigation using soft actor-critic. Future Int 16(7). https:\/\/doi.org\/10.3390\/fi16070238","DOI":"10.3390\/fi16070238"},{"key":"216_CR34","doi-asserted-by":"publisher","unstructured":"Erke S, Bin D, Yiming N, Qi Z, Liang X, Dawei Z (2020) An improved A-Star based path planning algorithm for autonomous land vehicles. Int J Adv Robotic Syst 17(5):1729881420962263. https:\/\/doi.org\/10.1177\/1729881420962263. Accessed 2025-01-07","DOI":"10.1177\/1729881420962263"},{"key":"216_CR35","doi-asserted-by":"publisher","unstructured":"Esposito JM, Wright JN (2019) Matrix completion as a post-processing technique for probabilistic roadmaps. Int J Robot Res 38(2\u20133):388\u2013400. https:\/\/doi.org\/10.1177\/0278364919830554. Accessed 2025-01-08","DOI":"10.1177\/0278364919830554"},{"key":"216_CR36","doi-asserted-by":"publisher","unstructured":"Everett M, Chen YF, How JP (2018) Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning. https:\/\/doi.org\/10.48550\/arXiv.1805.01956. arXiv:1805.01956 Accessed 2025-05-03","DOI":"10.48550\/arXiv.1805.01956"},{"key":"216_CR37","doi-asserted-by":"publisher","unstructured":"Faisal M, Hedjar R, Al Sulaiman M, Al-Mutib K (2013) Fuzzy Logic Navigation and Obstacle Avoidance by a Mobile Robot in an Unknown Dynamic Environment. Int J Adv Robot Syst 10(1):37. https:\/\/doi.org\/10.5772\/54427. Accessed 2025-01-08","DOI":"10.5772\/54427"},{"key":"216_CR38","doi-asserted-by":"publisher","unstructured":"Fan H, Huang J, Huang X, Zhu H, Su H (2024) BI-RRT*: An improved path planning algorithm for secure and trustworthy mobile robots systems. Heliyon 10(5):26403. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e26403. Accessed 2025-01-08","DOI":"10.1016\/j.heliyon.2024.e26403"},{"key":"216_CR39","doi-asserted-by":"publisher","unstructured":"Fatmi AE, Chentoufi A, Bekri MA, Benhlima S, Sabbane M, (2017) A heuristic algorithm for RNA secondary structure based on genetic algorithm. In, (2017) Intelligent Systems and Computer Vision (ISCV), pp 1\u20137. IEEE, Fez, Morocco. https:\/\/doi.org\/10.1109\/ISACV.2017.8054964. http:\/\/ieeexplore.ieee.org\/document\/8054964\/ Accessed 2025-05-03","DOI":"10.1109\/ISACV.2017.8054964"},{"key":"216_CR40","doi-asserted-by":"publisher","unstructured":"Feng S, Qian Y, Wang Y (2021) Collision avoidance method of autonomous vehicle based on improved artificial potential field algorithm. Proceed Institution Mech Eng, Part D: J Automobile Eng 235(14):3416\u20133430. https:\/\/doi.org\/10.1177\/09544070211014319. Accessed 2025-01-08","DOI":"10.1177\/09544070211014319"},{"key":"216_CR41","doi-asserted-by":"publisher","unstructured":"Ferguson D, Stentz A (2006) Using interpolation to improve path planning: The Field D$$^{*}$$ algorithm. J Field Robot 23(2):79\u2013101. https:\/\/doi.org\/10.1002\/rob.20109. Accessed 2025-01-08","DOI":"10.1002\/rob.20109"},{"key":"216_CR42","doi-asserted-by":"publisher","unstructured":"Fink W, Baker VR, Brooks AJ-W, Flammia M, Dohm JM, Tarbell MA (2019) Globally optimal rover traverse planning in 3D using Dijkstra\u2019s algorithm for multi-objective deployment scenarios. Planetary Space Sci 179:104707. https:\/\/doi.org\/10.1016\/j.pss.2019.104707. Accessed 2025-01-07","DOI":"10.1016\/j.pss.2019.104707"},{"key":"216_CR43","doi-asserted-by":"publisher","unstructured":"Foead D, Ghifari A, Kusuma MB, Hanafiah N, Gunawan E (2021) A systematic literature review of a* pathfinding. Procedia Computer Science 179:507\u2013514. https:\/\/doi.org\/10.1016\/j.procs.2021.01.034. Accessed 2025-05-02","DOI":"10.1016\/j.procs.2021.01.034"},{"key":"216_CR44","doi-asserted-by":"publisher","unstructured":"Fox D, Burgard W, Thrun S (1997) The dynamic window approach to collision avoidance. IEEE Robot Autom Mag 4(1):23\u201333. https:\/\/doi.org\/10.1109\/100.580977. Accessed 2025-01-08","DOI":"10.1109\/100.580977"},{"key":"216_CR45","doi-asserted-by":"publisher","unstructured":"Fransen K, Van Eekelen J (2023) Efficient path planning for automated guided vehicles using A* (Astar) algorithm incorporating turning costs in search heuristic. Int J Product Res 61(3):707\u2013725. https:\/\/doi.org\/10.1080\/00207543.2021.2015806. Accessed 2025-01-07","DOI":"10.1080\/00207543.2021.2015806"},{"key":"216_CR46","doi-asserted-by":"publisher","unstructured":"Garcia MAP, Montiel O, Castillo O, Sep\u00falveda R, Melin P (2009) Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Appl Soft Comput 9(3):1102\u20131110. https:\/\/doi.org\/10.1016\/j.asoc.2009.02.014. Accessed 2025-01-09","DOI":"10.1016\/j.asoc.2009.02.014"},{"key":"216_CR47","doi-asserted-by":"publisher","unstructured":"Garg S, Devi B (2023) Shortest Path Finding using Modified Dijkstra\u2019s algorithm with Adaptive Penalty Function. In: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp 1\u20139. https:\/\/doi.org\/10.1109\/ICCCNT56998.2023.10308130 . ISSN: 2473-7674. https:\/\/ieeexplore.ieee.org\/document\/10308130?denied= Accessed 2025-01-07","DOI":"10.1109\/ICCCNT56998.2023.10308130"},{"key":"216_CR48","doi-asserted-by":"publisher","unstructured":"Giusti A, Guzzi J, Cire\u015fan DC, He F-L, Rodr\u00edguez JP, Fontana F, Faessler M, Forster C, Schmidhuber J, Caro GD, Scaramuzza D, Gambardella LM (2016) A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots. IEEE Robot Autom Lett 1(2):661\u2013667. https:\/\/doi.org\/10.1109\/LRA.2015.2509024. Accessed 2025-05-03","DOI":"10.1109\/LRA.2015.2509024"},{"key":"216_CR49","doi-asserted-by":"publisher","unstructured":"Guo D, Wang J, Zhao JB, Sun F, Gao S, Li CD, Li MH, Li CC (2019) A vehicle path planning method based on a dynamic traffic network that considers fuel consumption and emissions. Sci Total Environ 663:935\u2013943. https:\/\/doi.org\/10.1016\/j.scitotenv.2019.01.222. Accessed 2025-01-07","DOI":"10.1016\/j.scitotenv.2019.01.222"},{"key":"216_CR50","doi-asserted-by":"publisher","unstructured":"Guruji AK, Agarwal H, Parsediya DK (2016) Time-efficient a* algorithm for robot path planning. Procedia Technol 23:144\u2013149. https:\/\/doi.org\/10.1016\/j.protcy.2016.03.010. Accessed 2025-01-07","DOI":"10.1016\/j.protcy.2016.03.010"},{"key":"216_CR51","unstructured":"Haarnoja T, Zhou A, Abbeel P, Levine S (2018) Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. arXiv:1801.01290"},{"key":"216_CR52","doi-asserted-by":"publisher","unstructured":"Hamandi M, D\u2019Arcy M, Fazli P (2019) DeepMoTIon: Learning to Navigate Like Humans. https:\/\/doi.org\/10.48550\/arXiv.1803.03719. arXiv:1803.03719 Accessed 2025-05-03","DOI":"10.48550\/arXiv.1803.03719"},{"key":"216_CR53","doi-asserted-by":"publisher","unstructured":"He Z, Liu C, Chu X, Negenborn RR, Wu Q (2022) Dynamic anti-collision A-star algorithm for multi-ship encounter situations. Appl Ocean Res 118:102995. https:\/\/doi.org\/10.1016\/j.apor.2021.102995. Accessed 2025-01-08","DOI":"10.1016\/j.apor.2021.102995"},{"key":"216_CR54","doi-asserted-by":"publisher","unstructured":"Henkel C, Bubeck A, Xu W (2016) Energy efficient dynamic window approach for local path planning in mobile service robotics*. IFAC-PapersOnLine 49(15):32\u201337. https:\/\/doi.org\/10.1016\/j.ifacol.2016.07.610. Accessed 2025-01-08","DOI":"10.1016\/j.ifacol.2016.07.610"},{"key":"216_CR55","doi-asserted-by":"crossref","unstructured":"Holland JH (1992) Genetic Algorithms. https:\/\/www.scientificamerican.com\/article\/genetic-algorithms\/ Accessed 2025-05-03","DOI":"10.1038\/scientificamerican0792-66"},{"key":"216_CR56","doi-asserted-by":"publisher","unstructured":"Hong Z, Chun-Long S, Zi-Jun Z, Wei A, De-Qiang Z, Jing-Jing W (2015) A modified dynamic window approach to obstacle avoidance combined with fuzzy logic. In: 2015 14th International symposium on distributed computing and applications for business engineering and science (DCABES), pp 523\u2013526. https:\/\/doi.org\/10.1109\/DCABES.2015.136. https:\/\/ieeexplore.ieee.org\/abstract\/document\/7429670 Accessed 2025-01-08","DOI":"10.1109\/DCABES.2015.136"},{"key":"216_CR57","doi-asserted-by":"publisher","unstructured":"Hou J, Jiang W, Luo Z, Yang L, Hu X, Guo B (2024) Dynamic path planning for mobile robots by integrating improved sparrow search algorithm and dynamic window approach. Actuators 13(1):24. https:\/\/doi.org\/10.3390\/act13010024. Accessed 2025-01-08","DOI":"10.3390\/act13010024"},{"key":"216_CR58","volume-title":"Study on path planning and location of mobile robot based on intelligent optimization algorithm","author":"C Huang","year":"2018","unstructured":"Huang C (2018) Study on path planning and location of mobile robot based on intelligent optimization algorithm. Dalian Jiaotong University, Dalian, China"},{"key":"216_CR59","doi-asserted-by":"publisher","unstructured":"Huang J, Zhang Z, Ruan X (2024) An improved Dyna-Q algorithm inspired by the forward prediction mechanism in the rat brain for mobile robot path planning. Biomimetics 9(6):315. https:\/\/doi.org\/10.3390\/biomimetics9060315. Accessed 2025-05-03","DOI":"10.3390\/biomimetics9060315"},{"key":"216_CR60","doi-asserted-by":"publisher","unstructured":"Hu Y, Yang SX (2022) A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments. https:\/\/doi.org\/10.48550\/arXiv.2209.01482. arXiv:2209.01482 Accessed 2025-01-08","DOI":"10.48550\/arXiv.2209.01482"},{"key":"216_CR61","doi-asserted-by":"publisher","unstructured":"Jiao Z, Ma K, Rong Y, Wang P, Zhang H, Wang S (2018) A path planning method using adaptive polymorphic ant colony algorithm for smart wheelchairs. J Computat Sci 25:50\u201357. https:\/\/doi.org\/10.1016\/j.jocs.2018.02.004. Accessed 2025-01-09","DOI":"10.1016\/j.jocs.2018.02.004"},{"key":"216_CR62","doi-asserted-by":"publisher","unstructured":"Ji Y, Liu B (2022) Research and implementation of robot path planning based on ant colony algorithm. J Phys: Conference Series 2171(1):012074. https:\/\/doi.org\/10.1088\/1742-6596\/2171\/1\/012074. Accessed 2025-01-09","DOI":"10.1088\/1742-6596\/2171\/1\/012074"},{"key":"216_CR63","doi-asserted-by":"publisher","unstructured":"Jingcun W, Xiaotong Z, Bin C, Heping C (2007) A heuristic optimization path-finding algorithm based on Dijkstra algorithm. Chinese J Eng 29(3):346\u2013350. https:\/\/doi.org\/10.13374\/j.issn1001-053x.2007.03.022. Accessed 2025-01-07","DOI":"10.13374\/j.issn1001-053x.2007.03.022"},{"key":"216_CR64","doi-asserted-by":"publisher","unstructured":"Kabas B (2022) Autonomous uav navigation via deep reinforcement learning using ppo. In: 2022 30th Signal processing and communications applications conference (SIU), pp 1\u20134. https:\/\/doi.org\/10.1109\/SIU55565.2022.9864769","DOI":"10.1109\/SIU55565.2022.9864769"},{"key":"216_CR65","doi-asserted-by":"publisher","unstructured":"Kadry S, Alferov G, Fedorov V (2020) D-Star Algorithm Modification. Int J Online Biomed Eng (iJOE) 16(08):108\u2013113. https:\/\/doi.org\/10.3991\/ijoe.v16i08.14243. Accessed 2025-05-03","DOI":"10.3991\/ijoe.v16i08.14243"},{"key":"216_CR66","doi-asserted-by":"publisher","unstructured":"Kalyan KS (2024) A survey of GPT-3 family large language models including ChatGPT and GPT-4. Natural Lang Process J 6:100048. https:\/\/doi.org\/10.1016\/j.nlp.2023.100048. Accessed 2025-05-03","DOI":"10.1016\/j.nlp.2023.100048"},{"key":"216_CR67","doi-asserted-by":"publisher","unstructured":"Karaman S, Frazzoli E (2011) Sampling-based algorithms for optimal motion planning. Int J Robot Res 30(7):846\u2013894. https:\/\/doi.org\/10.1177\/0278364911406761. Accessed 2025-05-03","DOI":"10.1177\/0278364911406761"},{"key":"216_CR68","doi-asserted-by":"publisher","unstructured":"Karnan H, Warnell G, Xiao X, Stone P (2021) VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation. https:\/\/doi.org\/10.48550\/arXiv.2105.09371. arXiv:2105.09371 Accessed 2025-05-03","DOI":"10.48550\/arXiv.2105.09371"},{"key":"216_CR69","doi-asserted-by":"publisher","unstructured":"Katona K, Neamah HA, Korondi P (2024) Obstacle avoidance and path planning methods for autonomous navigation of mobile robot. Sensors 24(11):3573. https:\/\/doi.org\/10.3390\/s24113573. Accessed 2025-01-07","DOI":"10.3390\/s24113573"},{"key":"216_CR70","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995 - International Conference on Neural Networks, vol. 4, pp 1942\u201319484. https:\/\/doi.org\/10.1109\/ICNN.1995.488968. https:\/\/ieeexplore.ieee.org\/document\/488968 Accessed 2025-01-09","DOI":"10.1109\/ICNN.1995.488968"},{"key":"216_CR71","doi-asserted-by":"publisher","unstructured":"Khatib O (1985) Real-time obstacle avoidance for manipulators and mobile robots. In: 1985 IEEE International conference on robotics and automation proceedings, vol. 2, pp 500\u2013505. https:\/\/doi.org\/10.1109\/ROBOT.1985.1087247. https:\/\/ieeexplore.ieee.org\/document\/1087247 Accessed 2025-01-08","DOI":"10.1109\/ROBOT.1985.1087247"},{"key":"216_CR72","doi-asserted-by":"publisher","unstructured":"Kim DK, Chen T (2015) Deep Neural Network for Real-Time Autonomous Indoor Navigation. https:\/\/doi.org\/10.48550\/ARXIV.1511.04668. arXiv:1511.04668 Accessed 2025-05-03","DOI":"10.48550\/ARXIV.1511.04668"},{"key":"216_CR73","unstructured":"Kolve E, Mottaghi R, Han W, VanderBilt E, Weihs L, Herrasti A, Deitke M, Ehsani K, Gordon D, Zhu Y, Kembhavi A, Gupta A, Farhadi A (2022) AI2-THOR: An Interactive 3D Environment for Visual AI. arXiv:1712.05474"},{"key":"216_CR74","doi-asserted-by":"crossref","unstructured":"Krantz J, Wijmans E, Majumdar A, Batra D, Lee S (2020) Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments. arXiv:2004.02857","DOI":"10.1007\/978-3-030-58604-1_7"},{"key":"216_CR75","doi-asserted-by":"publisher","unstructured":"Lamini C, Benhlima S, Elbekri A (2018) Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning. Procedia Comput Sci 127:180\u2013189. https:\/\/doi.org\/10.1016\/j.procs.2018.01.113. Accessed 2025-01-08","DOI":"10.1016\/j.procs.2018.01.113"},{"key":"216_CR76","doi-asserted-by":"publisher","unstructured":"Lamini C, Fathi Y, Benhlima S (2017) A fuzzy path planning system based on a collaborative reinforcement learning. Int Rev Autom Control (IREACO) 10(2):126. https:\/\/doi.org\/10.15866\/ireaco.v10i2.10623. Accessed 2025-05-03","DOI":"10.15866\/ireaco.v10i2.10623"},{"key":"216_CR77","doi-asserted-by":"publisher","unstructured":"Lamini C, Fathi Y, Benhlima S (2017) H-mas architecture and reinforcement learning method for autonomous robot path planning. In: 2017 Intelligent systems and computer vision (ISCV), pp 1\u20137. https:\/\/doi.org\/10.1109\/ISACV.2017.8054978","DOI":"10.1109\/ISACV.2017.8054978"},{"key":"216_CR78","doi-asserted-by":"publisher","unstructured":"Latif E (2024) 3P-LLM: probabilistic path planning using large language model for autonomous robot navigation. https:\/\/doi.org\/10.48550\/arXiv.2403.18778. arXiv:2403.18778 Accessed 2025-05-03","DOI":"10.48550\/arXiv.2403.18778"},{"key":"216_CR79","doi-asserted-by":"publisher","unstructured":"Lazarowska A (2019) Discrete artificial potential field approach to mobile robot path planning. IFAC-PapersOnLine 52(8):277\u2013282. https:\/\/doi.org\/10.1016\/j.ifacol.2019.08.083. Accessed 2025-01-08","DOI":"10.1016\/j.ifacol.2019.08.083"},{"key":"216_CR80","doi-asserted-by":"publisher","unstructured":"Lee M-FR, Yusuf SH (2022) Mobile robot navigation using deep reinforcement learning. Processes 10(12):2748. https:\/\/doi.org\/10.3390\/pr10122748. Accessed 2025-05-03","DOI":"10.3390\/pr10122748"},{"key":"216_CR81","doi-asserted-by":"publisher","unstructured":"Li X (2021) Path planning of intelligent mobile robot based on Dijkstra algorithm. J Phys: Conference Series 2083(4):042034. https:\/\/doi.org\/10.1088\/1742-6596\/2083\/4\/042034. Accessed 2025-01-08","DOI":"10.1088\/1742-6596\/2083\/4\/042034"},{"key":"216_CR82","doi-asserted-by":"publisher","unstructured":"Li Y, Chen X (2005) Mobile robot navigation using particle swarm optimization and adaptive NN. In: Wang L, Chen K, Ong YS (eds.) Advances in Natural Computation, pp 628\u2013631. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/11539902_76","DOI":"10.1007\/11539902_76"},{"key":"216_CR83","unstructured":"Likhachev M, Ferguson D, Gordon G, Stentz A, Thrun S (2005) Anytime dynamic a*: an anytime, replanning algorithm. In: Proceedings of the fifteenth international conference on international conference on automated planning and scheduling. ICAPS\u201905, pp 262\u2013271. AAAI Press, ???"},{"key":"216_CR84","doi-asserted-by":"publisher","unstructured":"Li Y, Li J, Zhou W, Yao Q, Nie J, Qi X (2022) Robot path planning navigation for dense planting red Jujube orchards based on the joint improved a* and DWA algorithms under laser SLAM. Agriculture 12(9):1445. https:\/\/doi.org\/10.3390\/agriculture12091445. Accessed 2025-01-08","DOI":"10.3390\/agriculture12091445"},{"key":"216_CR85","doi-asserted-by":"publisher","unstructured":"Liu Y, Li X (2022) A hybrid mobile robot path planning scheme based on modified gray wolf optimization and situation assessment. J Robot 2022:1\u20139. https:\/\/doi.org\/10.1155\/2022\/4167170. Accessed 2025-01-08","DOI":"10.1155\/2022\/4167170"},{"key":"216_CR86","doi-asserted-by":"crossref","unstructured":"Liu F, Liang S, Xian DX (2014) Optimal Path Planning for Mobile Robot Using Tailored Genetic Algorithm. TELKOMNIKA Indonesian J Electrical Eng 12(1):1\u20139. Accessed 2025-01-08","DOI":"10.11591\/telkomnika.v12i1.3127"},{"key":"216_CR87","doi-asserted-by":"publisher","unstructured":"Liu B, Liu C (2022) Path planning of mobile robots based on improved RRT algorithm. J Phys: Conference Series 2216(1):012020. https:\/\/doi.org\/10.1088\/1742-6596\/2216\/1\/012020. Accessed 2025-01-08","DOI":"10.1088\/1742-6596\/2216\/1\/012020"},{"key":"216_CR88","doi-asserted-by":"publisher","unstructured":"Liu L, Wang X, Yang X, Liu H, Li J, Wang P (2023) Path planning techniques for mobile robots: Review and prospect. Expert Systems with Applications 227:120254. https:\/\/doi.org\/10.1016\/j.eswa.2023.120254. Accessed 2025-05-03","DOI":"10.1016\/j.eswa.2023.120254"},{"key":"216_CR89","doi-asserted-by":"publisher","unstructured":"Liu C, Xie S, Sui X, Huang Y, Ma X, Guo N, Yang F (2023) PRM-D* Method for Mobile Robot Path Planning. Sensors 23(7):3512. https:\/\/doi.org\/10.3390\/s23073512. Accessed 2025-01-08","DOI":"10.3390\/s23073512"},{"key":"216_CR90","doi-asserted-by":"publisher","unstructured":"Liu C, Yan X, Liu C, Li G (2010) Dynamic Path Planning for Mobile Robot Based on Improved Genetic Algorithm. Chinese J Electron 19(2):245\u2013248. https:\/\/doi.org\/10.23919\/CJE.2010.10151834. Accessed 2025-01-08","DOI":"10.23919\/CJE.2010.10151834"},{"key":"216_CR91","doi-asserted-by":"publisher","unstructured":"Liu S, Zhang J, Wang L, Gao RX (2024) Vision AI-based human-robot collaborative assembly driven by autonomous robots. CIRP Ann 73(1):13\u201316. https:\/\/doi.org\/10.1016\/j.cirp.2024.03.004. Accessed 2025-05-03","DOI":"10.1016\/j.cirp.2024.03.004"},{"key":"216_CR92","doi-asserted-by":"publisher","unstructured":"Lun G, Xiaoguang Z, Yongcong W (2024) Learning to drive as humans do: Reinforcement learning for autonomous navigation. Int J Adv Robot Syst 21(5):17298806241278910. https:\/\/doi.org\/10.1177\/17298806241278910, https:\/\/arxiv.org\/abs\/doi.org\/10.1177\/17298806241278910","DOI":"10.1177\/17298806241278910"},{"key":"216_CR93","doi-asserted-by":"publisher","unstructured":"Luo J, Wang Z-X, Pan K-L (2022) Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method. IEEE Access 10:108276\u2013108284. https:\/\/doi.org\/10.1109\/ACCESS.2022.3212741. Accessed 2025-01-08","DOI":"10.1109\/ACCESS.2022.3212741"},{"key":"216_CR94","doi-asserted-by":"publisher","unstructured":"Lu Y, Yi S, Liu Y, Ji Y (2016) A novel path planning method for biomimetic robot based on deep learning. Assembly Autom 36(2):186\u2013191. https:\/\/doi.org\/10.1108\/AA-11-2015-108. Accessed 2025-05-03","DOI":"10.1108\/AA-11-2015-108"},{"key":"216_CR95","doi-asserted-by":"publisher","unstructured":"Ma X, Gong R, Tan Y, Mei H, Li C (2022) Path planning of mobile robot based on improved PRM based on cubic spline. Wireless Commun Mobile Comput 2022:1\u201312. https:\/\/doi.org\/10.1155\/2022\/1632698. Accessed 2025-01-08","DOI":"10.1155\/2022\/1632698"},{"key":"216_CR96","doi-asserted-by":"publisher","unstructured":"Mahmud KR, Wang L, Hassan S, Zhang Z (2025) A knowledge-driven framework for Robotic Odor Source Localization using large language models. Robot Autonomous Syst 186:104915. https:\/\/doi.org\/10.1016\/j.robot.2025.104915. Accessed 2025-05-03","DOI":"10.1016\/j.robot.2025.104915"},{"key":"216_CR97","doi-asserted-by":"publisher","unstructured":"Mahmud MSA, Abidin MSZ, Mohamed Z, Rahman MKIA, Iida M (2019) Multi-objective path planner for an agricultural mobile robot in a virtual greenhouse environment. Comput Electron Agric 157:488\u2013499. https:\/\/doi.org\/10.1016\/j.compag.2019.01.016. Accessed 2025-01-08","DOI":"10.1016\/j.compag.2019.01.016"},{"key":"216_CR98","unstructured":"Maniezzo V (1991) The ant system: An autocatalytic optimizing process. Italy: Dipartimento di Elettronica, Politecnico di Milano. Accessed 2025-05-03"},{"key":"216_CR99","doi-asserted-by":"publisher","unstructured":"Maoudj A, Christensen AL (2021) Q-learning-based navigation for mobile robots in continuous and dynamic environments. In: 2021 IEEE 17th International conference on automation science and engineering (CASE), pp 1338\u20131345. https:\/\/doi.org\/10.1109\/CASE49439.2021.9551641. ISSN: 2161-8089. https:\/\/ieeexplore.ieee.org\/document\/9551641 Accessed 2025-01-09","DOI":"10.1109\/CASE49439.2021.9551641"},{"key":"216_CR100","doi-asserted-by":"publisher","unstructured":"Martins OO, Adekunle AA, Olaniyan OM, Bolaji BO (2022) An Improved multi-objective a-star algorithm for path planning in a large workspace: Design, Implementation, and Evaluation. Sci African 15:01068. https:\/\/doi.org\/10.1016\/j.sciaf.2021.e01068. Accessed 2025-01-08","DOI":"10.1016\/j.sciaf.2021.e01068"},{"key":"216_CR101","doi-asserted-by":"publisher","unstructured":"Melchiorre M, Salamina L, Scimmi LS, Mauro S, Pastorelli S (2023) Experiments on the Artificial Potential Field with Local Attractors for Mobile Robot Navigation. Robotics 12(3):81. https:\/\/doi.org\/10.3390\/robotics12030081. Accessed 2025-01-08","DOI":"10.3390\/robotics12030081"},{"key":"216_CR102","doi-asserted-by":"crossref","unstructured":"Meng S, Wang Y, Yang C-F, Peng N, Chang K-W (2025) LLM-A*: large language model enhanced incremental heuristic search on path planning. arXiv:2407.02511","DOI":"10.21203\/rs.3.rs-4613568\/v1"},{"key":"216_CR103","doi-asserted-by":"publisher","unstructured":"Mirowski P, Pascanu R, Viola F, Soyer H, Ballard AJ, Banino A, Denil M, Goroshin R, Sifre L, Kavukcuoglu K, Kumaran D, Hadsell R (2017) Learning to Navigate in Complex Environments. https:\/\/doi.org\/10.48550\/arXiv.1611.03673. arXiv:1611.03673 Accessed 2025-05-03","DOI":"10.48550\/arXiv.1611.03673"},{"key":"216_CR104","doi-asserted-by":"publisher","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M (2013) Playing Atari with Deep Reinforcement Learning. https:\/\/doi.org\/10.48550\/arXiv.1312.5602. arXiv:1312.5602 Accessed 2025-05-03","DOI":"10.48550\/arXiv.1312.5602"},{"key":"216_CR105","doi-asserted-by":"crossref","unstructured":"Mohammad N, Bezzo N (2025) Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments. arXiv:2503.08479","DOI":"10.1109\/ICRA55743.2025.11128470"},{"key":"216_CR106","doi-asserted-by":"publisher","unstructured":"Mohanty PK, Singh AK, Kumar A, Mahto MK, Kundu S (2022) Path planning techniques for mobile robots: a review. In: Abraham A, Engelbrecht A, Scotti F, Gandhi N, Manghirmalani\u00a0Mishra P, Fortino G, Sakalauskas V, Pllana S (eds.) Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021), pp 657\u2013667. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-96302-6_62","DOI":"10.1007\/978-3-030-96302-6_62"},{"key":"216_CR107","doi-asserted-by":"publisher","unstructured":"Munaf A, Almusawi ARJ (2024) Integration of Q-Learning and PID Controller for Mobile Robots Trajectory Tracking in Unknown Environments. J Europ\u00e9en des Syst\u00e8mes Automatis\u00e9s 57(4):1023\u20131033. https:\/\/doi.org\/10.18280\/jesa.570410. Accessed 2025-05-03","DOI":"10.18280\/jesa.570410"},{"key":"216_CR108","doi-asserted-by":"publisher","unstructured":"Nasir J, Islam F, Malik U, Ayaz Y, Hasan O, Khan M, Muhammad MS (2013) RRT*-SMART: A Rapid Convergence Implementation of RRT*. Int J Adv Robot Syst 10(7):299. https:\/\/doi.org\/10.5772\/56718. Accessed 2025-01-08","DOI":"10.5772\/56718"},{"key":"216_CR109","doi-asserted-by":"publisher","unstructured":"Nazir A, Wang Z (2023) A comprehensive survey of ChatGPT: Advancements, applications, prospects, and challenges. Meta-Radiol 1(2):100022. https:\/\/doi.org\/10.1016\/j.metrad.2023.100022. Accessed 2025-05-03","DOI":"10.1016\/j.metrad.2023.100022"},{"key":"216_CR110","doi-asserted-by":"publisher","unstructured":"Nozari S, Krayani A, Marin P, Marcenaro L, Gomez DM, Regazzoni C (2024) Modeling autonomous vehicle responses to novel observations using hierarchical cognitive representations inspired active inference. Computers 13(7):161. https:\/\/doi.org\/10.3390\/computers13070161. Accessed 2025-05-03","DOI":"10.3390\/computers13070161"},{"key":"216_CR111","unstructured":"Optimal and Efficient Path Planning for Partially-Known Environments. https:\/\/www.ri.cmu.edu\/publications\/optimal-and-efficient-path-planning-for-partially-known-environments\/ Accessed 2025-01-08"},{"key":"216_CR112","doi-asserted-by":"publisher","unstructured":"Ouach MK, Eren T (2022) PRM path smoothening by circular arc fillet method for mobile robot navigation. https:\/\/doi.org\/10.48550\/arXiv.2112.03604. arXiv:2112.03604 Accessed 2025-01-08","DOI":"10.48550\/arXiv.2112.03604"},{"key":"216_CR113","doi-asserted-by":"publisher","unstructured":"Phung MD, Ha QP (2021) Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization. Appl Soft Comput 107:107376. https:\/\/doi.org\/10.1016\/j.asoc.2021.107376. Accessed 2025-01-09","DOI":"10.1016\/j.asoc.2021.107376"},{"key":"216_CR114","doi-asserted-by":"publisher","unstructured":"Qiao L, Luo X, Luo Q (2022) An optimized probabilistic roadmap algorithm for path planning of mobile robots in complex environments with narrow channels. Sensors 22(22):8983. https:\/\/doi.org\/10.3390\/s22228983. Accessed 2025-01-08","DOI":"10.3390\/s22228983"},{"key":"216_CR115","doi-asserted-by":"publisher","unstructured":"Qin H, Wu Z, Pan P, Wang M, Qin J, Ye Z (2022) A new multimodal planning algorithm based on PRM. ITM Web of Conferences 47:02005. https:\/\/doi.org\/10.1051\/itmconf\/20224702005. Accessed 2025-05-03","DOI":"10.1051\/itmconf\/20224702005"},{"key":"216_CR116","doi-asserted-by":"publisher","unstructured":"Qiuyun T, Hongyan S, Hengwei G, Ping W (2021) Improved particle swarm optimization algorithm for AGV path planning. IEEE Access 9:33522\u201333531. https:\/\/doi.org\/10.1109\/ACCESS.2021.3061288. Accessed 2025-01-09","DOI":"10.1109\/ACCESS.2021.3061288"},{"key":"216_CR117","doi-asserted-by":"publisher","unstructured":"Quan H, Li Y, Zhang Y (2020) A novel mobile robot navigation method based on deep reinforcement learning. Int J Adv Robotic Syst 17(3):1729881420921672. https:\/\/doi.org\/10.1177\/1729881420921672. Accessed 2025-05-03","DOI":"10.1177\/1729881420921672"},{"key":"216_CR118","doi-asserted-by":"publisher","unstructured":"Rafai ANA, Adzhar N, Jaini NI (2022) A Review on Path Planning and Obstacle Avoidance Algorithms for Autonomous Mobile Robots. J Robot 2022:1\u201314. https:\/\/doi.org\/10.1155\/2022\/2538220. Accessed 2025-05-03","DOI":"10.1155\/2022\/2538220"},{"key":"216_CR119","unstructured":"Raheem FA, Hameed UI (2018) Path Planning Algorithm using D* Heuristic Method Based on PSO in Dynamic Environment. Am Sci Res J Eng, Technol, Sci 49(1):257\u2013271. Accessed 2025-01-08"},{"key":"216_CR120","doi-asserted-by":"publisher","unstructured":"Ran K, Wang Y, Fang C, Chai Q, Dong X, Liu G (2024) Improved rrt* path-planning algorithm based on the clothoid curve for a mobile robot under kinematic constraints. Sensors 24(23). https:\/\/doi.org\/10.3390\/s24237812","DOI":"10.3390\/s24237812"},{"key":"216_CR121","doi-asserted-by":"publisher","unstructured":"Ravankar AA, Ravankar A, Emaru T, Kobayashi Y (2020) HPPRM: hybrid potential based probabilistic roadmap algorithm for improved dynamic path planning of mobile robots. IEEE Access 8:221743\u2013221766. https:\/\/doi.org\/10.1109\/ACCESS.2020.3043333. Accessed 2025-01-08","DOI":"10.1109\/ACCESS.2020.3043333"},{"key":"216_CR122","doi-asserted-by":"publisher","unstructured":"Ray PP (2023) ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Int Things Cyber-Phys Syst 3:121\u2013154. https:\/\/doi.org\/10.1016\/j.iotcps.2023.04.003. Accessed 2025-05-03","DOI":"10.1016\/j.iotcps.2023.04.003"},{"key":"216_CR123","first-page":"196","volume-title":"Intersection of Artificial Intelligence, Data Science, and Cutting-Edge Technologies: From Concepts to Applications in Smart Environment","author":"A Regragui","year":"2025","unstructured":"Regragui A, Benhlima S, Bekri A (2025) Vis-to-nav: Visual autonomous navigation for mobile robots with a limited field of view. In: Farhaoui Y, Herawan T, Lucky Imoize A, Allaoui AE (eds) Intersection of Artificial Intelligence, Data Science, and Cutting-Edge Technologies: From Concepts to Applications in Smart Environment. Springer, Cham, pp 196\u2013201"},{"key":"216_CR124","unstructured":"Research on Automatic Overtaking Control Method Based on Improved Artificial Potential Field Method. https:\/\/chn.oversea.cnki.net\/kcms\/detail\/detail.aspx?filename=1021698938.nh&dbcode=CMFD &dbname=CMFD202201 &uniplatform=NZKPT Accessed 2025-05-03"},{"key":"216_CR125","unstructured":"Research on Multi-Robot Target Search Based on Intelligent Optimization Algorithms in Unknown Environments. https:\/\/chn.oversea.cnki.net\/kcms\/detail\/detail.aspx?filename=1020813785.nh&dbcode=CDFD &dbname=CDFDLAST2021 &uniplatform=NZKPT Accessed 2025-01-09"},{"key":"216_CR126","doi-asserted-by":"publisher","unstructured":"Ruan X, Ren D, Zhu X, Huang J, (2019) Mobile robot navigation based on deep reinforcement learning. In, (2019) Chinese control and decision conference (CCDC), pp 6174\u20136178. IEEE, Nanchang, China. https:\/\/doi.org\/10.1109\/CCDC.2019.8832393. https:\/\/ieeexplore.ieee.org\/document\/8832393\/ Accessed 2025-05-03","DOI":"10.1109\/CCDC.2019.8832393"},{"key":"216_CR127","doi-asserted-by":"publisher","unstructured":"Samadi Gharajeh M, Jond HB (2022) An intelligent approach for autonomous mobile robots path planning based on adaptive neuro-fuzzy inference system. Ain Shams Eng J 13(1):101491. https:\/\/doi.org\/10.1016\/j.asej.2021.05.005. Accessed 2025-01-08","DOI":"10.1016\/j.asej.2021.05.005"},{"key":"216_CR128","doi-asserted-by":"publisher","unstructured":"Sangeetha V, Krishankumar R, Ravichandran KS, Cavallaro F, Kar S, Pamucar D, Mardani A (2021) A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments. Symmetry 13(2):280. https:\/\/doi.org\/10.3390\/sym13020280. Accessed 2025-01-08","DOI":"10.3390\/sym13020280"},{"key":"216_CR129","doi-asserted-by":"crossref","unstructured":"Savva M, Kadian A, Maksymets O, Zhao Y, Wijmans E, Jain B, Straub J, Liu J, Koltun V, Malik J, Parikh D, Batra D (2019) Habitat: A Platform for Embodied AI Research. arXiv:1904.01201","DOI":"10.1109\/ICCV.2019.00943"},{"key":"216_CR130","unstructured":"Saxena P, Raghuvanshi N, Goveas N (2025) UAV-VLN: End-to-End Vision Language guided Navigation for UAVs. arXiv:2504.21432"},{"key":"216_CR131","unstructured":"Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal Policy Optimization Algorithms. arXiv:1707.06347"},{"key":"216_CR132","doi-asserted-by":"publisher","unstructured":"Sfeir J, Saad M, Saliah-Hassane H (2011) An improved artificial potential field approach to real-time mobile robot path planning in an unknown environment. In: 2011 IEEE International symposium on robotic and sensors environments (ROSE), pp 208\u2013213. https:\/\/doi.org\/10.1109\/ROSE.2011.6058518. https:\/\/ieeexplore.ieee.org\/document\/6058518 Accessed 2025-01-08","DOI":"10.1109\/ROSE.2011.6058518"},{"key":"216_CR133","doi-asserted-by":"publisher","unstructured":"Shahabi C, Sharifzadeh M (2018) Voronoi diagrams. In: Liu L, \u00d6zsu MT (eds.) Encyclopedia of Database Systems, pp 4580\u20134582. Springer, New York, NY. https:\/\/doi.org\/10.1007\/978-1-4614-8265-9_451. Accessed 2025-01-08","DOI":"10.1007\/978-1-4614-8265-9_451"},{"key":"216_CR134","doi-asserted-by":"publisher","unstructured":"Shin Y, Kim E (2021) Hybrid path planning using positioning risk and artificial potential fields. Aerospace Sci Technol 112:106640. https:\/\/doi.org\/10.1016\/j.ast.2021.106640. Accessed 2025-01-08","DOI":"10.1016\/j.ast.2021.106640"},{"key":"216_CR135","doi-asserted-by":"publisher","unstructured":"Shivgan R, Dong Z (2020) Energy-Efficient Drone Coverage Path Planning using Genetic Algorithm. In: 2020 IEEE 21st International conference on high performance switching and routing (HPSR), pp 1\u20136. https:\/\/doi.org\/10.1109\/HPSR48589.2020.9098989. ISSN: 2325-5609. https:\/\/ieeexplore.ieee.org\/document\/9098989 Accessed 2025-01-08","DOI":"10.1109\/HPSR48589.2020.9098989"},{"key":"216_CR136","doi-asserted-by":"crossref","unstructured":"Shridhar M, Thomason J, Gordon D, Bisk Y, Han W, Mottaghi R, Zettlemoyer L, Fox D (2020) ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks. arXiv:1912.01734","DOI":"10.1109\/CVPR42600.2020.01075"},{"key":"216_CR137","doi-asserted-by":"publisher","unstructured":"Singh R, Ren J, Lin X (2023) A review of deep reinforcement learning algorithms for mobile robot path planning. Vehicles 5(4):1423\u20131451. https:\/\/doi.org\/10.3390\/vehicles5040078. Accessed 2025-05-03","DOI":"10.3390\/vehicles5040078"},{"key":"216_CR138","unstructured":"Steven M. LaValle. https:\/\/lavalle.pl\/rrtpubs.html Accessed 2025-05-03"},{"key":"216_CR139","doi-asserted-by":"publisher","unstructured":"Sun L, Kanezaki A, Caron G, Yoshiyasu Y (2025) Enhancing multimodal-input object goal navigation by leveraging large language models for inferring room-object relationship knowledge. Adv Eng Inf 65:103135. https:\/\/doi.org\/10.1016\/j.aei.2025.103135. Accessed 2025-05-03","DOI":"10.1016\/j.aei.2025.103135"},{"key":"216_CR140","doi-asserted-by":"publisher","unstructured":"Sun Y, Qiu Y, Aoki Y (2025) Dynamicvln: Incorporating dynamics into vision-and-language navigation scenarios. Sensors 25(2). https:\/\/doi.org\/10.3390\/s25020364","DOI":"10.3390\/s25020364"},{"key":"216_CR141","doi-asserted-by":"publisher","unstructured":"Sun Y, Wang W, Xu M, Huang L, Shi K, Zou C, Chen B (2023) Local path planning for mobile robots based on fuzzy dynamic window algorithm. Sensors 23(19):8260. https:\/\/doi.org\/10.3390\/s23198260. Accessed 2025-01-08","DOI":"10.3390\/s23198260"},{"key":"216_CR142","doi-asserted-by":"publisher","unstructured":"Surmann H, Jestel C, Marchel R, Musberg F, Elhadj H, Ardani M (2020) Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments. https:\/\/doi.org\/10.48550\/arXiv.2005.13857. arXiv:2005.13857 Accessed 2025-05-03","DOI":"10.48550\/arXiv.2005.13857"},{"key":"216_CR143","unstructured":"Taheri H, Hosseini SR, Nekoui MA (2024) Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation. arXiv:2405.16266"},{"key":"216_CR144","doi-asserted-by":"publisher","unstructured":"Tai L, Li S, Liu M (2016) A deep-network solution towards model-less obstacle avoidance. In: 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 2759\u20132764. IEEE, Daejeon, South Korea. https:\/\/doi.org\/10.1109\/IROS.2016.7759428. http:\/\/ieeexplore.ieee.org\/document\/7759428\/ Accessed 2025-05-03","DOI":"10.1109\/IROS.2016.7759428"},{"key":"216_CR145","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108792","volume":"130","author":"H Tang","year":"2022","unstructured":"Tang H, Yuan C, Li Z, Tang J (2022) Learning attention-guided pyramidal features for few-shot fine-grained recognition. Pattern Recognition 130:108792. https:\/\/doi.org\/10.1016\/j.patcog.2022.108792","journal-title":"Pattern Recognition"},{"issue":"3","key":"216_CR146","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1109\/TPAMI.2024.3511621","volume":"47","author":"H Tang","year":"2025","unstructured":"Tang H, Li Z, Zhang D, He S, Tang J (2025) Divide-and-conquer: confluent triple-flow network for rgb-t salient object detection. IEEE Trans Pattern Anal Mach Intell 47(3):1958\u20131974. https:\/\/doi.org\/10.1109\/TPAMI.2024.3511621","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"216_CR147","doi-asserted-by":"publisher","unstructured":"Tang H, Li Z, Peng Z, Tang J (2020) Blockmix: Meta regularization and self-calibrated inference for metric-based meta-learning. In: Proceedings of the 28th ACM international conference on multimedia. MM \u201920, pp 610\u2013618. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3394171.3413884","DOI":"10.1145\/3394171.3413884"},{"key":"216_CR148","doi-asserted-by":"publisher","unstructured":"Tang H, Liu J, Yan S, Yan R, Li Z, Tang J (2023) M3net: Multi-view encoding, matching, and fusion for few-shot fine-grained action recognition. In: Proceedings of the 31st ACM international conference on multimedia. MM \u201923, pp 1719\u20131728. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3581783.3612221","DOI":"10.1145\/3581783.3612221"},{"key":"216_CR149","doi-asserted-by":"publisher","unstructured":"Tao B, Kim J-H (2024) Mobile robot path planning based on bi-population particle swarm optimization with random perturbation strategy. J King Saud University - Comput Inf Sc 36(2):101974. https:\/\/doi.org\/10.1016\/j.jksuci.2024.101974. Accessed 2025-01-09","DOI":"10.1016\/j.jksuci.2024.101974"},{"key":"216_CR150","doi-asserted-by":"publisher","unstructured":"Tao Y, Wen Y, Gao H, Wang T, Wan J, Lan J (2022) A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm. Electronics 11(8):1192. https:\/\/doi.org\/10.3390\/electronics11081192. Accessed 2025-01-08","DOI":"10.3390\/electronics11081192"},{"key":"216_CR151","doi-asserted-by":"publisher","unstructured":"Tariq MT, Hussain Y, Wang C (2025) Robust mobile robot path planning via LLM-based dynamic waypoint generation. Expert Syst Appl 282:127600. https:\/\/doi.org\/10.1016\/j.eswa.2025.127600. Accessed 2025-05-03","DOI":"10.1016\/j.eswa.2025.127600"},{"key":"216_CR152","unstructured":"Tsai Y-HH, Dhar V, Li J, Zhang B, Zhang J (2023) Multimodal large language model for visual navigation. arXiv:2310.08669"},{"key":"216_CR153","doi-asserted-by":"publisher","unstructured":"Tuan PM, Tai ND, Huy TQ, Thinh NT (2024) Flexible path planning of mobile robot for avoiding the dynamic obstacles using fuzzy controllers. Int J Mech Eng Robot Res 13(1):126\u2013132. https:\/\/doi.org\/10.18178\/ijmerr.13.1.126-132. Accessed 2025-01-08","DOI":"10.18178\/ijmerr.13.1.126-132"},{"key":"216_CR154","doi-asserted-by":"publisher","unstructured":"Waga A, Ba-ichou A, Benhlima S, Bekri A, Abdouni J (2024) Efficient autonomous navigation for mobile robots using machine learning. IAES Int J Artif Intell (IJ-AI) 13(3):3061\u20133071. https:\/\/doi.org\/10.11591\/ijai.v13.i3.pp3061-3071. Accessed 2025-05-03","DOI":"10.11591\/ijai.v13.i3.pp3061-3071"},{"key":"216_CR155","doi-asserted-by":"publisher","unstructured":"Waga A, Benhlima S, Bekri A, Abdouni J (2025) A novel approach for end-to-end navigation for real mobile robots using a deep hybrid model. Intell Serv Robot 18(1):75\u201395. https:\/\/doi.org\/10.1007\/s11370-024-00569-8. Accessed 2025-05-03","DOI":"10.1007\/s11370-024-00569-8"},{"key":"216_CR156","doi-asserted-by":"publisher","unstructured":"Waga A, Benhouria Y, Ba-Ichou A, Benhlima S, Bekri A, Abdouni J (2023) A New Method for Mobile Robots to Learn an Optimal Policy from an Expert Using Deep Imitation Learning. In: Motahhir S, Bossoufi B (eds.) Digital Technologies and Applications, pp 873\u2013882. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-031-29857-8_87","DOI":"10.1007\/978-3-031-29857-8_87"},{"key":"216_CR157","doi-asserted-by":"publisher","unstructured":"Waga A, Lamini C, Benhlima S, Bekri A (2021) Fuzzy logic obstacle avoidance by a nao robot in unknown environment. In: 2021 Fifth international conference on intelligent computing in data sciences (ICDS), pp 1\u20137. https:\/\/doi.org\/10.1109\/ICDS53782.2021.9626718","DOI":"10.1109\/ICDS53782.2021.9626718"},{"key":"216_CR158","doi-asserted-by":"publisher","unstructured":"Wang J (2023) Intelligent Path Planning of Mobile Robot Based on Genetic Algorithm. J Phys: Conference Series 2547(1):012001. https:\/\/doi.org\/10.1088\/1742-6596\/2547\/1\/012001. Accessed 2025-01-08","DOI":"10.1088\/1742-6596\/2547\/1\/012001"},{"key":"216_CR159","doi-asserted-by":"publisher","unstructured":"Wang C, Cheng C, Yang D, Pan G, Zhang F (2022) Path Planning in Localization Uncertaining Environment Based on Dijkstra Method. Front Neurorobot 16:821991. https:\/\/doi.org\/10.3389\/fnbot.2022.821991. Accessed 2025-01-07","DOI":"10.3389\/fnbot.2022.821991"},{"key":"216_CR160","doi-asserted-by":"publisher","unstructured":"Wang D, Chen S, Zhang Y, Liu L (2021) Path planning of mobile robot in dynamic environment: fuzzy artificial potential field and extensible neural network. Artif Life Robot 26(1):129\u2013139. https:\/\/doi.org\/10.1007\/s10015-020-00630-6. Accessed 2025-01-08","DOI":"10.1007\/s10015-020-00630-6"},{"key":"216_CR161","doi-asserted-by":"publisher","unstructured":"Wang H, Li G, Hou J, Chen L, Hu N (2022) A path planning method for underground intelligent vehicles based on an improved RRT* Algorithm. Electronics 11(3):294. https:\/\/doi.org\/10.3390\/electronics11030294. Accessed 2025-01-08","DOI":"10.3390\/electronics11030294"},{"key":"216_CR162","doi-asserted-by":"publisher","unstructured":"Wang J, Zheng E (2024) Path planning of a mobile robot based on the improved rapidly exploring random trees star algorithm. Electronics 13(12):2340. https:\/\/doi.org\/10.3390\/electronics13122340. Accessed 2025-01-08","DOI":"10.3390\/electronics13122340"},{"key":"216_CR163","doi-asserted-by":"publisher","unstructured":"Wondosen A, Shiferaw D (2024) Fuzzy Logic Controller Design for Mobile Robot Outdoor Navigation. https:\/\/doi.org\/10.48550\/arXiv.2401.01756. arXiv:2401.01756 Accessed 2025-01-09","DOI":"10.48550\/arXiv.2401.01756"},{"key":"216_CR164","doi-asserted-by":"publisher","unstructured":"Wu B, Chi X, Zhao C, Zhang W, Lu Y, Jiang D (2022) Dynamic path planning for forklift AGV based on smoothing a* and improved DWA hybrid algorithm. Sensors 22(18):7079. https:\/\/doi.org\/10.3390\/s22187079. Accessed 2025-01-08","DOI":"10.3390\/s22187079"},{"key":"216_CR165","doi-asserted-by":"publisher","unstructured":"Wu K, Wang H, Esfahani MA, Yuan S (2022) Achieving real-time path planning in unknown environments through deep neural networks. IEEE Trans Intell Transport Syst 23(3):2093\u20132102. https:\/\/doi.org\/10.1109\/TITS.2020.3031962. Accessed 2025-05-03","DOI":"10.1109\/TITS.2020.3031962"},{"key":"216_CR166","doi-asserted-by":"publisher","unstructured":"Xin W, Wanlin L, Chao F, Likai H (2019) Path planning research based on an improved a* algorithmfor mobile robot. IOP Conference Series: Mater Sci Eng 569(5):052044. https:\/\/doi.org\/10.1088\/1757-899X\/569\/5\/052044. Accessed 2025-01-07","DOI":"10.1088\/1757-899X\/569\/5\/052044"},{"key":"216_CR167","doi-asserted-by":"publisher","unstructured":"Yang Z, Li N, Zhang Y, Li J (2023) Mobile robot path planning based on improved particle swarm optimization and improved dynamic window approach. J Robot 2023:1\u201316. https:\/\/doi.org\/10.1155\/2023\/6619841. Accessed 2025-01-09","DOI":"10.1155\/2023\/6619841"},{"key":"216_CR168","doi-asserted-by":"publisher","unstructured":"Yang H, Teng X (2022) Mobile robot path planning based on enhanced dynamic window approach and improved a* algorithm. J Robot 2022:1\u20139. https:\/\/doi.org\/10.1155\/2022\/2183229. Accessed 2025-01-08","DOI":"10.1155\/2022\/2183229"},{"key":"216_CR169","doi-asserted-by":"publisher","unstructured":"Ye L, Chen J, Zhou Y (2022) Real-time path planning for robot using OP-PRM in complex dynamic environment. Front Neurorobotics 16:910859. https:\/\/doi.org\/10.3389\/fnbot.2022.910859. Accessed 2025-01-08","DOI":"10.3389\/fnbot.2022.910859"},{"key":"216_CR170","doi-asserted-by":"publisher","unstructured":"Ye J, Papaioannou S, Kolios P (2025) Vlm-rrt: Vision language model guided rrt search for autonomous uav navigation. In: 2025 International conference on unmanned aircraft systems (ICUAS), pp 633\u2013640. IEEE, ???. https:\/\/doi.org\/10.1109\/icuas65942.2025.11007837","DOI":"10.1109\/icuas65942.2025.11007837"},{"key":"216_CR171","doi-asserted-by":"publisher","unstructured":"Yin Y, Chen Z, Liu G, Guo J (2023) A mapless local path planning approach using deep reinforcement learning framework. Sensors 23(4):2036. https:\/\/doi.org\/10.3390\/s23042036. Accessed 2025-05-03","DOI":"10.3390\/s23042036"},{"key":"216_CR172","doi-asserted-by":"publisher","unstructured":"Yonetani R, Taniai T, Barekatain M, Nishimura M, Kanezaki A (2021) Path planning using neural a* search. https:\/\/doi.org\/10.48550\/arXiv.2009.07476. arXiv:2009.07476 Accessed 2025-01-07","DOI":"10.48550\/arXiv.2009.07476"},{"key":"216_CR173","doi-asserted-by":"publisher","unstructured":"Yuan J, Wang H, Lin C, Liu D, Yu D (2019) A novel GRU-RNN network model for dynamic path planning of mobile robot. IEEE Access 7:15140\u201315151. https:\/\/doi.org\/10.1109\/ACCESS.2019.2894626. Accessed 2025-05-03","DOI":"10.1109\/ACCESS.2019.2894626"},{"issue":"6","key":"216_CR174","doi-asserted-by":"publisher","first-page":"4918","DOI":"10.1109\/lra.2024.3387171","volume":"9","author":"L Yue","year":"2024","unstructured":"Yue L, Zhou D, Xie L, Zhang F, Yan Y, Yin E (2024) Safe-vln: collision avoidance for vision-and-language navigation of autonomous robots operating in continuous environments. IEEE Robot Automation Lett 9(6):4918\u20134925. https:\/\/doi.org\/10.1109\/lra.2024.3387171","journal-title":"IEEE Robot Automation Lett"},{"key":"216_CR175","doi-asserted-by":"publisher","unstructured":"Yu L, Kong D, Shao X, Yan X (2018) A Path Planning and Navigation Control System Design for Driverless Electric Bus. IEEE Access 6:53960\u201353975. https:\/\/doi.org\/10.1109\/ACCESS.2018.2868339. Accessed 2025-01-07","DOI":"10.1109\/ACCESS.2018.2868339"},{"key":"216_CR176","doi-asserted-by":"publisher","unstructured":"Yun SC, Parasuraman S, Ganapathy V (2013) Mobile Robot Navigation: Neural Q-Learning. In: Meghanathan N, Nagamalai D, Chaki N (eds.) Advances in Computing and Information Technology, pp 259\u2013268. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-31600-5_26","DOI":"10.1007\/978-3-642-31600-5_26"},{"key":"216_CR177","doi-asserted-by":"publisher","unstructured":"Zadeh LA (1965) Fuzzy sets. Inf. Control 8(3):338\u2013353. https:\/\/doi.org\/10.1016\/S0019-9958(65)90241-X. Accessed 2025-05-03","DOI":"10.1016\/S0019-9958(65)90241-X"},{"key":"216_CR178","doi-asserted-by":"publisher","unstructured":"Zagradjanin N, Rodic A, Pamucar D, Pavkovic B (2021) Cloud-based multi-robot path planning in complex and crowded environment using fuzzy logic and online learning. Inf Technol Control 50(2):357\u2013374. https:\/\/doi.org\/10.5755\/j01.itc.50.2.28234. Accessed 2025-01-08","DOI":"10.5755\/j01.itc.50.2.28234"},{"key":"216_CR179","doi-asserted-by":"publisher","unstructured":"Zhang H-y, Lin W-m, Chen A-x (2018) Path Planning for the Mobile Robot: A Review. Symmetry 10(10):450. https:\/\/doi.org\/10.3390\/sym10100450. Accessed 2025-05-03","DOI":"10.3390\/sym10100450"},{"key":"216_CR180","doi-asserted-by":"publisher","unstructured":"Zhang K, Niroui F, Ficocelli M, Nejat G (2018) Robot navigation of environments with unknown rough terrain using deep reinforcement learning. In: 2018 IEEE International symposium on safety, security, and rescue robotics (SSRR), pp 1\u20137. https:\/\/doi.org\/10.1109\/SSRR.2018.8468643. ISSN: 2475-8426. https:\/\/ieeexplore.ieee.org\/document\/8468643 Accessed 2025-05-03","DOI":"10.1109\/SSRR.2018.8468643"},{"key":"216_CR181","doi-asserted-by":"publisher","unstructured":"Zhang S, Pu J, Si Y, Sun L (2021) Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization. Int J Adv Robot Syst 18(3):17298814211019222. https:\/\/doi.org\/10.1177\/17298814211019222. Accessed 2025-01-09","DOI":"10.1177\/17298814211019222"},{"key":"216_CR182","doi-asserted-by":"publisher","unstructured":"Zhang Z, Yang H, Bai X, Zhang S, Xu C (2025) The path planning of mobile robots based on an improved genetic algorithm. Appl Sc 15(7). https:\/\/doi.org\/10.3390\/app15073700","DOI":"10.3390\/app15073700"},{"key":"216_CR183","doi-asserted-by":"publisher","unstructured":"Zheng L, Yu W, Li G, Qin G, Luo Y (2023) Particle swarm algorithm path-planning method for mobile robots based on artificial potential fields. Sensors 23(13):6082. https:\/\/doi.org\/10.3390\/s23136082. Accessed 2025-01-09","DOI":"10.3390\/s23136082"},{"key":"216_CR184","doi-asserted-by":"publisher","unstructured":"Zhenyang X, Wei Y (2022) Mobile robot path planning based on fusion of improved A* algorithm and adaptive DWA algorithm. J Phys: Conference Series 2330(1):012003. https:\/\/doi.org\/10.1088\/1742-6596\/2330\/1\/012003. Accessed 2025-01-08","DOI":"10.1088\/1742-6596\/2330\/1\/012003"},{"key":"216_CR185","doi-asserted-by":"publisher","unstructured":"Zheyi C, Bing X (2021) AGV Path Planning Based on Improved Artificial Potential Field Method. In: 2021 IEEE International conference on power electronics, computer applications (ICPECA), pp 32\u201337. https:\/\/doi.org\/10.1109\/ICPECA51329.2021.9362519. https:\/\/ieeexplore.ieee.org\/document\/9362519 Accessed 2025-01-08","DOI":"10.1109\/ICPECA51329.2021.9362519"},{"key":"216_CR186","doi-asserted-by":"publisher","unstructured":"Zhu Y, Mottaghi R, Kolve E, Lim JJ, Gupta A, Fei-Fei L, Farhadi A (2017) Target-driven visual navigation in indoor scenes using deep reinforcement learning. In: 2017 IEEE International conference on robotics and automation (ICRA), pp 3357\u20133364. https:\/\/doi.org\/10.1109\/ICRA.2017.7989381. https:\/\/ieeexplore.ieee.org\/document\/7989381 Accessed 2025-05-03","DOI":"10.1109\/ICRA.2017.7989381"},{"key":"216_CR187","doi-asserted-by":"publisher","unstructured":"Ziegler J, Werling M, Schroder J (2008) Navigating car-like robots in unstructured environments using an obstacle sensitive cost function. In: 2008 IEEE Intelligent Vehicles Symposium, pp 787\u2013791. https:\/\/doi.org\/10.1109\/IVS.2008.4621302. ISSN: 1931-0587. https:\/\/ieeexplore.ieee.org\/document\/4621302\/ Accessed 2025-01-08","DOI":"10.1109\/IVS.2008.4621302"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00216-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00216-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00216-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T12:45:12Z","timestamp":1758113112000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00216-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,21]]},"references-count":187,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["216"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00216-x","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,21]]},"assertion":[{"value":"20 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"198"}}