{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T15:51:23Z","timestamp":1785858683872,"version":"3.56.0"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T00:00:00Z","timestamp":1697414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>In today's dynamic logistics landscape, the role of intelligent robots is paramount for enhancing efficiency, reducing costs, and ensuring safety. Traditional path planning methods often struggle to adapt to changing environments, resulting in issues like collisions and conflicts. This research addresses the challenge of path planning and control for logistics robots operating in complex environments. The proposed method aims to integrate information from various perception sources to enhance path planning and obstacle avoidance, thereby increasing the autonomy and reliability of logistics robots.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The method presented in this paper begins by employing a 3D Convolutional Neural Network (CNN) to learn feature representations of objects within the environment, enabling object recognition. Subsequently, Long Short-Term Memory (LSTM) models are utilized to capture spatio-temporal features and predict the behavior and trajectories of dynamic obstacles. This predictive capability empowers robots to more accurately anticipate the future positions of obstacles in intricate settings, thereby mitigating potential collision risks. Finally, the Dijkstra algorithm is employed for path planning and control decisions to ensure the selection of optimal paths across diverse scenarios.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In a series of rigorous experiments, the proposed method outperforms traditional approaches in terms of both path planning accuracy and obstacle avoidance performance. These substantial improvements underscore the efficacy of the intelligent path planning and control scheme.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>This research contributes to enhancing the practicality of logistics robots in complex environments, thereby fostering increased efficiency and safety within the logistics industry. By combining object recognition, spatio-temporal modeling, and optimized path planning, the proposed method enables logistics robots to navigate intricate scenarios with higher precision and reliability, ultimately advancing the capabilities of autonomous logistics operations.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnbot.2023.1285673","type":"journal-article","created":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T05:05:45Z","timestamp":1697432745000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Multimodal intelligent logistics robot combining 3D CNN, LSTM, and visual SLAM for path planning and control"],"prefix":"10.3389","volume":"17","author":[{"given":"Zhuqin","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,10,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"34","DOI":"10.38094\/sgej1027","article-title":"The simultaneous localization and mapping (SLAM)-an overview","volume":"2","author":"Alsadik","year":"2021","journal-title":"Surv. Geospat. Eng. J."},{"key":"B2","doi-asserted-by":"publisher","first-page":"5939","DOI":"10.32604\/cmc.2022.028165","article-title":"Improved Dijkstra algorithm for mobile robot path planning and obstacle avoidance","volume":"72","author":"Alshammrei","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"B3","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.jmsy.2022.09.014","article-title":"Survey on robotic systems for internal logistics","volume":"65","author":"Bernardo","year":"2022","journal-title":"J. Manufact. Syst."},{"key":"B4","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/j.dcan.2020.12.001","article-title":"Robot and its living space: a roadmap for robot development based on the view of living space","volume":"7","author":"Cai","year":"2021","journal-title":"Digit. Commun. Netw."},{"key":"B5","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s11370-021-00387-2","article-title":"Reinforcement learning-based dynamic obstacle avoidance and integration of path planning","volume":"14","author":"Choi","year":"2021","journal-title":"Intell. Serv. Robot."},{"key":"B6","doi-asserted-by":"publisher","first-page":"176","DOI":"10.3991\/ijoe.v14i11.9527","article-title":"Path planning for batch picking of warehousing and logistics robots based on modified A* algorithm","volume":"14","author":"Duan","year":"2018","journal-title":"Int. J. Online Eng."},{"key":"B7","doi-asserted-by":"publisher","first-page":"106476","DOI":"10.1016\/j.engappai.2023.106476","article-title":"UAV sensor data applications with deep neural networks: a comprehensive survey","volume":"123","author":"Dudukcu","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"B8","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s42979-020-00207-x","article-title":"CNN-biLSTM model for violence detection in smart surveillance","volume":"1","author":"Halder","year":"2020","journal-title":"SN Comput. Sci."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.1109\/TIP.2018.2878958","article-title":"An augmented linear mixing model to address spectral variability for hyperspectral unmixing","volume":"28","author":"Hong","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2301.06201","article-title":"Risk-aware vehicle motion planning using bayesian LSTM-based model predictive control","author":"Huang","year":"2023","journal-title":"arXiv preprint arXiv:2301.06201"},{"key":"B11","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/OJITS.2020.2965969","article-title":"Surround vehicle motion prediction using LSTM-RNN for motion planning of autonomous vehicles at multi-lane turn intersections","volume":"1","author":"Jeong","year":"2020","journal-title":"IEEE Open J. Intell. Transport. Syst."},{"key":"B12","doi-asserted-by":"publisher","first-page":"94118","DOI":"10.1109\/ACCESS.2019.2926350","article-title":"MagicVO: an end-to-end hybrid CNN and bi-LSTM method for monocular visual odometry","volume":"7","author":"Jiao","year":"2019","journal-title":"IEEE Access"},{"key":"B13","doi-asserted-by":"publisher","first-page":"118176","DOI":"10.1109\/ACCESS.2022.3218699","article-title":"Neural path planning with multi-scale feature fusion networks","volume":"10","author":"Jin","year":"2022","journal-title":"IEEE Access"},{"key":"B14","unstructured":"\u201cOptimal path planning of automated guided vehicle using dijkstra algorithm under dynamic conditions,\u201d231236 KimS. JinH. SeoM. HarD. 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA)2019"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1038\/s41598-022-05079-0","article-title":"A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity","volume":"12","author":"Kumar","year":"2022","journal-title":"Sci. Rep."},{"key":"B16","unstructured":"\u201cFrom scratch to sketch: deep decoupled hierarchical reinforcement learning for robotic sketching agent,\u201d55535559 LeeG. KimM. LeeM. ZhangB.-T. 2022 International Conference on Robotics and Automation (ICRA)2022"},{"key":"B17","unstructured":"\u201cDHRL: a graph-based approach for long-horizon and sparse hierarchical reinforcement learning,\u201d1366813678 LeeS. KimJ. JangI. KimH. J. KoyejoS. MohamedS. AgarwalA. BelgraveD. ChoK. OhA. Curran Associates, IncAdvances in Neural Information Processing Systems 352022"},{"key":"B18","doi-asserted-by":"publisher","first-page":"5513412","DOI":"10.1109\/TGRS.2023.3309960","article-title":"LRR-Net: an interpretable deep unfolding network for hyperspectral anomaly detection","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"B19","doi-asserted-by":"publisher","first-page":"2956","DOI":"10.1109\/TII.2019.2903118","article-title":"Simultaneous localization and mapping-based in vivo navigation control of microparticles","volume":"16","author":"Li","year":"2019","journal-title":"IEEE Trans. Indus. Inform."},{"key":"B20","unstructured":"\u201cA novel recurrent neural network for improving redundant manipulator motion planning completeness,\u201d29562961 LiY. LiS. HannafordB. 343363682018 IEEE International Conference on Robotics and Automation (ICRA)2018"},{"key":"B21","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/MRA.2020.2976322","article-title":"Multifingered grasp planning via inference in deep neural networks: outperforming sampling by learning differentiable models","volume":"27","author":"Lu","year":"2020","journal-title":"IEEE Robot. Automat. Mag."},{"key":"B22","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1109\/TCDS.2021.3063273","article-title":"Conditional generative adversarial networks for optimal path planning","volume":"14","author":"Ma","year":"2021","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"B23","doi-asserted-by":"publisher","first-page":"60253","DOI":"10.1109\/ACCESS.2022.3181131","article-title":"Field evaluation of path-planning algorithms for autonomous mobile robot in smart farms","volume":"10","author":"Pak","year":"2022","journal-title":"IEEE Access"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3510032","article-title":"GRNN: generative regression neural network\u2013a data leakage attack for federated learning","volume":"13","author":"Ren","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"B25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ICAECA52838.2021.9675525","article-title":"\u201cPath planning for mobile robots using deep learning architectures,\u201d","volume-title":"2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA)","author":"Siddarth","year":"2021"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/9781271","article-title":"Artificial intelligence of things (AIoT) technologies and applications","volume":"2021","author":"Sung","year":"2021","journal-title":"Wireless Commun. Mobile Comput."},{"key":"B27","doi-asserted-by":"crossref","unstructured":"\u201cAbnormal behavior recognition using CNN-LSTM with attention mechanism,\u201d15 TayN. C. TeeC. OngT. S. TehP. S. 2019 1st International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)2019","DOI":"10.1109\/ICECIE47765.2019.8974824"},{"key":"B28","doi-asserted-by":"publisher","first-page":"182784","DOI":"10.1109\/ACCESS.2019.2959346","article-title":"A novel path planning method for articulated road roller using support vector machine and longest accessible path with course correction","volume":"7","author":"Tong","year":"2019","journal-title":"IEEE Access"},{"key":"B29","doi-asserted-by":"publisher","first-page":"4706","DOI":"10.3390\/app11104706","article-title":"Machine learning approach to real-time 3D path planning for autonomous navigation of unmanned aerial vehicle","volume":"11","author":"Tullu","year":"2021","journal-title":"Appl. Sci."},{"key":"B30","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1109\/THMS.2021.3131683","article-title":"A survey of hybrid human-artificial intelligence for social computing","volume":"52","author":"Wang","year":"2022","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"B31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2020.3040277","article-title":"Convolutional neural networks for multimodal remote sensing data classification","volume":"60","author":"Wu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"B32","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1109\/TIP.2022.3228497","article-title":"UIU-Net: U-net in U-net for infrared small object detection","volume":"32","author":"Wu","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8859689","article-title":"UB-LSTM: a trajectory prediction method combined with vehicle behavior recognition","author":"Xiao","year":"2020","journal-title":"J. Adv. Transport."},{"key":"B34","doi-asserted-by":"publisher","first-page":"3034","DOI":"10.3934\/mbe.2021152","article-title":"Path planning of mobile robot based on improved ANT colony algorithm for logistics","volume":"18","author":"Xue","year":"2021","journal-title":"Math. Biosci. Eng."},{"key":"B35","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/s13198-021-01243-3","article-title":"Positioning of logistics and warehousing automated guided vehicle based on improved LSTM network","volume":"14","author":"Yan","year":"2023","journal-title":"Int. J. Syst. Assurance Eng. Manage."},{"key":"B36","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1049\/trit.2020.0024","article-title":"Multi-robot path planning based on a deep reinforcement learning DQN algorithm","volume":"5","author":"Yang","year":"2020","journal-title":"CAAI Trans. Intell. Technol."},{"key":"B37","doi-asserted-by":"publisher","first-page":"5514415","DOI":"10.1109\/TGRS.2023.3284671","article-title":"Extended vision transformer (EXVIT) for land use and land cover classification: a multimodal deep learning framework","volume":"61","author":"Yao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1903.09374","article-title":"Deep hierarchical reinforcement learning based recommendations via multi-goals abstraction","author":"Zhao","year":"2019","journal-title":"arXiv preprint arXiv:1903.09374"},{"key":"B39","doi-asserted-by":"publisher","first-page":"100716","DOI":"10.1016\/j.suscom.2022.100716","article-title":"Airport AGV path optimization model based on ANT colony algorithm to optimize Dijkstra algorithm in urban systems","volume":"35","author":"Zhou","year":"2022","journal-title":"Sustain. Comput. Inform. Syst."}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2023.1285673\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T21:50:15Z","timestamp":1730325015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2023.1285673\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,16]]},"references-count":39,"alternative-id":["10.3389\/fnbot.2023.1285673"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2023.1285673","relation":{},"ISSN":["1662-5218"],"issn-type":[{"value":"1662-5218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,16]]},"article-number":"1285673"}}