{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T08:51:29Z","timestamp":1780563089832,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Interactive technologies such as augmented reality have grown in popularity, but specialized sensors and high computer power must be used to perceive and analyze the environment in order to obtain an immersive experience in real time. However, these kinds of implementations have high costs. On the other hand, machine learning has helped create alternative solutions for reducing costs, but it is limited to particular solutions because the creation of datasets is complicated. Due to this problem, this work suggests an alternate strategy for dealing with limited information: unpaired samples from known and unknown surroundings are used to generate a path on embedded devices, such as smartphones, in real time. This strategy creates a path that avoids virtual elements through physical objects. The authors suggest an architecture for creating a path using imperfect knowledge. Additionally, an augmented reality experience is used to describe the generated path, and some users tested the proposal to evaluate the performance. Finally, the primary contribution is the approximation of a path produced from a known environment by using an unpaired dataset.<\/jats:p>","DOI":"10.3390\/s22239411","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T03:55:37Z","timestamp":1669953337000},"page":"9411","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Path Generator with Unpaired Samples Employing Generative Adversarial Networks"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5187-0298","authenticated-orcid":false,"given":"Javier","family":"Maldonado-Romo","sequence":"first","affiliation":[{"name":"Institute of Advanced Materials and Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City 14380, Mexico"},{"name":"Centro de Innovaci\u00f3n y Desarrollo Tecnol\u00f3gico en C\u00f3mputo, Instituto Polit\u00e9cnico Nacional, Unidad Profesional Adolfo L\u00f3pez Mateos, Juan de Dios B\u00e1tiz s\/n esq. Miguel Oth\u00f3n de Mendiz\u00e1bal, Mexico City 07700, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6342-3508","authenticated-orcid":false,"given":"Alberto","family":"Maldonado-Romo","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Unidad Profesional Adolfo L\u00f3pez Mateos, Juan de Dios B\u00e1tiz s\/n esq. Miguel Oth\u00f3n de Mendiz\u00e1bal, Mexico City 07700, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1504-4714","authenticated-orcid":false,"given":"Mario","family":"Aldape-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Centro de Innovaci\u00f3n y Desarrollo Tecnol\u00f3gico en C\u00f3mputo, Instituto Polit\u00e9cnico Nacional, Unidad Profesional Adolfo L\u00f3pez Mateos, Juan de Dios B\u00e1tiz s\/n esq. Miguel Oth\u00f3n de Mendiz\u00e1bal, Mexico City 07700, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2947","DOI":"10.1109\/TVCG.2018.2868591","article-title":"Revisiting Trends in Augmented Reality Research: A Review of the 2nd Decade of ISMAR (2008\u20132017)","volume":"24","author":"Kim","year":"2018","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1109\/LRA.2020.2967280","article-title":"Increasing Robot Autonomy via Motion Planning and an Augmented Reality Interface","volume":"5","author":"Sobti","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kularbphettong, K., Vichivanives, R., and Roonrakwit, P. (2019, January 28\u201331). Student Learning Achievement through Augmented Reality in Science Subjects. Proceedings of the 2019 11th International Conference on Education Technology and Computers, Amsterdam, Netherlands. ICETC 2019.","DOI":"10.1145\/3369255.3369282"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1109\/THMS.2020.2984746","article-title":"A Comparative Evaluation of a Virtual Reality Table and a HoloLens-Based Augmented Reality System for Anatomy Training","volume":"50","year":"2020","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TMRB.2019.2957061","article-title":"A Review of Augmented Reality in Robotic-Assisted Surgery","volume":"2","author":"Qian","year":"2020","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"ref_6","unstructured":"Zhou, F., Duh, H.B.L., and Billinghurst, M. (2008, January 15\u201318). Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR. Proceedings of the 2008 7th IEEE\/ACM International Symposium on Mixed and Augmented Reality, Cambridge, UK."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101046","DOI":"10.1016\/j.destud.2021.101046","article-title":"Mixed reality in design prototyping: A systematic review","volume":"77","author":"Kent","year":"2021","journal-title":"Des. Stud."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Speicher, M., Hall, B.D., and Nebeling, M. (2019, January 4\u20139). What is Mixed Reality?. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI \u201919), Glasgow, UK.","DOI":"10.1145\/3290605.3300767"},{"key":"ref_9","first-page":"1","article-title":"Towards Augmented Reality Driven Human-City Interaction: Current Research on Mobile Headsets and Future Challenges","volume":"54","author":"Lee","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lalanne, D., and Kohlas, J. (2009). Mixed Reality: A Survey. Human Machine Interaction: Research Results of the MMI Program, Springer.","DOI":"10.1007\/978-3-642-00437-7"},{"key":"ref_11","unstructured":"Evans, D. (2022, June 02). How the Next Evolution of the Internet Is Changing Everything\u2014Networking, Cloud, and Cybersecurity Solutions, CISCO. Available online: https:\/\/www.cisco.com\/c\/dam\/en_us\/about\/ac79\/docs\/innov\/IoT_IBSG_0411FINAL.pdf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8201","DOI":"10.1109\/ACCESS.2018.2802699","article-title":"A Practical Evaluation of Commercial Industrial Augmented Reality Systems in an Industry 4.0 Shipyard","volume":"6","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., and Kacprzyk, J. (2019). Capabilities of ARCore and ARKit Platforms for AR\/VR Applications. Engineering in Dependability of Computer Systems and Networks. DepCoS-RELCOMEX 2019. Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-3-030-19501-4"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100411","DOI":"10.1016\/j.entcom.2021.100411","article-title":"Systematic literature review on health effects of playing Pok\u00e9mon Go","volume":"38","author":"Wang","year":"2021","journal-title":"Entertain. Comput."},{"key":"ref_15","first-page":"27","article-title":"Augmented reality game related injuries","volume":"2","author":"Richards","year":"2017","journal-title":"New Horizons Clin. Case Rep."},{"key":"ref_16","first-page":"1","article-title":"A novel collision-free navigation approach for multiple nonholonomic robots based on Orca and linear MPC","volume":"2020","author":"Mao","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ajani, T.S., Imoize, A.L., and Atayero, A.A. (2021). An Overview of Machine Learning within Embedded and Mobile Devices\u2013Optimizations and Applications. Sensors, 21.","DOI":"10.3390\/s21134412"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2980179.2980254","article-title":"Burst Photography for High Dynamic Range and Low-Light Imaging on Mobile Cameras","volume":"35","author":"Hasinoff","year":"2016","journal-title":"ACM Trans. Graph."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_20","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., and Weinberger, K.Q. (2014). Generative Adversarial Nets. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tripathy, S., Kannala, J., and Rahtu, E. (2019). Learning Image-to-Image Translation Using Paired and Unpaired Training Samples, Springer.","DOI":"10.1007\/978-3-030-20890-5_4"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A. (2017). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks, IEEE International Conference on Computer Vision (ICCV); Venice, Italy, 22\u201329 October 2017; IEEE.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Leven, D., and Sharir, M. (1985, January 5\u20137). An Efficient and Simple Motion Planning Algorithm for a Ladder Moving in Two-Dimensional Space amidst Polygonal Barriers (Extended Abstract). Proceedings of the First Annual Symposium on Computational Geometry (SCG \u201985), Baltimore, Maryland, USA.","DOI":"10.1145\/323233.323262"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/0196-8858(83)90014-3","article-title":"On the \u201cpiano movers\u201d problem. II. General techniques for computing topological properties of real algebraic manifolds","volume":"4","author":"Schwartz","year":"1983","journal-title":"Adv. Appl. Math."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7585","DOI":"10.1109\/TITS.2020.3004984","article-title":"Learning a Deep Cascaded Neural Network for Multiple Motion Commands Prediction in Autonomous Driving","volume":"22","author":"Hu","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shah, S., Dey, D., Lovett, C., and Kapoor, A. (2017, January 3). AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles. Proceedings of the FSR, First Online.","DOI":"10.1007\/978-3-319-67361-5_40"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1109\/TPAMI.2019.2909895","article-title":"Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery","volume":"42","author":"Shamwell","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","article-title":"ORB-SLAM: A Versatile and Accurate Monocular SLAM System","volume":"31","author":"Montiel","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luo, S., and Guo, T. (2021, January 21\u201324). Image-to-Image Transfer Makes Chaos to Order. Proceedings of the 2021 International Conference on Multimedia Retrieval (ICMR \u201921), Taipei, Taiwan.","DOI":"10.1145\/3460426.3463611"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3197517.3201329","article-title":"Synthetic depth-of-field with a single-camera mobile phone","volume":"37","author":"Wadhwa","year":"2018","journal-title":"ACM Trans. Graph."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5595","DOI":"10.1109\/ACCESS.2021.3137797","article-title":"Analysis of Depth and Semantic 373 Mask for Perceiving a Physical Environment Using Virtual Samples Generated by a GAN","volume":"10","year":"2022","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wu, T., Luo, A., Huang, R., Cheng, H., and Zhao, Y. (2019, January 3\u20138). End-to-End Driving Model for Steering Control of Autonomous Vehicles with Future Spatiotemporal Features. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968453"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1109\/TIP.2018.2869695","article-title":"Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer","volume":"28","author":"Chen","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8706","DOI":"10.1109\/TIP.2020.3018856","article-title":"RPD-GAN: Learning to Draw Realistic Paintings With Generative Adversarial Network","volume":"29","author":"Gao","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/TIP.2008.920761","article-title":"Segmentation by Fusion of Histogram-Based K-Means Clusters in Different Color Spaces","volume":"17","author":"Mignotte","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2600","DOI":"10.1109\/TSP.2018.2813322","article-title":"Hierarchical Clustering Given Confidence Intervals of Metric Distances","volume":"66","author":"Huang","year":"2018","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, W., Gao, H., Yi, Q., Zheng, K., and Gu, T. (2020, January 12\u201314). An Improved RRT* Path Planning Algorithm for Service Robot. Proceedings of the 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China.","DOI":"10.1109\/ITNEC48623.2020.9085226"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1002\/nav.20165","article-title":"Aircraft routing under the risk of detection","volume":"53","author":"Zabarankin","year":"2006","journal-title":"Nav. Res. Logist. (NRL)"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Xue, Y., and Sun, J.Q. (2018). Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm. Appl. Sci., 8.","DOI":"10.3390\/app8091425"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Maldonado-Romo, J., Aldape-P\u00e9rez, M., and Rodr\u00edguez-Molina, A. (2021). Path planning generator with metadata through a domain change by Gan between physical and Virtual Environments. Sensors, 21.","DOI":"10.3390\/s21227667"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Poghosyan, A., and Sarukhanyan, H. (2017, January 5\u20138). Short-term memory with read-only unit in neural image caption generator. Proceedings of the 2017 Computer Science and Information Technologies (CSIT), Lviv, Ukraine.","DOI":"10.1109\/CSITechnol.2017.8312163"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., and Schmid, C. (2012, January 7\u201313). Real-Time Camera Tracking: When is High Frame-Rate Best?. Proceedings of the Computer Vision\u2014ECCV 2012, Florence, Italy.","DOI":"10.1007\/978-3-642-33709-3"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zainuddin, N., Mustafah, Y., Shawgi, Y., and Rashid, N. (2014, January 23\u201325). Autonomous Navigation of Mobile Robot Using Kinect Sensor. Proceedings of the 2014 International Conference on Computer and Communication Engineering, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICCCE.2014.21"},{"key":"ref_46","unstructured":"Bowman, D.A., Kruijff, E., LaViola, J.J., and Poupyrev, I. (2004). 3D User Interfaces: Theory and Practice, Addison Wesley Longman Publishing Co., Inc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9411\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:32:41Z","timestamp":1760146361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,2]]},"references-count":46,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239411"],"URL":"https:\/\/doi.org\/10.3390\/s22239411","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,2]]}}}