Autors: Popov, V. L., Ahmed, S. A., Topalov, A. V., Shakev, N. G.
Title: Development of Mobile Robot Target Recognition and Following Behaviour Using Deep Convolutional Neural Network and 2D Range Data
Keywords: mobile robots deep learning deep convolutional neural networ

Abstract: In this work, a novel approach allowing a mobile robot to recognize and follow dynamic targets is proposed. The designed behavior can simplify subsequent development of autonomous navigation and obstacle avoidance tasks as well as the capability of mobile platforms to operate within multi-agent formations. In the proposed approach, the target recognition problem is considered as an image classification task and it tackled by using a state-of-the-art deep convolutional neural network working with a sequence of images obtained from the environmental scene. The subsequent target following problem is implemented by using 2D range data. The proposed approach has been implemented on a KUKA youBot omnidirectional mobile platform using robot operating system middleware and equipped with an onboard USB camera and a 2D LiDAR. The conducted experiments have shown that the mobile robot is capable to detect and follow accurately another mobile platform (iRobot Create) in a shared environment. The

References

    Issue

    IFAC-PapersOnLine, vol. 51, issue 30, pp. 210-215, 2018, Austria, Elsevier, DOI 10.1016/j.ifacol.2018.11.288

    Цитирания (Citation/s):
    1. Li X., Wu T., Target Recognition Method of Rehabilitation Robot Based on Image Local Features, (2020) IEEE Access, 8, art. no. 9184007, pp. 160607 - 160615 DOI: 10.1109/ACCESS.2020.3020879 - 2020 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    2. Kabir R., Watanobe Y., Islam M.R., A Cloud-Based Robot Framework for Indoor Object Identification Using Unsupervised Segmentation Technique and Convolution Neural Network (CNN), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12799 LNAI, pp. 199 - 211, DOI: 10.1007/978-3-030-79463-7_17 - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    3. Yang Z., Li S., Xu H., Yu D., Wang Z., Philip Chen C.L., Formation Control of Omnidirectional Mobile Robots Based on Bionic Coupling Mechanism (2021) Proceedings of 2021 IEEE International Conference on Unmanned Systems, ICUS 2021, pp. 184 - 189 DOI: 10.1109/ICUS52573.2021.9641498 - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    4. Wang D., Yang K., Liu L., An adaptive behavior decision model of mobile robot based on the neuromodulation, (2021) Artificial Life and Robotics, 26 (1), pp. 66 - 75, DOI: 10.1007/s10015-020-00629-z - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    5. Barreto-Cubero AJ, Gómez-Espinosa A, Escobedo Cabello JA, Cuan-Urquizo E, Cruz-Ramírez SR. Sensor Data Fusion for a Mobile Robot Using Neural Networks. Sensors. 2022; 22(1):305. https://doi.org/10.3390/s22010305 - 2022 - в издания, индексирани в Scopus или Web of Science
    6. Khan M.S.A., Hussain D., Naveed K., Khan U.S., Mundial I.Q., Aqeel A.B., Investigation of Widely Used SLAM Sensors Using Analytical Hierarchy Process (2022) Journal of Sensors, 2022, art. no. 5428097 DOI: 10.1155/2022/5428097 - 2022 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    7. Zhu L., Chen Y., Positioning of Basketball Robot Target Recognition System Based on Data Mining Algorithm (2022) 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2022, pp. 1317 - 1320 DOI: 10.1109/IPEC54454.2022.9777376 - 2022 - в издания, индексирани в Scopus или Web of Science
    8. Syntakas S., Vlachos K., Likas A., Object Detection and Navigation of a Mobile Robot by Fusing Laser and Camera Information (2022) 2022 30th Mediterranean Conference on Control and Automation, MED 2022, pp. 557 - 563 DOI: 10.1109/MED54222.2022.9837249 - 2022 - в издания, индексирани в Scopus или Web of Science
    9. Xu J., Wang L., Kou Q., Fang T., You D., Zhou L., Zhang Y., REAL-TIME BEHAVIOR DECISION OF MOBILE ROBOT BASED ON THE DELIBERATE/REACTIVE ARCHITECTURE (2022) International Journal of Innovative Computing, Information and Control, 18 (4), pp. 1163 - 1180, DOI: 10.24507/ijicic.18.04.1163 - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в реферирано издание, индексирана в Scopus