Adaptive Vision-Based Navigation in Autonomous Robotic Systems
Article Sidebar
Main Article Content
Abstract
An intelligent vision-based autonomous robotic framework that integrates deep learning-based object detection with hybrid adaptive navigation for dynamic environments is proposed in this research. The proposed system addresses the challenges of real-time perception and robust navigation in unstructured settings by combining a convolutional neural network (CNN) for object detection with a hybrid control mechanism for motion planning. The CNN, implemented using a state-of-the-art architecture such as YOLO, processes visual input to identify obstacles and target objects, providing critical environmental awareness. Moreover, the hybrid navigation strategy merges reactive obstacle avoidance, achieved through algorithms like the Vector Field Histogram (VFH), with adaptive path planning using Rapidly-exploring Random Trees (RRT) to ensure both immediate collision avoidance and long-term goal convergence. The integration of these components enables the robotic system to dynamically adjust its navigation policy in response to environmental changes, thereby improving robustness and adaptability. The novelty of our approach lies in the seamless fusion of vision-based perception and adaptive control, which enhances the system’s capability to operate in complex, dynamic scenarios. Experimental validation demonstrates the effectiveness of the framework in real-world applications, highlighting its potential for deployment in autonomous vehicles, service robotics, and industrial automation. The proposed method offers a scalable and efficient solution for autonomous systems requiring high levels of situational awareness and adaptive decision-making.
Manuscript received: 18 Dec 2025 | Revised: 17 Apr 2026 | Accepted: 23 Apr 2026 | Published: 31 Jul 2026
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
B. Romão and E. Fagotto, "Convolutional Neural Networks for Object Detection," SAE Technical Paper Series, vol. 1, 2024.
DOI: https://doi.org/10.4271/2023-36-0097
A. Medina-Santiago, L. A. Morales-Rosales, J. L. Hernandez-Gonzalez, and R. A. Osornio-Rios, “Reactive obstacle-avoidance systems for wheeled mobile robots based on artificial intelligence,” Applied Sciences, vol. 11, no. 12, pp. 1–24, 2021.
DOI: https://doi.org/10.3390/app11125512
M. Elbanhawi, M. Simic, and R. Jazar, “Autonomous robots path planning: An adaptive roadmap approach,” Applied Mechanics and Materials, vol. 110–116, pp. 150–158, 2013.
DOI: https://doi.org/10.4028/www.scientific.net/AMM.110-116.150
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 779–788, 2016. DOI: https://doi.org/10.1109/CVPR.2016.91
J. Redmon and A. Farhadi, “YOLOv3: An incremental improvement”, arXiv preprint, arXiv:1804.02767, 2018. [Online]. Available: https://arxiv.org/abs/1804.02767
J. Borenstein and Y. Koren, “The vector field histogram—Fast obstacle avoidance for mobile robots,” IEEE Transactions on Robotics and Automation, vol. 7, no. 3, pp. 278–288, 1991.
DOI: https://doi.org/10.1109/70.88137
M. Gromniak and J. Stenzel, “Deep reinforcement learning for mobile robot navigation”, 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), Nagoya, Japan, pp. 25–30, 2019.
DOI: https://doi.org/10.1109/ACIRS.2019.8936033
S. M. LaValle, “Rapidly-exploring random trees: A new tool for path planning”, Computer Science Dept., Iowa State Univ., Ames, IA, USA, Tech. Rep. 9811, 1998.
S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning”, The International Journal of Robotics Research, vol. 30, no. 7, pp. 846–894, 2011.
DOI: https://doi.org/10.1177/0278364911406761
J. Bruce and M. Veloso, “Real-time randomized path planning for robot navigation”, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Lausanne, Switzerland, pp. 2383–2388, 2002.
DOI: https://doi.org/10.1109/IRDS.2002.1044082
C. Duan, S. Junginger, J. Huang, and K. Jin, “Deep learning for visual SLAM in transportation robotics: A review”, Transportation Safety and Environment, Vol. 1, No. 2, pp. 135–150, 2019.
DOI: https://doi.org/10.1093/tse/tdz015
Y. H. Kim, J. I. Jang, and S. Yun, “End-to-end deep learning for autonomous navigation of mobile robot”, 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), Kuala Lumpur, Malaysia, pp. 1039–1044, 2018.
DOI: https://doi.org/10.1109/ROBIO.2018.8664765
M. B. Alatise and G. P. Hancke, “A review on challenges of autonomous mobile robot and sensor fusion methods”, IEEE Access, vol. 8, pp. 39830–39846, 2020.
DOI: https://doi.org/10.1109/ACCESS.2020.2975643
S. Battle, “Principles of Robot Autonomy I”, 2024. [Online]. Available: https://www.roboticsbook.org
M. G. Mohanan and A. Salgoankar, “A survey of robotic motion planning in dynamic environments”, Robotics and Autonomous Systems, Vol. 100, pp. 171–185, 2018.
DOI: https://doi.org/10.1016/j.robot.2017.10.011
B. S. Park, S. J. Yoo, J. B. Park, and Y. H. Choi, “A simple adaptive control approach for trajectory tracking of electrically driven nonholonomic mobile robots”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 39, No. 4, pp. 1196–1202, 2009.
DOI: https://doi.org/10.1109/TSMCB.2009.2018617
D. Sankowski and J. Nowakowski, "Computer Vision in Robotics and Industrial Applications," Series in Computer Vision, 2014.
DOI: https://doi.org/10.1142/9090
S. Abrecht, L. Gauerhof, C. Gladisch, and K. Groh, “Testing deep learning-based visual perception for automated driving”, ACM Transactions on Intelligent Systems and Technology, Vol. 12, No. 4, pp. 1–29, 2021.
DOI: https://doi.org/10.1145/3459994
A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “YOLOv4: Optimal speed and accuracy of object detection”, arXiv preprint, arXiv:2004.10934, 2020. [Online]. Available: https://arxiv.org/abs/2004.10934
T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár and C.L. Zitnick, "Microsoft COCO: Common Objects in Context," Lecture Notes in Computer Science, pp. 740-755, 2014.
DOI: https://doi.org/10.1007/978-3-319-10602-1_48
L. Cai, T. Lai, L. Wang, Y. Zhou and Y. Xiong, "Graph convolutional network combining node similarity association and layer attention for personalized recommendation," Engineering Applications of Artificial Intelligence, vol. 121, pp. 105981, 2023.
DOI: https://doi.org/10.1016/j.engappai.2023.105981
W. Roth, G. Schindler, B. Klein, R. Peharz, and F. Pernkopf, “Resource-efficient neural networks for embedded systems,” Journal of Machine Learning Research, Vol. 25, No. 1, pp. 1–38, 2024. [Online]. Available: https://www.jmlr.org
C. Wang and D. Du, “Research on logistics autonomous mobile robot system”, 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), Beijing, China, pp. 141–145, 2016.
DOI: https://doi.org/10.1109/SOLI.2016.7551603
A. C. Lopes, G. Pires, and U. Nunes, “Assisted navigation for a brain-actuated intelligent wheelchair”, Robotics and Autonomous Systems, Vol. 61, No. 3, pp. 245–258, 2013.
DOI: https://doi.org/10.1016/j.robot.2012.11.002
L. Johansson, “Ethical aspects of military maritime and aerial autonomous systems”, Journal of Military Ethics, Vol. 17, No. 4, pp. 285–302, 2018,
DOI: https://doi.org/10.1080/15027570.2018.1516592
A. M. Guerrero, “Cybersecurity of robotics and autonomous systems: Privacy and safety”, Robotics: Legal, Ethical and Socioeconomic Aspects, Cham, Switzerland: Springer, pp. 123–139, 2017.
DOI: https://doi.org/10.1007/978-3-319-51454-3_8
D. Pasquali, J. Gonzalez-Billandon, F. Rea, G. Sandini and A. Sciutti, "Magic iCub," Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 293-302, 2021.