Intelligent Robotic Navigation Using Hybrid Sensor Networks

  • Authors

    • Dr. Hiroshi Tanaka Professor, University of Tokyo, Japan Author
    • Dr. Yuki Nakamura Associate Professor, Kyoto University, Japan Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2019PII9H8V

    Published 10-04-2019

  • Intelligent Robotics, Hybrid Sensor Networks, Autonomous Navigation, Sensor Fusion, SLAM, Mobile Robots, Lidar, Ultrasonic Sensors, Path Planning, Obstacle Avoidance, Wireless Sensor Networks, Machine Learning, Artificial Intelligence, Robotic Localization, IMU, Vision-Based Navigation

    Issue

    Vol. 2 No. 2 (2019)

    Section

    Articles

    How to Cite

    [1]
    H. Tanaka and Y. Nakamura, “Intelligent Robotic Navigation Using Hybrid Sensor Networks”, IJIARE, vol. 2, no. 2, pp. 01–17, Oct. 2019, doi: 10.67228/30715725/IJIARE-2019PII9H8V.

  • Abstract

    Intelligent robotic navigation has become an important research area for autonomous systems operating in dynamic and uncertain environments. Before 2019, advancements in robotics, artificial intelligence, embedded systems, and wireless communication improved autonomous robots used in healthcare, agriculture, transportation, military, and service applications. Traditional single-sensor systems faced challenges such as low localization accuracy and poor environmental perception. To address these issues, Hybrid Wireless Sensor Networks (HWSNs) combined sensors like LiDAR, GPS, IMUs, cameras, ultrasonic sensors, and RFID to enhance navigation, obstacle detection, mapping, and reliability. Research focused on sensor fusion, SLAM, Kalman filtering, fuzzy logic, neural networks, machine learning, and real-time obstacle avoidance. The proposed hybrid framework supports accurate localization, autonomous mapping, and efficient path planning for indoor and outdoor environments. Experimental results show that hybrid sensor systems improve navigation accuracy, reduce localization errors, and enhance obstacle detection efficiency. The study concludes that hybrid sensor networks are essential for future autonomous robotic systems, with future research focusing on deep reinforcement learning, cloud robotics, edge computing, cognitive navigation, and IoRT-based architectures.

  • References

    [1] R. A. Brooks, “A Robust Layered Control System for a Mobile Robot,” IEEE Journal of Robotics and Automation, vol. 2, no. 1, pp. 14–23, Mar. 1986.

    [2] S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. Cambridge, MA, USA: MIT Press, 2005.

    [3] H. Durrant-Whyte and T. Bailey, “Simultaneous Localization and Mapping: Part I,” IEEE Robotics & Automation Magazine, vol. 13, no. 2, pp. 99–110, Jun. 2006.

    [4] T. Bailey and H. Durrant-Whyte, “Simultaneous Localization and Mapping (SLAM): Part II,” IEEE Robotics & Automation Magazine, vol. 13, no. 3, pp. 108–117, Sept. 2006.

    [5] M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem,” in Proc. AAAI National Conference on Artificial Intelligence, Edmonton, Canada, 2002, pp. 593–598.

    [6] G. Welch and G. Bishop, “An Introduction to the Kalman Filter,” University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, Tech. Rep. TR 95-041, 2006.

    [7] A. Doucet, N. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice. New York, NY, USA: Springer, 2001.

    [8] L. A. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.

    [9] E. H. Mamdani and S. Assilian, “An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller,” International Journal of Man-Machine Studies, vol. 7, no. 1, pp. 1–13, 1975.

    [10] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015.

    [11] D. Scaramuzza and F. Fraundorfer, “Visual Odometry [Tutorial],” IEEE Robotics & Automation Magazine, vol. 18, no. 4, pp. 80–92, Dec. 2011.

    [12] R. Mur-Artal, J. M. M. Montiel, and J. D. Tardós, “ORB-SLAM: A Versatile and Accurate Monocular SLAM System,” IEEE Transactions on Robotics, vol. 31, no. 5, pp. 1147–1163, Oct. 2015.

    [13] J. Levinson and S. Thrun, “Robust Vehicle Localization in Urban Environments Using Probabilistic Maps,” in Proc. IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 2011, pp. 4372–4378.

    [14] B. Siciliano and O. Khatib, Springer Handbook of Robotics. Berlin, Germany: Springer, 2016.

    [15] C. Cadena et al., “Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age,” IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1309–1332, Dec. 2016.

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