Mobile Robot Localization in Indoor Environments Using Fuzzy Adaptive Unscented Kalman Filter and Random Tree Routing Algorithm with Fast Exploration

[1] Panigrahi, P.K. and S.K. Bisoy, Localization strategies for autonomous mobile robots: A review. Journal of King Saud University-Computer and Information Sciences, 2022. 34(8): p. 6019-6039.

[2] Y. Liu, S. Wang, Y. Xie, T. Xiong, and M. Wu, "A Review of Sensing Technologies for Indoor Autonomous Mobile Robots," Sensors, vol. 24, no. 4, p. 1222, Feb. 2024.

[3] Siegwart, R., I.R. Nourbakhsh, and D. Scaramuzza, Introduction to autonomous mobile robots. 2011: MIT press.

[4] Zheng, L., et al., Heading estimation for multimode pedestrian dead reckoning. IEEE Sensors Journal, 2020. 20(15): p. 8731-8739.

[5] Madray, I., et al., Relative angle correction for distance estimation using K-nearest neighbors. IEEE Sensors Journal, 2020. 20(14): p. 8155-8163.

[6] Guo, S., et al., An improved PDR/UWB integrated system for indoor navigation applications. IEEE Sensors Journal, 2020. 20(14): p. 8046-8061.

[7] Wang, X.-l., L.-q. Li, and W.-x. Xie, A novel TS fuzzy particle filtering algorithm based on fuzzy C-regression clustering. International Journal of Approximate Reasoning, 2020. 117: p. 81-95.

[8] A. H. Hassaballa, A. M. Kamel, I. Arafa, and Y. Z. Elhalwagy, "Adaptive Precise Attitude Estimation Using Unscented Kalman Filter in High Dynamics Environments," Unmanned Systems, vol. 12, no. 04, pp. 653–665, 2024.

[9] R. D. Puriyanto and A. K. Mustofa, "Design and Implementation of Fuzzy Logic for Obstacle Avoidance in Differential Drive Mobile Robot," Journal of Robotics and Control (JRC), vol. 5, no. 1, 2024.

[10] Shaheen, O., et al., Stable adaptive probabilistic Takagi–Sugeno–Kang fuzzy controller for dynamic systems with uncertainties. ISA transactions, 2020. 98: p. 271-283.

[11] Liu, Y. and D. Cui, Vehicle state estimation based on adaptive fading unscented kalman filter. Mathematical Problems in Engineering, 2022. 2022(1): p. 7355110.

[12] S. H. Hashemi and N. Pariza, "Adaptive Transformed Unscented Simplex Cubature Kalman Filter for Target Tracking," Automatika, vol. 62, no. 3, pp. 3279–3287, 2021.

[13] Al-sudany, H.N. and B. Lantos, Comparison of Adaptive Fuzzy EKF and Adaptive Fuzzy UKF for State Estimation of UAVs Using Sensor Fusion. Periodica Polytechnica Electrical Engineering and Computer Science, 2022. 66(3): p. 215-266.

[14] Sun, Z., et al., Multi-Risk-RRT: An Efficient Motion Planning Algorithm for Robotic Autonomous Luggage Trolley Collection at Airports. IEEE Transactions on Intelligent Vehicles, 2024.

[15] Ding, J., et al., An improved RRT* algorithm for robot path planning based on path expansion heuristic sampling. Journal of Computational Science, 2023. 67: p. 101937.

[16] Szabat, K., et al., A fuzzy unscented Kalman filter in the adaptive control system of a drive system with a flexible joint. Energies, 2020. 13(8): p. 2056.

[17] Fang, Y., et al., Adaptive Unscented Kalman Filter for Robot Navigation Problem (Adaptive Unscented Kalman Filter Using Incorporating Intuitionistic Fuzzy Logic for Concurrent Localization and Mapping). IEEE Access, 2022. 10: p. 101869-101879.

[18] V. E. Papageorgiou and G. Tsaklidis, "An improved epidemiological-unscented Kalman filter (hybrid SEIHCRDV-UKF) model for the prediction of COVID19: Application on real-time data," *Chaos*, vol. 2022, Article 112914, 2022.

[19] Kumar, M. and S. Mondal, A Fuzzy-based Adaptive Unscented Kalman Filter for State Estimation of Threedimensional Target Tracking. International Journal of Control, Automation and Systems, 2023. 21(11): p. 3804-3812.

[20] K. Feng, J. Wang, X. Wang, G. Wang, Q. Wang, and J. Han, "Adaptive state estimation and filtering for dynamic positioning ships under time-varying environmental disturbances," *Ocean Engineering*, vol. 2024, Article 117798, 2024.

[21] X. Cui, C. Wang, Y. Xiong, L. Mei, and S. Wu, "More Quickly-RRT*: Improved Quick Rapidly-exploring Random Tree Star algorithm based on optimized sampling point with better initial solution and convergence rate," Engineering Applications of Artificial Intelligence, vol. 108, p. 108246, 2024.

[22] Orthey, A., C. Chamzas, and L.E. Kavraki, Samplingbased motion planning: A comparative review. Annual Review of Control, Robotics, and Autonomous Systems, 2023. 7.

[23] Y. Lee, "Trajectory Manifold Optimization for Fast and Adaptive Kinodynamic Motion Planning," arXiv preprint arXiv:2410.12193, 2024.

[24] D. Debnath, F. Vanegas, J. Sandino, A. F. Hawary, and F. Gonzalez, “A Review of UAV Path-Planning Algorithms and Obstacle Avoidance Methods for Remote Sensing Applications,” *Remote Sens.*, vol. 16, no. 21, p. 4019, 2024.

[25] Szabat, K., et al., A fuzzy unscented Kalman filter in the adaptive control system of a drive system with a flexible joint. Energies, 2020. 13(8): p. 2056. [26] Woo, R., E.-J. Yang, and D.-W. Seo, A fuzzy-innovationbased adaptive Kalman filter for enhanced vehicle positioning in dense urban environments. Sensors, 2019. 19(5): p. 1142.

[27] Asl, R.M., et al., Fuzzy-based parameter optimization of adaptive unscented Kalman filter: Methodology and experimental validation. IEEE Access, 2020. 8: p. 54887-54904.

[28] J. C. R. Alcantud, A. Z. Khameneh, G. Santos-García, et al., "A systematic literature review of soft set theory," *Neural Comput. Appl.*, vol. 36, pp. 8951–8975, Jun. 2024.

[29] Matía, F., et al., The fuzzy Kalman filter: Improving its implementation by reformulating uncertainty representation. Fuzzy Sets and Systems, 2021. 402: p. 78-104.

[30] R. Havangi and M. Moradi, "PSO Based EKF Wheel-rail Adhesion Estimation," *Research Articles*, University of Birjand, doi: 10.22111/ieco.2023.43360.1446, in press.