為了在無GPS區域實現全向移動機器人 (ODMR)的有效無線定位和導航，以 Residual Bi-LSTM (RBLSTM) 模型設計具有分佈式模塊化 UWB網絡(DM-UWBN)以提供這些優勢，例如，避免可能的過度擬合問題，再不會犧牲太多定位的前題下減少計算時間。除了 ODMR 的動態定位之外，還根據輸出的相對階數設計一種新的基於 Residue RNN 的固定時間跟踪控制（RRNN-FTTC），以對總體動態不確定性的穩定權重學習補償，達到更有效的追蹤控制。基於 ODMR 的運動學，設計新的及更有效的2D 參考姿態，與以往的研究相比，所設計的RRNN-FTTC具有以較小的控制輸入實現出色的跟踪性能。由於 RRNN-FTC（例如，5ms）和 DM- RBLSTM-UWBN（例如，150ms）的多採樣任務，相對應的導引將面臨更大的挑戰。最後，在無GPS的廣大區域中且有兩個支柱以呈現非視線效應，使用 RRNN-FTTC 導引 ODMR，證實我們的 RBLSTM-DM-UWBN 的優越性。

To achieve an effective wireless localization and navigation of omnidirectional mobile robot (ODMR) in a global GPS-denied region, the distributively modular UWB network (DM-UWBN) is designed by Residue Bi-LSTM (RBLSTM) model to provide these advantages, e.g., avoidance of the possible overfitting problem, reduction of computation time without much sacrifice of localization. Besides the dynamic localization of ODMR, a Residue RNN-based fixed-time tracking control (RRNN-FTTC), including the stabilizing weight learning compensation of aggregately dynamic uncertainties, is designed by relative degree of output. Based on the kinematics of ODMR, a new and more effective 2D reference pose is designed, so that the outstanding tracking performance is accomplished by small control input in comparison to previous research. Owing to multi-sampling tasks of RRNN-FTC (e.g., 5ms) and DM- RBLSTM-UWBN (e.g., 150ms), the navigation will be more challenged. Finally, the navigation of ODMR using RRNN-FTTC in the global GPS-denied region with serval pillars to present Non-Line of Sight effect confirm the superiority of the proposed approach.

[1] S. Gezici et al., “Localization via ultra-wideband radios: A look at positioning aspects for future sensor networks,” IEEE Signal Process. Mag., vol. 22, no. 4, pp. 70–84, Jul. 2005.
[2] A. Alarifi et al., “Ultra wideband indoor positioning technologies: Analysis and recent advances,” Sensors, vol. 16, no. 5, pp. 1–36, May 2016.
[3] Apple. Apple—iPhone 11. Accessed: Mar. 31. [Online]. Available: https://www.apple.com/iphone-11.
[4] K. Guo, X. Li, and L. Xie, “Ultra-wideband and odometry-based cooperative relative localization with application to multi-UAV formation control,” IEEE Trans. Cybern., vol. 50, no. 6, pp. 2590-2603, Jun. 2020.
[5] Y.-Y. Chen, S.-P. Huang, T.-W. Wu, W.-T. Tsai, C.-Y. Liou, and S.-G. Mao, “UWB system for indoor positioning and tracking with arbitrary target orientation, optimal anchor location, and adaptive NLOS mitigation,” IEEE Trans. Veh. Technol., vol. 69, no. 9, pp. 9304-9314, Sep. 2020.
[6] K. Zhao, M. Zhu, B. Xiao, X. Yang, C. Gong, and J. Wu, “Joint RFID and UWB technologies in intelligent warehousing management system,” IEEE Internet Things J., vol. 7, no. 12, pp. 11640–11655, Dec. 2020.
[7] Y. Xu, Y. S. Shmaliy, C. K. Ahn, T. Shen, and Y. Zhuang, “Tightly-coupled integration of INS and UWB using fixed-lag extended UFIR smoothing for quadrotor localization,” IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1716-1727, Feb. 2021.
[8] B. Silva and G. P. Hancke, “IR-UWB-based non-line-of-sight identification in harsh environments: principles and challenges,” IEEE Trans. Ind. Informat., vol. 12, no. 3, pp. 1188–1195, Jun. 2016.
[9] S. He and X. Dong, “High-accuracy localization platform using asynchronous time difference of arrival technology,” IEEE Trans. Instrum. Meas., vol. 66, no. 7, pp. 1728–1742, Jul. 2017.
[10] A. R. J. Ruiz and F. S. Granja, “Comparing ubisense, BeSpoon, and Deca Wave UWB location systems: Indoor performance analysis,” IEEE Trans. Instrum. Meas., vol. 66, no. 8, pp. 2106–2117, Aug. 2017.
[11] J. Sidorenko, V. Schatz, N. Scherer-Negenborn, M. Arens, and U. Hugentobler, “Error corrections for ultrawideband ranging,” IEEE Trans. Instrum. Meas., vol. 69, no. 11, pp. 9037–9047, Nov. 2020.
[12] P. Krapez and M. Munih, “Anchor calibration for real-time-measurement localization systems,” IEEE Trans. Instrum. Meas., vol. 69, no. 12, pp. 9907–9917, Dec. 2020.
[13] C. Taramasco, T. Rodenas, F. Martinez, P. Fuentes, R. Munoz, R. Olivares, V. H. De Albuquerque, and J. Demongeot, “A novel monitoring system for fall detection in older people,” IEEE Access, vol. 6, pp. 43563-43574, 2018.
[14] J. Zhou, Y. Lu, H.-N. Dai, H. Wang, and H. Xiao, “Sentiment analysis of Chinese microblog based on stacked bidirectional LSTM,” IEEE Access, vol. 7, pp. 38856-38866, 2019.
[15] C. Dhiman, and D. K. Vishwakarma, “View-invariant deep architecture for human action recognition using two-stream motion and shape temporal dynamics,” IEEE Trans. Image Process., vol. 29, pp. 3835-3844, 2020.
[16] G. Du, Z. Wang, B. Gao, S. Mumtaz, K. M. Abualnaja, and C. Du, “A convolution bidirectional long short-term memory neural network for driver emotion recognition,” IEEE Trans. Intel. Transp. Syst., vol. 22, no. 7, pp. 4570-4578, Jul. 2021.
[17] X. Sun, Y. Gao, R. Sutcliffe, S.-X. Guo, X. Wang, and J. Feng, “Word representation learning based on bidirectional GRUs with drop loss for sentiment classification,” IEEE Trans. Syst. Man Cybern.: Syst., DOI: 10.1109/TSMC.2019.2940097.
[18] Z. Huang, W. Xu, and K. Yu, “Bidirectional lstm-crf models for sequence tagging,” Computation and Language, vol. arXiv:1508.01991, 2015.
[19] K. Zhang, M. Sun, T. X. Han, X. Yuan, L. Guo, and T. Liu, “Residual networks of residual networks: multilevel residual networks,” IEEE Trans. Cir. Syst. Vid. Technol., vol. 28, no. 6, pp. 1303-1314, Jun. 2018.
[20] L. Zhu, S. Zhang, K. Chen, S. Chen, X. Wang, D. Wei, and H. Zhao, “Low-SNR recognition of UAV-to-ground targets based on micro-doppler signatures using deep convolutional denoising encoders and deep residual learning,” IEEE Trans. Geosci. Remote Sens, vol. 60, Art. No. 5106913, 2022.
[21] K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in arXiv:1603.05027v3, Jul 2016.
[22] G. Yang and H. Xu, “A Residual BiLSTM model for named entity recognition,” IEEE Access, vol. 8, pp. 227710-227718, 2020.
[23] M.-S. Ko, K. Lee, J.-K. Kim, C. W. Hong, Z. Y. Dong, and K. Hur, “Deep concatenated residual network with bidirectional LSTM for one-hour-ahead wind power forecasting,” IEEE Trans. Sustainable Energy, vol. 12, no. 2, pp. 1321-1335, Apr. 2021.
[24] C.-L. Hwang and Y.-H. Chen, “Fuzzy fixed-time learning control with saturated input, nonlinear switching surface and switching gain to achieve null tracking error,” IEEE Trans. Fuzzy Syst., vol. 28, no. 7, pp. 1464-1476, Jul. 2020.
[25] L. Xiao, S. Li, K. Li, L. Jin, and B. Liao, “Co-design of finite-time convergence and noise suppression: a unified neural model for time varying linear equations with robotic applications,” IEEE Trans. Syst. Man Cybern.: Syst. vol. 50, no. 12, pp. 2701-2711, Dec. 2020.
[26] K. Li, S. Tong, and Y. Li, “Finite-time adaptive fuzzy decentralized control for nonstrict-feedback nonlinear systems with output- constraint,” IEEE Trans. Syst. Man Cybern.: Syst. vol. 50, no. 12, pp. 5271-5284, Dec. 2020.
[27] Z. Lu, Q. Ge, Y. Li, and J. Hu, “Finite-time synchronization of memristor-based recurrent neural networks with inertial items and mixed delays,” IEEE Trans. Syst. Man Cybern.: Syst. vol. 51, no. 5, pp. 2701-2711, May 2021.
[28] C. Liu, H. Wang, X. Liu , and Y. Zhou, “Adaptive finite-time fuzzy funnel control for nonaffine nonlinear systems,” IEEE Trans. Syst. Man Cybern.: Syst. vol. 51, no. 5, pp. 2894-2903, May 2021.
[29] W. Qi, X. Yang, C. K. Ahn, J. Cao, and J. Cheng, “Input–output finite-time sliding-mode control for T–S fuzzy systems with application,” IEEE Trans. Syst. Man Cybern.: Syst., vol. 51, no. 9, pp. 5446-5455, Sep. 2021.
[30] C.-L. Hwang and B. S. Chen, “Adaptive finite-time saturated tracking control for a class of partially known robots,” IEEE Trans. Syst. Man Cybern.: Syst., vol. 51, no. 9, pp. 5674-5684, Sep. 2021.
[31] Y. Fu, N. Sun, T. Yang, Z. Qiu, and Y. Fang, “Adaptive coupling anti-swing tracking control of underactuated dual boom crane systems,” IEEE Trans. Syst. Man Cybern.: Syst. DOI: 10.1109/TSMC.2021. 3102244.
[32] V. K. Tripathi, A. K. Kamath, L. Behera, N. K. Verma, and S. Nahavandi, “An adaptive fast terminal sliding-mode controller with power rate proportional reaching law for quadrotor position and altitude tracking,” IEEE Trans. Syst. Man Cybern.: Syst. DOI: 10.1109 /TSMC.2021.3072099.
[33] J. Yan, Z. Guo, X. Yang, X. Luo, and X. Guan, “Finite-time tracking control of autonomous underwater vehicle without velocity measurements,” IEEE Trans. Syst. Man Cybern.: Syst. DOI: 10.1109/ TSMC.2021.3095975.
[34] C.-L. Hwang and H. B. Abebe, “Generalized and heterogeneous nonlinear dynamic multiagent systems using online RNN-based finite-time formation tracking control and application to transportation systems,” IEEE Trans. Intell. Transp. Syst., DOI: 10.1109/TITS. 2021.3126662.
[35] C.-F. Juang, C.-H. Lin, and T. B. Bui, “Multiobjective rule-based cooperative continuous ant colony optimized fuzzy systems with a robot control application,” IEEE Trans. Cybern., vol. 50, no. 2, pp. 650-663, Feb. 2020.
[36] C.-W. Kuo, C.-C. Tsai, and C.-T. Lee, “Intelligent leader-following consensus formation control using recurrent neural networks for small-size unmanned helicopters,” IEEE Trans. Syst. Man Cybern.: Syst. vol. 51, no. 2, pp. 1288-1301, Feb. 2021.
[37] W. Yuan, Z. Li, and C.-Y. Su, “Multisensor-based navigation and control of a mobile service robot,” IEEE Trans. Syst. Man Cybern.: Syst. vol. 51, no. 4, pp. 2624-2634, Apr. 2021.
[38] W. Xie, D. Cabecinhas, R. Cunha, and C. Silvestre, “Cooperative path following control of multiple quadcopters with unknown external disturbances,” IEEE Trans. Sys., Man, Cybern.: Syst., vol. 52, no. 1, pp. 667-679, Jan. 2022.
[39] C.-L. Hwang, F. C. Weng, D. S. Wang, and F. Wu, “Experimental validation of speech improvement based adaptive stratified finite-time saturation control of omnidirectional service robot,” IEEE Trans. Sys., Man, Cybern.: Syst., vol. 52, no. 2, pp. 1317-1330, Feb. 2022.
[40] T. Terakawa, M. Komori, K. Matsuda, and S. Mikami, “A novel omnidirectional mobile robot with wheels connected by passive sliding joints,” IEEE/ASME Trans. Mechatronics, vol. 23, no. 4, 1716-1727, Jul. 2018.
[41] C. Ren, X. Li , X. Yang, and S. Ma, “Extended state observer-based sliding mode control of an omnidirectional mobile robot with friction compensation,” IEEE Trans. Ind. Electron., vol. 66, no. 12, pp. 9480-9489, Dec. 2019.
[42] C.-L. Hwang, D.-S. Wang, F.-C. Weng, and S.-L. Lai, “Interactions between specific human and omnidirectional mobile robot using deep learning approach: SSD-FN-KCF,” IEEE Access, vol. 8, pp. 41186-41200, 2020.
[43] L. Barbieri, M. Brambilla, A. Trabattoni, S. Mervic, and M. Nicoli, “UWB localization in a smart factory: augmentation methods and experimental assessment,” IEEE Trans. Instru. Meas., DOI: 10.1109/TIM.2021.3074403.
[44] S. R. Dubey, S. Chakraborty, S. K. Roy, S. Mukherjee, S. K. Singh, and B. B. Chaudhuri, “diffGrad: An optimization method for convolutional neural networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 11, pp. 4500-4511, Nov. 2020.
[45] H. K. Khalil, Noninear System. 2nd Ed., Prentice-Hall Inc. 1996.
[46] C.-L. Hwang and N. W. Chang, “Fuzzy decentralized sliding-mode control of car-like mobile robots in a distributed sensor-network space,” IEEE Trans. Fuzzy Syst., vol. 16, no. 1, pp. 97-109, Feb. 2008.