This paper proposes a UKF-Based indoor localization method that evaluates the optimal position of a robot by fusing the position information from encoders and the distance information of the obstacle measured by ultrasonic sensors. UKF is a method of evaluating the robot’s position by transforming optimal sigma points extracted using the unscented transform and is advantageous for the localization of a nonlinear system. To solve the problem of the specular reflection effect of ultrasonic sensors, we propose a validation gate that evaluates the reliability of the ranges measured by sonar sensors, that can maximize the quality of the position evaluation. The experimental results showed that the method is stable and convergence of the position error regardless of the size of the initial position error and the length of the sampling time.
This paper proposes a practical method, for evaluating 3-D positioning of outdoor mobile robots using the Unscented Kalman Filter (UKF). The UKF method does not require the linearization process unlike conventional EKF localization, so it can minimize effects of errors caused by linearization of non-linear models for position estimation. Also, this method does not require Jacobian calculations difficult to calculate in the actual implementation. The 3-D position of the robot is predicted using an encoder and tilt sensor, and the optimal position is estimated by fusing these predicted positions with the GPS and digital compass information. Experimental results revealed the proposed method is stable for localization of the 3D position regardless of initial error size, and observation period.
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Research on Parameter Compensation Method and Control Strategy of Mobile Robot Dynamics Model Based on Digital Twin Renjun Li, Xiaoyu Shang, Yang Wang, Chunbai Liu, Linsen Song, Yiwen Zhang, Lidong Gu, Xinming Zhang Sensors.2024; 24(24): 8101. CrossRef
A Study on Improving the Sensitivity of High-Precision Real-Time Location Receive based on UWB Radar Communication for Precise Landing of a Drone Station Sung-Ho Hong, Jae-Youl Lee, Dong Ho Shin, Jehun Hahm, Kap-Ho Seo, Jin-Ho Suh Journal of the Korean Society for Precision Engineering.2022; 39(5): 323. CrossRef
This paper proposes a practical method, for evaluating positioning of outdoor mobile robots using Unscented Kalman Filter (UKF). Since the UKF method does not require the linearization process unlike EKF localization, it can minimize effects of errors caused by linearization of non-linear models for position estimation. This method enables relatively high performance position estimation, using only non-inertial sensors such as low-precision GPS and a digital compass. Effectiveness of the UKF localization method was verified through actual experiments and performance of position estimation was compared with that of the existing EKF method. Experimental results revealed the proposed method has better performance than the EKF method, and it is stable regardless of initial error size, and observation period.
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Localization-based waiter robot for dynamic environment using Internet of Things Muhammad Waqas Qaisar, Muhammad Mudassir Shakeel, Krzysztof Kędzia, José Mendes Machado, Ahmed Zubair Jan International Journal of Information Technology.2025; 17(6): 3675. CrossRef
Research on Parameter Compensation Method and Control Strategy of Mobile Robot Dynamics Model Based on Digital Twin Renjun Li, Xiaoyu Shang, Yang Wang, Chunbai Liu, Linsen Song, Yiwen Zhang, Lidong Gu, Xinming Zhang Sensors.2024; 24(24): 8101. CrossRef
Indoor Localization of a Mobile Robot based on Unscented Kalman Filter Using Sonar Sensors Soo Hee Seo, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2021; 38(4): 245. CrossRef
Unscented Kalman Filter Based 3D Localization of Outdoor Mobile Robots Woo Seok Lee, Min Ho Choi, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2020; 37(5): 331. CrossRef
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