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 3D localization method for an outdoor mobile robot. This method assesses the 3D position including the altitude information, which is impossible in the existing 2D localization method. In this method, the 3D position of the robot is predicted using an encoder and an inclination sensor. The predicted position is fused with the position information obtained from the DGPS and the digital compass using extended kalman filter to evaluate the 3D position of the robot. The experimental results showed that the proposed method can effectively evaluate the 3D position of the robot in a sloping environment. Moreover, this method was found to be more effective than the conventional 2D localization method even in the evaluation of the plane position where altitude information is unnecessary.
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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
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
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
This paper presents a GPS-based method for outdoor robots to track humans. This new method can overcome the crucial problems of conventional techniques in complex environments with obstacles or sloped terrain that do not allow detecting the locations of humans out of the robot"s line of sight. The robot determines the position of the human with respect to GPS data and forms the trajectory of the human’s movement. This trajectory is then smoothed in real time to reduce sudden changes in the path and improve the tracking performance. We also propose an autonomous trajectory tracking method for the robot to avoid obstacles while effectively tracking the human trajectories. This method allows the robot to follow the human even in an environment with many robots and humans simultaneously present because the robot can always distinguish the human it should follow. The experiments demonstrate that robots can effectively follow the human while avoiding obstacles in complex environments.
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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
Extended Kalman Filter Based 3D Localization Method for Outdoor Mobile Robots Woo Seok Lee, Min Ho Choi, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2019; 36(9): 851. CrossRef
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