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
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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
This paper introduces a new outdoor localization method for practical application to guide robots. This method uses only encoder data from the robot’s wheels and non-inertial sensors, such as GPS and a digital compass, to guarantee ease of use and economy in real world usage without cumulative error. Position and orientation information from DGPS (Differential Global Positioning System) and a digital compass are combined with encoder data from the robot’s wheels to more accurately estimate robot position using an extended Kalman filter. Conventional robot guidance methods use different types of fusion that rely on DGPS. We use a very simple and consistent method that ensures localization stability by using the validation gate to evaluate DGPS reliability and digital compass data that can be easily degraded by various noise sources. Experimental results of the localization are presented that show the feasibility and effectiveness of the methods using a real robot in real world conditions.
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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
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
Estimation of Vertical Displacement based on Inertial Sensor Signals Combined with Joint Constraint Jung Keun Lee Journal of the Korean Society for Precision Engineering.2019; 36(3): 233. CrossRef
Kinematic Constraint-Projected Kalman Filter to Minimize Yaw Estimation Errors Induced by Magnetic Distortions Tae Hyeong Jeon, Jung Keun Lee Journal of the Korean Society for Precision Engineering.2019; 36(7): 659. 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
Unscented Kalman Filter based Outdoor Localization of a Mobile Robot Woo Seok Lee, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2019; 36(2): 183. CrossRef
Dynamic Accuracy Improvement of a MEMS AHRS for Small UAVs Min-Shik Roh, Beom-Soo Kang International Journal of Precision Engineering and Manufacturing.2018; 19(10): 1457. CrossRef
Study on Robust Lateral Controller for Differential GPS-Based Autonomous Vehicles Hyung-Gyu Park, Kyoung-Kwan Ahn, Myeong-Kwan Park, Seok-Hee Lee International Journal of Precision Engineering and Manufacturing.2018; 19(3): 367. CrossRef
GPS-Based Human Tracking Methods for Outdoor Robots Woo Seok Lee, In Ho Cho, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2018; 35(4): 413. CrossRef
The paper presented a method of early fire detection based on the environmental characteristics inside the nacelle of wind turbine generator system(WTGS). The rising rates of the temperature and smoke density were used as the parameters for early fire detection. By considering the characteristics of temperature and smoke density of a nacelle, this method is very reliable and can minimize the possibility of a malfunction of fire detection. The performance of the method was tested through sets of experiments by using nacelle simulator.
This paper presents an optimal capacity design of a Hybrid generation system based on economical evaluation for various loads. Optimal sizes of a standalone and grid connection wind-PV hybrid systems were designed for normal, residential and industrial loads using HOMER(Hybrid Optimization Model for Electronic Renewable). Their economical evaluation were performed and compared with a diesel generation system that covers the same loads. The results showed that the stand alone hybrid generation system can be more economical than a diesel generation system for long term operation.
The localization system for tour guide robot was developed which is inevitable and important for the guide robot in order to guide the tourists and explain the history or contents of the site. The localization system is based on the non-inertial sensors such as a DGPS, Dead-Reckoning. The information of the DGPS is used to update the estimated positions from Dead Reckoning. The extended Kalman filter was used for the fusion of the measured information from the sensors and estimated positions by Dead Reckoning. The simulation results show that it is very reliable and the position error is bounded within a certain extend.
This paper presents an efficient method of extracting line segments from the occupancy grids in a grid map. The grid map is composed of 2-D grids that have both the occupancy and orientation probabilities using sonar sensors. We evaluate the orientation information of every grid when the occupancy probability of the grid is updated from sonar range data. To find the shape of an object in the map from orientation information, the orientations are clustered into several groups according to their values. The line segments are, then, extracted from the clusters based on Hough transform. Finally, a feature-based map is built with these line segments. The proposed method is illustrated with the results produced by sets of experiments in an indoor environment.
This paper presents a 3-D localization of an autonomous underwater vehicle(AUV). Conventional methods of localization, such as LBL or SBL, require additional beacon systems, which reduces the flexibility and availability of the AUV. We use a digital compass, a pressure sensor, a clinometer and ultrasonic sensors for localization. From the orientation and velocity information, a priori position of the AUV is estimated based on the dead reckoning. With the aid of extended Kalman filter algorithm, a posteriori position of the AUV is estimated by using the distance between the AUV and a mother ship on the surface of the water together with the water depth information from the pressure sensor. Simulation results show the possibility of practical application of the method to autonomous navigation of the AUV
The paper presents a small unmanned probing ship that can be used for acquiring information on marine and coastal environments. For communication system, we adopt direct and indirect methods based on the wireless modem of a commercial cellular telephone. The former is a direct communication between the modems of the ship and the server, and the latter' is an indirect communication via internet between the ship and the server. The system is equipped with a digital compass and a GPS for position estimation, and extended Kalman filter is used for Navigation. The performance of the ship is demonstrated with the results produced by sets of experiments.
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