With the increasing severity of global warming, there is a growing need for eco-friendly vehicles to reduce greenhouse gas emissions. However, the expansion of charging infrastructure is struggling to keep up with the rising number of electric vehicles due to space constraints and installation costs. This paper aims to address this issue by proposing an autonomous driving algorithm for a mobile robot-based movable charging system for electric vehicles, as an alternative to traditional stationary charging stations. Our paper introduces a rule-based path planning algorithm for autonomous robot-based charging systems. To achieve this, we employ the A* (A-star) algorithm for global path planning towards the charging request position, while utilizing the Dynamic Window Approach (DWA) algorithm for generating avoidance paths around obstacles in the parking lot. The avoidance path generation algorithm differentiates between dynamic and static obstacles, with specific algorithms formulated for each type of obstacle. Finally, we implement the suggested algorithm and verify its performance through simulation.
This study reports an autonomous fine dust source tracking system of a water spray robot for high-rise building demolition. The core function of this system is performing a self-controlled fine dust tracking of the endpoint of the excavator, which is the fine dust generation point. The water spray robot has a lift with a parallelogram-shaped linkage to lift the water spray drum to 10 m from the ground. The sensor network system is connected to the robot and the excavator to calculate the relative position of the water spray drum and excavator endpoint using forward kinematics. RTK-GPS is attached to the robot and the excavator to calculate the relative distance. By sensor network, forward kinematics, and RTK-GPS, the water spray robot can autonomously track fine dust generation point and spray water to the endpoint of the excavator. The experiment was conducted to confirm the accuracy of kinematics calculation and tracking performance of the robot. The first experiment showed that the calculation result of forward kinematics was accurate enough to fulfill tracking operations. The second experiment showed that the tracking accuracy was precise enough, meaning that the robot could autonomously track fine dust generation point.
In this study, a novel size adjustable robot that could overcome an unstructured environment was introduced. To provide the robot with a volume-modifiable function, negative Poisson’s ratio structure with a unique characteristic about deformation of material was applied to the design of the body frame. The robot could simultaneously adjust its width and length with only one directional control with the help of the negative Poisson’s ratio structure. An omni-directional mobile mechanism was adopted to drive its wheels and allow flexible movement in a narrow space. However, during the procedure to adjust the size of the robot, a slip phenomenon occurred, resulting in an unnecessary movement. To solve this problem, the unnecessary offset was measured through repetitive tests and applied to the robot to compensate the position shift. To verify the performance of the robot, a test bed with a narrow space was fabricated. Extensive experiments were conducted to evaluate environmental recognition and size adjustment function by calculating the width of the narrow space and scaling the robot"s body. Results confirmed that the robot sufficiently achieved the motion objective to move in a narrow space with its size adjustment function.
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Auxetic and Holonomic Mobile Robot for Enhanced Navigation in Constrained Terrains Cheonghwa Lee, Jinwon Kim, Hyeongyeong Jeong, Hyunbin Park, Baeksuk Chu Journal of Field Robotics.2025; 42(8): 4414. CrossRef
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
In this study, slip phenomenon that occurs during trajectory tracking motion of an omni-directional mobile robot based on Mecanum wheels was analyzed. Mecanum wheels which generate the omni-directionality to the mobile robot comprise a centered rim wheel and passive sub-rollers. In forward and backward motion, they function like usual wheels to enable rolling along the ground. However, in sideways motion, they create lateral motion of the mobile robot from the rotational actuation using their peculiar structural configuration, during which slip of the sub-rollers occurs. Unnecessary over-slip of the sub-rollers causes tracking errors of the mobile robot motion. To analyze the properties and reasons for the slip phenomenon, squared and circular trajectory tacking experiments were performed. From the experiments, it was observed that sideways motion generated respectively larger tracking errors than forward and backward motion. The geometric analysis regarding the tracking error generation was discussed using the Mecanum wheel structure. Finally, it was confirmed that suspension mechanism to provide four Mecanum wheels of the mobile robot with even reaction forces on the ground is necessary.
Citations
Citations to this article as recorded by
Auxetic and Holonomic Mobile Robot for Enhanced Navigation in Constrained Terrains Cheonghwa Lee, Jinwon Kim, Hyeongyeong Jeong, Hyunbin Park, Baeksuk Chu Journal of Field Robotics.2025; 42(8): 4414. CrossRef
Development of Pipe Robot by Using Mecanum Wheels Daeyoung Kim, Soonwook Park, Hojoong Lee, Jongpil Kim, Wonji Chung, Dohoon Kwak Journal of the Korean Society of Manufacturing Process Engineers.2021; 20(2): 58. CrossRef
Mobile Robot Overcoming Narrow Space Using Negative Poisson’s Ratio Jinwon Kim, Hyeongyeong Jeong, Baeksuk Chu Journal of the Korean Society for Precision Engineering.2021; 38(7): 479. 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.
Citations
Citations to this article as recorded by
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
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