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In autonomous driving of the mobile robot, the robot's current location should be identified first to plan and move a path to the target location. Accordingly, research on the robot's localization using GPS, 3D LiDAR, and Vision has been actively conducted. However, there is a limitation in that it is difficult to locate robots in indoor spaces where signals are disturbed by walls or ceilings, or in areas where sufficient environmental information cannot be obtained. This paper introduces the robot's position estimation method to overcome these environmental problems by using sensor fusion in an indoor tennis court. We propose a localization method that has low latency performance and high location accuracy through the use of Kalman filters to fuse data from wheel odometry and visual-inertial odometry. To evaluate its performance, this method was compared against wheel odometry, visual-inertial odometry, and LIO-SAM after the robot completed three rectangular paths. The resultant mean absolute errors in the x and y directions were 1.908 m and 0.707 m for wheel odometry, 1.169 m and 1.430 m for visual-inertial odometry, and 0.400 m and 0.383 m for LIO-SAM, respectively. In contrast, the wheel-visual-inertial odometry introduced in this study reported errors of 0.209 m and 0.103 m in the x and y directions, respectively, indicating superior accuracy compared to the other algorithms. This underscores the effectiveness of the proposed method in indoor environments where signals can be obstructed by walls or ceilings, or in areas lacking abundant environmental information. [ABSTRACT FROM AUTHOR]

Collaborative robots combined with mobile platforms can move freely and have a wide work area. Technological developments in AI and vision have also led to increased research on recognition of objects and surroundings using mobile manipulators. However, even though such trends have resulted in growing demand for research on mobile manipulators, their high prices make them difficult to purchase. We solved this problem using 3D printing. A mobile manipulator was manufactured by replacing more expensive reducers with 3D printed planetary gear and cycloid gear reducers. As well, a mecanum in-wheel motor equipped with a 3D printed cycloid reducer was developed to increase the usability of the interior space of the mobile platform. The study employed a task-space control algorithm, which controls the mobile platform and the manipulator simultaneously to ensure effective control of the mobile manipulator. Task-space motion control performance was verified through Gazebo simulation and experiment. Additionally, the repeatability of the task-space position control was measured through experiments and its performance was compared with a commercial manipulator. Finally, we have developed a mobile manipulator that can perform similar levels of work as a commercial robot at one tenth the price of a general commercial mobile manipulator. [ABSTRACT FROM AUTHOR]