In this study, we introduce a method for estimating the position of a self-driving solar panel-cleaning mobile robot. This estimation relies on line counts, typically 16 cm in panel width, obtained through image processing on the panel floor, along with wheel encoder information and inertial sensor data. To achieve accurate line counts, we introduce two adjusted threshold values and allow offsets in these values based on the robot's speed. Additionally, inertial measurement unit (IMU) signals assist in determining whether a line is horizontal or vertical, depending on the robot's movement direction on the panel, utilizing the robot's heading angle and detected line angle. When the robot is positioned between lines on the panel, more precise location estimation is necessary beyond simple line counts. To tackle this challenge, we integrate the extended Kalman filter with IMU data and encoder information, significantly enhancing position estimation. This integration achieves an RMSE accuracy value of up to 0.089 m, notably at a relatively high speed of 100 mm/s. This margin of error is almost half that of the vision-based line-counting method.

This paper explores the design and control of a center-rotor type pentacopter that can move in contact with a wall to accurately measure cracks in a concrete outer wall. A center rotor pentacopter is a type of pentacopter that uses an electric ducted fan (EDF) motor and two axis servomotors at the center of gravity of the quadcopter. Center-rotor pentacopters are characterized by the ability to generate thrust in the desired direction using two servomotors and an EDF motor to maintain a stable attitude without tilting or leaning during flight. In this paper, the mathematical modeling of a center-rotor pentacopter was verified through simulation and compared with a regular quadcopter. We also simulated the thrust of the center rotor to see how much it could withstand when a sudden gust of wind occurred. Then, we built a pentacopter and measured the cracks and calculated the width of the cracks through the actual pentacopter in addition to flight experiments. The results showed the advantages and applicability of the pentacopter.

In this study, we introduce a method for estimating the position of a self-driving solar panel-cleaning mobile robot. This estimation relies on line counts, typically 16 cm in panel width, obtained through image processing on the panel floor, along with wheel encoder information and inertial sensor data. To achieve accurate line counts, we introduce two adjusted threshold values and allow offsets in these values based on the robot's speed. Additionally, inertial measurement unit (IMU) signals assist in determining whether a line is horizontal or vertical, depending on the robot's movement direction on the panel, utilizing the robot's heading angle and detected line angle. When the robot is positioned between lines on the panel, more precise location estimation is necessary beyond simple line counts. To tackle this challenge, we integrate the extended Kalman filter with IMU data and encoder information, significantly enhancing position estimation. This integration achieves an RMSE accuracy value of up to 0.089 m, notably at a relatively high speed of 100 mm/s. This margin of error is almost half that of the vision-based line-counting method.

This paper explores the design and control of a center-rotor type pentacopter that can move in contact with a wall to accurately measure cracks in a concrete outer wall. A center rotor pentacopter is a type of pentacopter that uses an electric ducted fan (EDF) motor and two axis servomotors at the center of gravity of the quadcopter. Center-rotor pentacopters are characterized by the ability to generate thrust in the desired direction using two servomotors and an EDF motor to maintain a stable attitude without tilting or leaning during flight. In this paper, the mathematical modeling of a center-rotor pentacopter was verified through simulation and compared with a regular quadcopter. We also simulated the thrust of the center rotor to see how much it could withstand when a sudden gust of wind occurred. Then, we built a pentacopter and measured the cracks and calculated the width of the cracks through the actual pentacopter in addition to flight experiments. The results showed the advantages and applicability of the pentacopter.

In this study, a system for automatically picking mechanical parts required in the industrial automation field was proposed. In particular, using deep learning, bolts and nuts were recognized and geometric information of these parts was extracted. By applying YOLOv3 specialized in high recognition rate and fast processing speed, the recognition of target object, location, and postural information were obtained. The geometric information for the bolt can be obtained by creating two bounding boxes and calculating the orientation vector formed by these center values of two bounding boxes after successfully detecting two individual bounding boxes. Moreover, to obtain more precise geometric information on bolts and nuts, image distortion compensation on the detected object was done after detecting the center value of the bolt and nut through YOLOv3. Based on this result, it was proven that an automatic picking of the mechanical parts using a five-axis robot was successfully implemented.

This study proposed a quadcopter equipped with flaps designed to attach to and move along walls, enabling safe photography of square-shaped bridge piers. It was verified through experiments whether flaps can generate sufficient and continuous force in the direction of the wall. Additionally, a mathematical model of the quadcopter with flaps was developed using lift and drag measurement data. Through simulations, it was confirmed that the drone could be controlled using the lift generated by the flaps. A prototype quadcopter with applied flaps was then created. An algorithm for processing essential sensor signals, such as attitude and speed, was presented for effective quadcopter control. Moreover, an algorithm utilizing two distance sensors was proposed to measure the angle and distance between the quadcopter and the wall. A controller was also designed to effectively manage the speed and yaw angle of the quadcopter. The developed quadcopter was validated through real experiments, demonstrating its ability to attach to walls and move along the bridge structure.

<span lang="EN-US">In this study, we propose a walking-type solar power cleaning robot mechanism driven by a driving unit composed of three driving lines. The triple driving lines are driven using a link mechanism, and vacuum pads are attached to each driving line to move the robot body through a sequence operation between the three lines. Through this mechanism, the robot body can be moved horizontally with the panel without folding the pad, and the amount of vertical movement is minimized during movement. By analyzing the pressure patterns of the pads on the driving line, smooth and fast movement was possible.</span>

<span lang="EN-US">In this study, we propose a method for recognizing the self-location of a drone flying in an indoor environment and introduce the flying performance using it. DWM1000, which is an ultra-wide band communication module, was used for accurate indoor self-location recognition. The self-localization algorithm constructs a formula using trilateration and finds the solution using the gradient descent method. Using the measured values of the distance between the modules in the room, it is found that the error stays within 10-20 cm when the newly proposed trilateration method is applied. We confirmed that the 3D position information of the drone can be obtained in real-time, and it can be controlled to move to a specific location. We proposed a drone control scheme to enable autonomous flight indoors based on deep learning. In particular, to improve the conventional convolutional neural network (CNN) algorithm that uses images from three video cameras, we designed a distinguished CNN structure with deeper layers and appropriate dropouts to use the input data set provided by only one camera.</span>

In this paper, we propose a novel fault-tolerant algorithm for CAN protocol, called “Seamless CAN”. Its operation concept is based on High-availability Seamless Redundancy (HSR) implementation inside the in-vehicle network's ring structure and fault tolerance is achieved by encapsulating CAN frames inside HSR frames and sending them in a ring topology. Simulation results show that Seamless CAN provides better performance regarding no frame loss in case of link failures and a lower number of error occurrences.