Localization of solar panel cleaning robot combining vision processing and extended Kalman filter

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.

LPWAN-Based Real-Time 2D SLAM and Object Localization for Teleoperation Robot Control

In this study, the teleoperation robot control on a mobile robot with 2D SLAM and object localization using LPWAN is proposed. The mobile robot is a technology gaining popularity due to flexibility and robustness in a variety of terrains. In search and rescue activities, the mobile robots can be used to perform some missions, assist and preserve human life. However, teleoperation control becomes a challenging problem for this implementation. The robust wireless communication not only allows the operator to stay away from dangerous area, but also increases the mobility of the mobile robot itself. Most of teleoperation mobile robots use Wi-Fi having high-bandwidth, yet short communication range. LoRa as LPWAN, on the other hand, has much longer range but low-bandwidth communication speed. Therefore, the combination of them complements each other’s weaknesses. The use of a two-LoRa configuration also enhances the teleoperation capabilities. All information from the mobile robot can be sent to the PC controller in relatively fast enough for real-time SLAM implementation. Furthermore, the mobile robot is also capable of real-time object detection, localization, and transmitting images. Another problem of LoRa communication is a timeout. We apply timeout recovery algorithms to handle this issue, resulting in more stable data. All data have been confirmed by real-time trials and the proposed method can approach the Wi-Fi performance with a low waiting time or delay.