Ardy Seto P.

The fundamental aim of this project is to develop a solar-powered automatic weather station (AWS), which can be accessed via the website. Users can find out the weather changes in an area without needing to come to the area and can do an analysis of irrigation water needs. This design uses ESP32 as main processor. The measure weather parameters include temperature and humidity using HDC1080 sensor, wind speed using an anemometer sensor, wind direction using a wind vane sensor, air pressure using BME280, rainfall using a tipping bucket sensor, and the last small solar panel for irradiance sensor. AWS has two working modes, normal and maintenance mode. During maintenance mode, sensor data will be displayed on a local website that can be accessed via a wifi network broadcasted by ESP32. In normal mode, the ESP32 will send sensor data to the cloud using SIM800L GPRS Module. The system proposed is also designed to have a feature to log sensor data locally in SD card. Watchdog timer circuit uses timer 555 implemented in this project for the recovery system from the failure process. Power sources are limited, so the system must be put into deep-sleep mode state when not processing data. Data transmission is carried out periodically with an interval of 5-6 minutes. Test results show that the sending of sensor data can be received by the cloud with an acceptance rate of up to 98%.

The fundamental aim of this research is to develop a face detection system for a quadcopter in order to follow the face object. This research has two main stages, namely the face detection stage and the position control system. The face detection algorithm used in this research is the haar cascade method which is run using the python and OpenCV programming languages. The algorithm worked well, getting around 16fps on a low spec computer without a GPU unit. The results of the face detection algorithm are proven to be able to recognize faces from the camera installed on the DJI Tello mini drone. The mini drone was chosen because it is small and light, so it is harmless, and testing can be carried out indoors. Besides, the DJI Tello can be programmed easily using the python programming language. The drone’s position is then compared with the set point in the middle of the image to obtain errors so that control signals can be calculated for up/down, forward/backward, and right/left movements. From the testing results, the response speed that occurs in the right/left and up/down movements is less than 2 seconds, while for the forward/backward movement, it is less than 3 seconds.

This research aims to develop an algorithm for the precision vertical landing process on a quadcopter using GPS sensors and computer vision. This research consists of 2 main parts: GPS-based control used by the quadcopter to return to the home area and computer vision-based control in the form of ArUco marker detection, which is used by the quadcopter in order to land precisely. Detection of the ArUco marker that is used goes through 2 stages: the application of threshold and ratio and the process of calculating the black and white area, which can then be searched for four corners of the marker. The algorithm designed works well on the Webots simulation program with mid-computer specifications. The ArUco marker detection process is fast and reliable, so it does not overload the processor unit so that the simulation process can run well. From 30 tests with different initial positions, the average error of the landing position of the quadcopter at coordinate X is 0.02 meters, and coordinates Y is 0.03 meters. All test results show that the quadcopter can land inside the landing pad area without any parts coming out.