This paper presents our research on the development of navigation systems of autonomous drone for delivering items that uses a GNSS (Global Navigation Satellite System) and a compass as the main tools in drone. The grand purpose of our research is to deliver important medical aids for patients in emergency situations and implementation in agriculture in Indonesia, as part of the big mission of Society 5.0 and related with big data. In sending process, drone must be able to detect object and reach goal position and go back safely using GPS. We proposed a navigation algorithm for drone including the usage of course-over-ground information to help drone in doing autonomous navigation. In the experiment that we did, the average of positional deviation of landing position between the actual landing position and the desired landing position in the flight tests of flying from start to goal is 1.1125 m and for the tests that use the algorithm which uses course-over-ground, the positional deviation has average of 2.39 m. Navigation using course-over-Ground algorithm is not more reliable than the navigation algorithm with GNSS and Compass at a navigation distance of less than 1 m.
The paper presents our research progress in the development of object detection using deep learning based on drone camera. The grand purpose of our research is to deliver important medical aids for patients in emergency situations. The case can be simplified into delivery of an item from start to the goal position. We will exploit the drone technology for transporting items efficiently. In sending process, our drone must detect the object target, where the items will be delivered. Therefore, we need object detection module that can detect what is in video stream and where the object is by using GPS as well. To implement the module, we use combination of MobileNet and the Single Shot Detector (SSD) framework for fast and efficient deep learning-based method to object detection. The ability of deep learning to detect and localize specific objects is studied by conducting experiments using drone camera and, as comparison, using stereo camera Minoru.
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