GNSS-based navigation systems of autonomous drone for delivering items
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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.
KeywordsDrone GNSS Navigation systems Big data
Unmanned Aerial Vehicles
The Global Positioning System
Global Navigation Satellite System
Robot Operating System
Single Shot Detector
One of the state of the art researches that have similar approach with this research is the usage of only GPS in drone navigation in the research done by . There’s also another one that uses GPS and onboard sensor in AR. Drone in the research done by . Last but no least, there’s also a research that’s not only uses GPS and onboard sensors, but also uses camera that uses Dense Optical Flow Method in the research done by . In our previous work, we have developed a drone with object detection capabilities to improve delivery system.
Geodetic coordinate system
Body coordinate system
In our previous research, we use object detection module that can detect what is in video stream by using combination of MobileNet and the Single Shot Detector (SSD) framework for fast and efficient deep learning-based method to object detection . We use Erle-Brain 3 which consists a Linux based embedded computer and an autopilot shield which we design our system on. Inside the embedded computer, there’s a ROS system and Autopilot Software installed. Research on autonomous drone usually using GPS , so we use GPS to get information of the position of the drone. To have a smooth navigational performance from autonous drone, the need of precise positional data is a must. To get a precise positional data, we need to collect a large amount of satellite data which is why we use GNSS and compass that could connect to more variations of satellites. The autopilot shield consists of sensors and other components which essential for flying a drone. Our proposed system acts as an interface between user and both Autopilot Software and sensors as described in Fig. 3.
Proposed system on ROS
Delivering items algorithm
Navigation algorithm using course-over-ground
Positional deviation from navigation algorithm
Positional deviation (m)
Average of error
Positional deviation from navigation algorithm with course-over-ground
Positional deviation (m)
Average of error
The proposed navigation algorithm successfully makes our drone fly from the start position to goal position without problem and have an acceptable deviation, with the mean of deviation in navigation algorithm without course-over-ground 1.1125 m and 2.39 m for the navigation algorithm using course-over-ground. Autonomous navigation system using GNSS module and compass succeeded in finishing a flight mission from start position to goal position nicely and has a relatively small positional deviation. The proposed navigation algorithm still has weakness, such as, this algorithm isn’t good for short distance navigation, so when the drone is nearing the goal position, drone is having a bit of difficulty to reach the goal position perfectly. One of the factors of this weakness is the mistakes from GNSS module. Other than that, the main weakness of the drone itself is wind speed, as seen in Fig. 11, drone’s trajectory will never match the ideal trajectory in a windy condition, but the proposed algorithm will ensure the drone will approach and land in the goal position. Navigation using the 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. When the drone is flying to the target position, every few seconds, our system must recalculate the trajectory between current position and target position.
From the results, we can conclude that the proposed system in this research succeeded in making drone do a simulated items delivery mission with a good navigation, and an acceptable landing position deviation. Comparing both navigation algorithm, we can conclude that navigation without course-over-ground results in better landing position deviation, the reason being the course-over-ground algorithm can’t contribute too much in terms of refining the accuracy with course-over-ground calculation at a navigation with distance of less than 1 m. The system has a few features such as altitude and speed settings. Proposed system designed to be able to interact with interactive sensors that supported items delivery, such as items sensor. This system also has an interface in the form of a mobile app that could be used relatively easy. Moreover, the system could run separately from Ground Station, so with only a mobile app and the drone, it could do an items delivery mission. Autonomous drones have potential usage in other fields, by using navigational algorithm from this research as the base for drone flight. Other than that, the navigational algorithm itself could be further enhanced and fixed using a more precise and adaptive algorithm that could navigate from any distance and for any usages. For future work, we need to address some issue regarding larger drone fleet and handle larger amount of navigation data to manage drone traffic. In other side, by using a bigger drone could give opportunity to do researches with a bigger scale experiments, such as delivering items with distance more than 1 km.
We say thanks to Bina Nusantara University for supporting this research.
All authors read and approved the final manuscript.
Availability of data and materials
The authors declare that they have no competing interests.
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