Determining the most suitable control algorithm for a system is not an easy task. In theory, each controller has its own advantages and disadvantages comparing to the others. However, in the real world, the behavior of the controller also depends on many other factors such as the calculating ability of the control board, the accuracy of the sensors, the way the hardware communicate with the others, etc. In order to find the pros and cons of each control algorithm in the real world, each of them has to be tested and then comparing their results. This article presents a simple way to test the behavior of various control algorithms, with the quadrotor as the control target and ArduPilot is the framework to create the firmware carrying multi controllers. At the end of this article, the results of 3 control algorithms: Original PID of ArduPilot, new developed PID and Integral Backstepping will be presented and compared. These data is created by using Software In The Loop simulation (SITL), a tool provided by ArduPilot to test the new developed firmware.
The paper presents preliminary research results about implementing an object detection program on a Single Board Computer. These results are used later to develop applications for drones. The object identification program is developed in Python using the TensorFlow library. The authors have succeeded in implementing and testing this object identification module using the artificial neural network model SSDMobileNet V2 on the Raspberry Pi 3B+. The results in this paper demonstrate the potential of this module for further development in the future. Based on the simulation and real-world results, the authors showed that a good outcome is achievable with limited resources for the AI module. Along with a high-precision object detection feature, this module can also estimate the distance and velocity of the “human” object with good accuracy. Besides, the paper also proposes several solutions to increase the performance and most importantly, the real-time feature of the developed module.
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