Trajectory Tracking Control of UAV Bicopter using Linear Quadratic Gaussian

This paper describes the design of a linear quadratic gaussian (LQG) for trajectory tracking control of UAV Bicopter. In this work, disturbance in the form of payload significantly affects the trajectory tracking control process on the UAV Bicopter when using a linear quadratic regulator (LQR) controller. The use of a LQR control will be optimal in the case of a state regulator towards an equilibrium point in a system, but for the tracking case, the LQR controller is not capable of optimally, especially in systems that have high levels of nonlinearity and system dynamic changes such as inertial disturbances. Therefore, this paper proposes the design of a LQG control that is expected to overcome system dynamic changes, in this case in the form of inertial disturbances to the UAV Bicopter when carrying a payload. The success of LQG control was tested in two scenarios, the first trajectory tracking at a circular position and the second with the position of the trajectory number "8". The simulation results show that the proposed LQG controller successfully overcame inertial disturbances when the UAV Bicopter performs trajectory tracking. When given an inertial disturbance, the trajectory tracking test results show that the LQG control has a lower root mean square error (RMSE) value than the LQR control.

Attitude Control and Low Cost Design of UAV Bicopter

This paper present a control system for the attitude and low cost design of a Bicopter. The control system uses a PID controller that receives feedback from an IMU to calculate control inputs that adjust the Bicopters attitude (roll, pitch and yaw angles) which is resistant to disturbances (wind noise) on a test bed. The control system is implemented on a hardware platform consisting of a Bicopter, an IMU sensor, and a microcontroller with low cost design. In mechanical design, the Bicopter is designed to more closely resemble the letter "V" so that the distribution of the centre of mass (CoM) of the Bicopter can be such that the servomotor torque reaction is parallel to the axis of rotation of the Bicopter during the movement of the pitch angle attitude. In electronic design, the Bicopter was developed using the ATmega328P microcontroller.

PlasmoID: A dataset for Indonesian malaria parasite detection and segmentation in thin blood smear

Indonesia holds the second-highest-ranking country for the highest number of malaria cases in Southeast Asia. A different malaria parasite semantic segmentation technique based on a deep learning approach is an alternative to reduce the limitations of traditional methods. However, the main problem of the semantic segmentation technique is raised since large parasites are dominant, and the tiny parasites are suppressed. In addition, the amount and variance of data are important influences in establishing their models. In this study, we conduct two contributions. First, we collect 559 microscopic images containing 691 malaria parasites of thin blood smears. The dataset is named PlasmoID, and most data comes from rural Indonesia. PlasmoID also provides ground truth for parasite detection and segmentation purposes. Second, this study proposes a malaria parasite segmentation and detection scheme by combining Faster RCNN and a semantic segmentation technique. The proposed scheme has been evaluated on the PlasmoID dataset. It has been compared with recent studies of semantic segmentation techniques, namely UNet, ResFCN-18, DeepLabV3, DeepLabV3plus and ResUNet-18. The result shows that our proposed scheme can improve the segmentation and detection of malaria parasite performance compared to original semantic segmentation techniques.