Using a Combination of PID Control and Kalman Filter to Design of IoT-based Telepresence Self-balancing Robots during COVID-19 Pandemic

COVID-19 is a very dangerous respiratory disease that can spread quickly through the air. Doctors, nurses, and medical personnel need protective clothing and are very careful in treating COVID-19 patients to avoid getting infected with the COVID-19 virus. Hence, a medical telepresence robot, which resembles a humanoid robot, is necessary to treat COVID-19 patients. The proposed self-balancing COVID-19 medical telepresence robot is a medical robot that handles COVID-19 patients, which resembles a stand-alone humanoid soccer robot with two wheels that can maneuver freely in hospital hallways. The proposed robot design has some control problems; it requires steady body positioning and is subjected to disturbance. A control method that functions to find the stability value such that the system response can reach the set-point is required to control the robot's stability and repel disturbances; this is known as disturbance rejection control. This study aimed to control the robot using a combination of Proportional-Integral-Derivative (PID) control and a Kalman filter. Mathematical equations were required to obtain a model of the robot's characteristics. The state-space model was derived from the self-balancing robot's mathematical equation. Since a PID control technique was used to keep the robot balanced, this state-space model was converted into a transfer function model. The second Ziegler-Nichols's rule oscillation method was used to tune the PID parameters. The values of the amplifier constants obtained were Kp=31.002, Ki=5.167, and Kd=125.992128. The robot was designed to be able to maintain its balance for more than one hour by using constant tuning, even when an external disturbance is applied to it. Doi: 10.28991/esj-2021-SP1-016 Full Text: PD

Optimisation of camera positions for optical coordinate measurement based on visible point analysis

In optical coordinate measurement using cameras, the number of images, and positions and orientations of the cameras, are critical to object accessibility and the accuracy of a measurement. In this paper, we propose a technique to optimise the number of cameras and the positions of these cameras for the measurement of a given object using visible point analysis of the object's computer aided design data. The visible point analysis technique is based on a hidden point removal approach; this technique is used to detect which surface points on the object are visible from a given camera position. A genetic algorithm is used to find the set of positions that provide optimum surface point density and overlap between views, while minimising the total number of camera images required. The genetic algorithm is used to minimise the measurement data processing time while maintaining optimum surface point density. We test this optimisation procedure on four artefacts and the measurements are shown to be comparable to that from a traceable contact co-ordinate measurement machine. We show that using our procedure improves the measurement quality compared to the more conventional approach of using equally spaced images. This work is part of a larger effort to fully automate and optimise optical coordinate measurement techniques

Real-Time View Planning for Unstructured Lumigraph Modeling

https://stars.library.ucf.edu/cfm-ch-register-vol22/1602/thumbnail.jp