ANALYSIS OF EFFICIENCY OF SENSOR SIGNAL RECOGNITION AND FILTRATION FOR MOBILE ROBOT MOTION CONTROL ON RUSSIAN PLATFORM TRACKDUINO
Authors:
1.
Kazan National Research Technical University named after A.N. Tupolev
Type:
Article
Pages:
from 147
to 152
Status:
In work
Language:
Russian
Keywords:
DIGITAL FILTERING, KALMAN FILTER, MEDIAN FILTER, ULTRASONIC SENSOR, INFRARED SENSOR, POSITION SENSOR
Abstract (English):
This study is devoted to the comparative evaluation of the digital filters for filtering signals received from sensors. Sensory information about the location of mobile robots, environmental objects, including possible obstacles, its overall 3D picture is the basis for constructing a model of the surrounding space, choosing the direction and trajectory of movement in accordance with the tasks performed, which is why it is extremely important to receive a high-quality signal from a sensor device. From an engineering point of view, it makes sense to classify sensor devices by the type of their output signals. This criterion is important for connecting the sensor to the controller via the interface. The objective of this study was to ensure the trajectory of movement of a mobile robot moving in a limited space and controlled by a control system receiving information from sensor devices. For this, the following steps were performed: a stable signal from the sensors was received, graphs of the original sensor signals were obtained using the Arduino IDE software, preliminary signal processing (filtering) was carried out, the processed signals were transmitted to the controller. The following sensor devices were used: an ultrasonic sensor for determining the distance of obstacles, two infrared sensors for determining the boundary of the trajectory of movement, and a position sensor for orienting the robot in space. This article presents the results of signal filtering using the Kalman filter and the median filter. A program code in C++ was developed for their implementation. The article considers the main methods of noise suppression and the features of digital filters. As a result of the study, the features of using a nonlinear median filter and a Kalman filter were determined.
This study is devoted to the comparative evaluation of the digital filters for filtering signals received from sensors. Sensory information about the location of mobile robots, environmental objects, including possible obstacles, its overall 3D picture is the basis for constructing a model of the surrounding space, choosing the direction and trajectory of movement in accordance with the tasks performed, which is why it is extremely important to receive a high-quality signal from a sensor device. From an engineering point of view, it makes sense to classify sensor devices by the type of their output signals. This criterion is important for connecting the sensor to the controller via the interface. The objective of this study was to ensure the trajectory of movement of a mobile robot moving in a limited space and controlled by a control system receiving information from sensor devices. For this, the following steps were performed: a stable signal from the sensors was received, graphs of the original sensor signals were obtained using the Arduino IDE software, preliminary signal processing (filtering) was carried out, the processed signals were transmitted to the controller. The following sensor devices were used: an ultrasonic sensor for determining the distance of obstacles, two infrared sensors for determining the boundary of the trajectory of movement, and a position sensor for orienting the robot in space. This article presents the results of signal filtering using the Kalman filter and the median filter. A program code in C++ was developed for their implementation. The article considers the main methods of noise suppression and the features of digital filters. As a result of the study, the features of using a nonlinear median filter and a Kalman filter were determined.
Keywords:
DIGITAL FILTERING, KALMAN FILTER, MEDIAN FILTER, ULTRASONIC SENSOR, INFRARED SENSOR, POSITION SENSOR
DIGITAL FILTERING, KALMAN FILTER, MEDIAN FILTER, ULTRASONIC SENSOR, INFRARED SENSOR, POSITION SENSOR
