Design and Development of Sensor Data Handling Controller for Modern Robotics
Rajesh Kannan Megalingam, Sakthiprasad Kuttankulungara Manoharan, A Saju
- Year
- 2024
- Citations
- 2
Abstract
This paper presents the design and development of a sensor controller, focusing on efficient data handling for integrated sensors utilized in robotic applications. In this study, we describe an extensive analysis of data collected with various sensors using multiple communication interfaces, such as USB Serial, I2C, and UART, which are used to transmit control commands and sensor data. A Teensy 4.1 microcontroller serves as the sensor controller in this system, enabling communication between the sensor controller and the system master controller. The study provides comprehensive environmental perception and control in robotics with the integration of ultrasonic sensors, current sensors, IMU, and hall sensors. To guarantee dependable sensor data acquisition and system functionality, the testing approach evaluates both manual and protocol-based communication for each sensor. This work demonstrates the simultaneous data reception made possible by the integration of several sensors with different protocols into a single microcontroller. The process of evaluating accuracy required contrasting human measurements with protocol-based communication for a range of sensors, including IMUs (orientation detection), current sensors (current flow measurement), and ultrasonic sensors (distance measurement). The integrated sensor system was put to the test using a variety of measuring techniques, including software-based and manual protocols, as part of the validation process. The precision of individual sensors is demonstrated by the results, which validate the correctness of the data acquired. The results of the experiment provide a detailed comparison between data provided by software and manual measurements, confirming the dependability and efficiency of the integrated sensor system. From the experiment results it is clear that the accuracy of the system is above 90%.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991