Multi-Sensors for Robot Teaming Using Raspberry Pi and VEX Robotics Construction Kit
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American Society for Engineering Education, 2018. The paper presents an engineering design approach to develop an instructional module for college students to learn Microprocessors and Robotics using multiple sensors, microprocessors and software design tools. The module consists of research analysis, lesson content development and laboratory practice selection, which satisfies the ABET (Accreditation Board for Engineering & Technology) requirement for engineering education. The research analysis covers the work reported by the scholars from MIT and other universities [1] [2], where the main concern is how to enhance students' capability in developing engineering products using new technologies in the industry, such as multiple sensor fusion methodology as well as working skills on cross-platform hardware (Intel, ARM, AMD and PIC, etc.) and software (Linux, Windows and Androids, etc.). After the research paper analysis, a class activity has been developed. The activity includes teaching students to build teaming robots by combining the Cortex controllers with ROBOTC programming environment under Windows and the Raspberry Pi (in ARM cores) using Python under Linux and guiding students to develop a cross-platform software and hardware design using PIC Microcontrollers with embedded C under Windows and the Raspberry Pi with Python under Linux. The teaming robots under development contains a leading robot and a tracking robot. The leading robot in the front can turn in any direction. When it "sees" an obstacle ahead using an ultrasonic sensor, it will turn left or right to avoid the obstacle. The tracking robot has a Cortex controller to drive the motors and an ultrasonic range finder to detect the distance between it and the leading robot. At the same time, the tracking robot also holds a Raspberry Pi board with a Pi Camera on it. The image signal obtained from the Pi Camera is processed by the Raspberry Pi and sent to the Cortex controller. Based on the processed image signal, the second robot can follow the first robot to turn accordingly, thus, they make a robot team. The exercise package contains a PIC microcontroller with a speaker and the Raspberry Pi with Pi Camera. If the Pi Camera finds some motion around, a signal will be sent to the PIC microcontroller and the siren sound will be emitted. The activities largely aroused students' curiosity and attention. The survey result is very encouraging. After the class, students' enthusiasm in learning Python, image processing and engineering design using multiple boards and programming languages as well as the applications has been noticeably enhanced.
