FS-ROBOT is a modular indoor robot learning and research platform, with self balancing intelligent vehicles as the main structure, which can complete interest based teaching and research related to self balancing intelligent vehicles. With the assistance of devices such as the Mecanum wheel, card computer (Raspberry Pi 3B), LiDAR, etc., research and teaching on the relevant knowledge points of ROS robot operating system can be completed.

FS-ROBOT mainly consists of Cortex-M4 core control board, self balancing drive board, omnidirectional drive board, sensor board, CCD sensor board, Raspberry Pi 3, LiDAR, high-definition camera, 4 DC deceleration motors, 4 McNam wheels, 2 high-end rubber tires, and aluminum alloy structural components. By selecting Cortex-M4 core board, Raspberry Pi 3B, and various combinations of sensing and control devices, teaching and research on platforms such as RTOS, OpenWRT, OpenCV, ROS, embedded, and Android (mobile app) are completed.

1. Modularization

FS-ROBOT consists of aluminum alloy connectors, drive modules, core control modules, and power modules. A complete set of intelligent robot development kits can be assembled into balance car mode or omnidirectional car mode as needed.

2. Multi platform

The underlying structure and driver of FS-ROBOT support multi platform access. Cortex-M4 version for teaching and research on ARM architecture and RTOS platform for secondary and vocational colleges; Research on OpenWRT, OpenCV, ROS platforms for embedded and IoT applications using Raspberry Pi3 version.

3. Fun and interesting

FS-ROBOT can complete research on self balancing intelligent vehicles and omnidirectional mobile platforms through the combination of different modules. For example, by selecting the structure of self balancing intelligent vehicles, the self balancing principle and driving algorithm can be studied. For higher platform applications, research on WiFi intelligent balancing vehicles, machine vision, and other topics can be completed by selecting Raspberry Pi modules. By using omnidirectional mobile platform components, research on ROS robot related topics can also be achieved, maximizing its fun.

4. Openness

The FS-ROBOT system software and hardware are both open architecture, and customers can expand and develop them according to their own needs. All code, systems, and algorithms are open source.

5. Intelligentization

With the compatibility design of mechanical structure and driving circuit, FS-ROBOT can be paired with a powerful ROS robot operating system, enabling it to complete indoor map construction, autonomous navigation, marker recognition, speech recognition and other related research topics.

6. Wide adaptability range

A complete set of routine development, guidance materials, all sensors and development documents required for intelligent robots, suitable for undergraduate and vocational teaching, and can also be used as a research platform for graduate students to conduct research projects.

1. Cortex-M4 development platform experiment

GPIO input/output experiment; SysTick system tick experiment; UART serial port data transmission and reception experiment; Universal timer counting experiment; Orthogonal code wheel input capture experiment; PWM DC motor drive experiment; A/D analog-to-digital conversion experiment; I2C bus driver experiment; SPI serial peripheral design; Direct Memory Access Experiment (DMA); FLASH data storage experiment; PID regulation control experiment; MPU6050 internal DMP firmware migration analysis; OLED display experiment; MPU6050 driver experiment; QMC5883L driving experiment; ESP-32S WiFi transparent transmission experiment; A/D analog-to-digital conversion experiment (CCD camera);

2. Raspberry Pi section

Ubuntu development environment setup; OpenWRT development environment setup experiment; OpenWRT development experiment; ROS development environment setup experiment; ROS system framework and basic concepts; ROS node subscription and message sending experiment; ROS system communicates with the chassis; Keyboard control Explorer balance car; Obtain information on the Explorer balance car; Development of two-dimensional laser sensors; Robot indoor map construction and autonomous navigation (LiDAR); OpenCV porting experiment; OpenCV tag recognition experiment; ROS system simple robot simulation

3. Android side experiment

Android development environment setup; Intelligent vehicle development process; Detailed explanation of intelligent car application development