to mark a step as 【Advanced AI Vision with ROS Starter Kit】
Transbot SE Tank Robot Kit is developed based on the ROS system, programmed using Python and C++, designed for AI artificial intelligence projects. It enables various AI vision recognition and visual control internet operations, suitable for beginners in ROS and AI vision advancements for Jetson Nano and Raspberry Pi projects. For those who wish to delve deeper into ROS, we highly recommend Yahboom Transbot.
【High-quality Aluminum Alloy Off-road Chassis Robot Car】
By following the installation video in the details, you will obtain a desktop-level tank track robot equipped with a 3-degree-of-freedom robotic arm and a 2-DOF camera gimbal. The versatile expansion board allows for deep development. The 4400mAh rechargeable battery, combined with 520 reduction encoder motors, provides ample power for the car.
【AI Learning Framework】
Implements OpenCV image processing, human feature, QR/AR recognition, visual tracking of faces and objects, MediaPipe machine learning, and more using an AI camera. Achieve gesture-controlled manipulation and explore other interesting project activities by camera with the robotic arm. Intelligent servo robotic arm allows for research on projects such as Movelt simulation and Cartesian path planning.Transbot SE is a fully functional,cost-effective project development kit.
【Multiple Remote Control Options】
Transbot SE tank robot can be controlled using Yahboom's app for an excellent control experience, or via a gamepad for superior handling. It can also be programmed and controlled through Jupyter web interface. Multiple control methods can be used to achieve multi-vehicle formation control.
【Technical Documentation Provide】
Transbot SE offers two versions to choose from, compatible with Jetson Nano and Raspberry Pi. Yahboom provides comprehensive documentation and technical support for this DIY product.
The car was not assembled before shipment, so we need to complete the installation according to the instructions or assembly video.
Transbot SE Tutorial Link: http://www.yahboom.net/study/Transbot-SE
Check installation video on our website. As shown below.
Shipping list:
- Transbot SE Frame *1
- Transbot SE Top Plate *1
- Driven wheel *4 + bearing wheel *16
- Driven shaft *2 + load-bearing column *8
- Driving wheel *2
- Transbot SE Track *2
- 3DOF robotic arm *1
- 2DOF camera PTZ *1
- Expansion board *1
- 520 motor with encoder *2
- Cable tie *1
- Several cables
- Battery pack acrylic board *1
- Screwdriver *1
- Battery pack *1
- Battery pack charger *1
- Parts package *1
- Handle+AAA battery *1
- Instructions manual *1
- Transbot SE coupling *2
- OLED display screen+acrylic protective board+wire
- L-type camera cable *1
- Velcro *1
There are two version for choice
JETSON NANO version
- Jetson nano B01(optional) *1
- U disk *1
- M.2 wireless network cards+antennas *1
- Cooling fan *1
- Parts Package (NANO) *1
Raspberry Pi Version
- Raspberry Pi 4B (optional) *1
- RGB cooling HAT *1
- Card reader *1
- TF card *1
- Parts Package (PI) *1
1. This small car adopts a white aluminum alloy structure as a whole, with a three degree of freedom robotic arm.
2. The 520 motor is paired with two black tracks.
After testing, the car can walk on different ground surfaces. Possess certain off-road capabilities.
1. Transbot SE adopt 1:56 205 ± 10rpm 520 high-power motor; 12.6V 4400mAH battery pack with longer battery life, sufficient for users to use; And multifunctional expansion board connects all the parts of the car together, assisting the car in completing a series of functions.
2. Transbot SE 2DOF camera PTZ consists of two servos and a 2MP camera module, supporting real-time transmission of all-round video images.
3. The three degree of freedom robotic arm consists of a 6KG bus servo and two 15KG bus servo, which can grip and transport objects.
Autonomous driving
Use the camera module to obtain real-time images, and analyze the scene through image processing and computer vision Analysis of algorithms to identify key elements such as roads, obstacles, traffic signs, etc.
Object handling
Three degree of freedom robotic arm can freely grip any object, and the crawler type setting of the car allows the car to achieve climbing function.
Color recognition+tracking
Acquire image -- Use the camera module to obtain real-time image data.
Image preprocessing -- Preprocess the obtained images, including adjusting brightness, contrast, noise reduction, and other operations to improve the accuracy of color recognition.
Convert Color Space -- Convert an image from RGB color space to HSV color space. Make color recognition more convenient and accurate.
Set color range -- Set the corresponding HSV color range based on the desired color to be recognized. Determine the range of colors of interest by adjusting the upper and lower limits of hue, saturation, and brightness.
Color segmentation -- Compare each pixel in the image with the set color range, mark the eligible pixels as the target color, and mark the other pixels as the background.
Target detection -- Process the marked color area through morphological operations such as corrosion and dilation to eliminate noise and connect the target area. This can obtain the position and shape of the identified target.
The tracked car supports multiple programming languages, including Python and C++. By writing code, we can achieve precise control, such as forward, backward, turn, and various custom actions. This provides users with a vast creative space to customize the behavior of the car according to their own needs.
Yahboom provides many open source courses for users to reference.
Tutorials link: http://www.yahboom.net/study/Transbot-SE
Viewpoint
Transbot SE is an ideal educational tool that can stimulate students' interest in science and technology. Through programming and practice, students can understand the working principles and control methods of robots, cultivate problem-solving abilities and innovative thinking. At the same time, the rich functions also provide teachers with rich teaching resources and cases.
In addition, it is also an ideal experimental platform for professional developers to conduct robot research and innovation. In addition, it can also be used in practical scenarios such as smart home control, patrol monitoring, and environmental monitoring.