Zevon EduBot
  • ROS2 Robot Ackerman Mecanum Wheel SLAM Mapping Navigation 3D Vision Programming Cart Jetson
  • ROS2 Robot Ackerman Mecanum Wheel SLAM Mapping Navigation 3D Vision Programming Cart Jetson
  • ROS2 Robot Ackerman Mecanum Wheel SLAM Mapping Navigation 3D Vision Programming Cart Jetson
  • ROS2 Robot Ackerman Mecanum Wheel SLAM Mapping Navigation 3D Vision Programming Cart Jetson
  • ROS2 Robot Ackerman Mecanum Wheel SLAM Mapping Navigation 3D Vision Programming Cart Jetson
  • ROS2 Robot Ackerman Mecanum Wheel SLAM Mapping Navigation 3D Vision Programming Cart Jetson
  • ROS2 Robot Ackerman Mecanum Wheel SLAM Mapping Navigation 3D Vision Programming Cart Jetson
  • ROS2 Robot Ackerman Mecanum Wheel SLAM Mapping Navigation 3D Vision Programming Cart Jetson
  • ROS2 Robot Ackerman Mecanum Wheel SLAM Mapping Navigation 3D Vision Programming Cart Jetson
  • ROS2 Robot Ackerman Mecanum Wheel SLAM Mapping Navigation 3D Vision Programming Cart Jetson

ROS2 Robot Ackerman Mecanum Wheel SLAM Mapping Navigation 3D Vision Programming Cart Jetson

Jetson Robotics Development Kit Based on ROS2: Integrating SLAM and 3D Vision, Compatible with Ackermann/Mecanum Wheels

color classification:
Ackermann body
McNamara wheel body
Tracked vehicle body
Core main control:
Jetson Nano

USD 929

output value: Monthly Output200

contact shop

ROS Educational Robot

Support ROS2/mapping navigation/depth vision/technical support

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Product Introduction

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ROSHunter is our ROS robot car developed for ROS education programming. It is equipped with a series of high-performance hardware, including NVIDIA Jetson Nano, high torque coding deceleration motor, LiDAR, 3D depth camera, 7-inch LCD display screen, cool programmable car lights, and more. These hardware configurations enable ROSHunter to develop and apply various aspects such as robot motion control, ROSSLAM algorithm, mapping navigation path planning, deep learning, and visual interaction.

ROSHunter offers a variety of chassis options to fully adapt to the ROs2 system, which not only better meets users' learning and verification needs for robot SLAM functions, but also provides a fast and convenient integration solution for ROS development. In addition, the accompanying ROS course covers rich technical materials, functional source code, course documentation, and instructional videos, helping users quickly master the development and application of ROS robots.

                 Full support for ROS2                           Map Navigation                   3D real scene mapping                Visual recognition

                     Four Wheel Plate                            Wheat Wheel Plate                   Ackermann chassis                      Crawler chassis

                        Path planning                           Multi-point navigation                      APP control                           Handle control

                        Deep learning                                     PID control                      Multi aircraft formation                Machine learning

                            OpenCV                                 Development Manual                      Video tutorial                        Technical Support


Multiple chassis options to choose from

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When exploring the world of ROS robots, one can choose from four vehicle models based on specific learning objectives and practical application scenarios. Each model is carefully designed to provide the best learning experience and application performance for users with different skill levels.

                          Ackermann                                   Mecanum wheel                        Tracked vehicle                          Differential car


① Ackermann steering chassis with flexible front wheel steering control

Ackermann steering chassis inherits the standard steering architecture of modern cars, with precise steering accuracy achieved through fine angle adjustment of the front wheels for navigation, making it particularly suitable for research with high simulation requirements for vehicle driving dynamics.

In order to optimize the stability of the four-wheel contact with the ground, the rear wheels of the chassis are equipped with a pendulum suspension system, which can adapt to uneven road surfaces and ensure the measurement accuracy of the motor encoder odometer. The all metal CNC precision manufacturing of its steering components provides durability and precision.


② Mecanum wheel chassis moves in all directions

The Mecanum wheel chassis endows the ROS robot with 360 degree omnidirectional mobility, enhancing its flexibility in narrow spaces. As an omnidirectional maneuvering structure, it particularly meets the complex application requirements of multi angle operation.

(1) Straight ahead                     (2) Side movement                      (3) Turn right


③ Tracked chassis

The tracked robot chassis, with its excellent adaptability and stability, can easily handle various complex terrains. Equipped with nylon tracks and high-performance DC motors, it ensures precise control and provides reliable support for transportation and other fields. Its flexible direction and angle adjustment optimize the learning and development experience of robots.


④ Four wheel drive chassis differential drive

The four-wheel chassis is controlled by motor differential steering, providing flexibility and easy operation, suitable for education and beginners. Equipped with 100mm diameter rubber wheels, ensuring small deformation and high mobility, simplifying mechanical structure while maintaining excellent performance.


Full support for ROS2

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ROS2 aims to become the preferred operating system widely used in various types of robots, and currently, it has become the direct choice for almost all robot companies' newly approved projects. The car will be equipped with the ROS2Galactive version, and the adaptation work for the ROS2Humble version is steadily progressing.

2020 Release

Noetic Ninjemys

The official recommended version of ROS is the LTS version of ROS1 after Zui, and no new versions will be released in the future. It is compatible with the mainstream Ubuntu 20.04, the preferred version for learning ROS, and supports mainstream Python 3. x. It has a significant advantage in terms of the number of feature packs and new additions.

ROS2 galactic
Using Cyclone DDS to enhance the stability of NAV2 and

provide multiple functional case studies


ROS (Robot Operating System)

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Global mainstream robot communication framework

ROS (Robot Operating System) is an open-source meta-operating system for robots. It provides services similar to those of an operating system, including hardware abstraction description, low-level driver management, execution of common functions, inter-process message passing, and program distribution package management. Its main goal is to provide support for code reuse in robot research and development

ROS   =   Communication mechanism   +   Development tools   +   Application Functionality   +   Ecosystem


① Lidar mapping and navigation

Can develop SLAM algorithms such as Gmapping, Karto, Hector for mapping, and support path planning, fixed-point, navigation, and multi-point navigation.


② RTAB-VSLAM 3D Visual Mapping and Navigation

Using the RTABSLAM algorithm, a 3D color map is constructed by integrating visual and radar data. The robot can autonomously navigate and avoid obstacles in the map, and supports global repositioning and autonomous positioning functions.


③ Multi point navigation dynamic obstacle avoidance

Lidar can detect the surrounding environment in real time and re plan the path after detecting obstacles.

④ Depth image data point cloud image

Through the corresponding API, depth images, color images, and point cloud data of the camera can be obtained.


⑤ MediaPipe development, machine vision processing

By using the MediaPpe development framework, functions such as face detection, edge detection, augmented reality, and color recognition can be achieved.


⑥ KCF target tracking

The image-based KCF correlation filtering algorithm can select any object in the image and achieve target tracking.

⑦ RGB programmable headlights

Ackermann, Mecanum wheels, and four-wheel bodies support 8 editable RGB headlights.


Dual brain architecture collaboration

Performance Upgrade

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JetsonNano leads ROS algorithm and visual processing, while STM32 is responsible for motion control and sensor data processing. Intelligent division of labor between the two, achieving dual improvement in performance and efficiency

① Main control board: Jetson Nano

ROS SLAM algorithm

Sensor data fusion

visual recognition

image processing

deep learning

human-computer interaction

edge computing

Multitasking

......

② Secondary control board: STM32

Motor drive feedback

Servo drive feedback

IMU processing

Sensor data collection

Encoder reading

Battery voltage detection

Peripheral device control.

.....


Ergonomic design expands horizons

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Thanks to our innovative linkage design, the display screen of the robot car is different from similar products, achieving infinite adjustment ability of screen angle and supporting free folding from 0 to 90 degrees, aiming to provide a tilted visual experience. This design provides flexible angle adjustment, making it easy to find the optimal usage angle that is more ergonomic.


ROS Easy to Use Artificial Intelligence Interaction

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The robot car is equipped with the "XR-ROS human-machine interaction system" independently developed by XiaoR Technology, which enables users to monitor the status and various information of the robot in real time. With this system, users can easily interact and manage without relying on the command line or other complex operating methods.


Operation method

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Mobile APP remote control

Support mobile app control of robots, combined with virtual machines, can achieve applications in various scenarios.

Wireless controller

Equipped with a wireless joystick as standard, it can control the movement and pan tilt operation of the robot.


Product Parameters

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Product Model

ROSHunter SLAM Autonomous Navigation and Vision Education Robot

chassis type

Ackermann chassis

Mecanum wheel chassis

Crawler chassis

Four-wheel drive chassis

Product size (mm)

294*138*223

303*249*226

326*234*229

319*256*243

Product weight

3500g

3000g

3000g

3050g

ROS main controller

Nvidia Jetson Nano 128-core NVIDIA Maxwell™ architecture GPU

Sub-control board

XR-PNL-4RL-32

LiDAR

XR-LIDAR S2

camera

DCW depth camera

motor

XR520 Permanent Magnet DC Geared Motor with Encoder

battery

10000mAh

operating system

Ubuntu20.04LTS+ROS Melodic

communication method

USB/WIFI/Ethernet

Control method

Mobile app/Wireless controller/PC

programming tools

Python /C/C++ /JavaScript

storage

64GB TF card

Body material

All-metal aluminum alloy chassis

Steering servo motor

S015M 15KG Metal Shaft Servo (Ackerman Version)

headlight

8 RGB LED beads


① Depth cameras are standard across the entire range

Binocular imaging can provide high-precision depth images within 0.2-2.5m. The camera adopts a modular design and integrates a depth computing platform

DCW Depth Camera

Baseline

40 mm

Data transmission

USB2.0 Type-C

Working distance

0.2m-2.5m

Power consumption

Average power consumption < 2.3W

Depth accuracy

1.0%@1m

Power supply method

Type-C

Depth image

Supports up to 1024x768 resolution

Applicable scenarios

Indoor

Depth FOV

H79° V55°D88.5°+3°

Safety

Class 1 laser


② Laser radar

LIDARS2 laser radar is equipped with a high-speed image processing chip, which can perform 360 degree all-round laser ranging scanning within a radius range

XR-LIDAR S2 LiDAR

Working Voltage

5V

Scanning angle

360°

Operating current

250mA

Output interface

UART serial port

Laser Safety

Class I

Operating Temperature

-10℃~+40℃

Measure distance

150mm-6000mm

Pitch angle

0°~1.5°

Angular resolution

≤1°

Laser light source

780nm LD


③ Standard 7-inch display screen

Foldable 90 degree display screen

90° foldable 7-inch high-definition touch LCD screen

Display screen size

7 inch

Operating Temperature

-20℃~ + 70℃

Maximum folding angle

90°

Brightness

500LCM

Use

Windows/Linux

Size

165*110*20mm

Resolution

1024*600PX

Weight

≤266g


④ High performance two degree of freedom gimbal

S370 High Voltage Bus Serial Servo

High-performance two-degree-of-freedom gimbal

Servo model

S370 high-voltage bus serial servo

Locked-rotor torque

30kg.cm

Control method

UART serial port command

Readback function

Support angle readback

Baud rate

One hundred and fifteen thousand and two hundred

Control algorithm

PID

Protect

Stall protection/overheat protection, etc

Servo ID

Power-off protection for user settings from 0 to 253

Electronic resolution

0.088°

Weight

≈55g


 Ackermann vehicle size diagram 


 Dimensional drawing of four-wheel differential vehicle body 


 Dimensional drawing of tracked vehicle body 


 Dimensional drawing of wheeled vehicle body 


Shipping List

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Robot assembly and debugging completed, finished product shipped

                          Robot machine                               Remote control handle            Charger                 Card reader         Warranty Card


Optional robot specific aviation aluminum box

Optional robot aviation aluminum box for shipment, the box body is not easily deformed, sturdy and durable, ensuring safety and protection during long-distance transportation. If needed, please contact customer service for price difference and optional selection


Our services

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About express delivery
If there is a specific express delivery requirement, please contact customer service to confirm the shipping cost.

Gift materials
The product provides supporting information, which can be provided by contacting customer service after arrival.

Technical Support
Support technical support. If you have any functional questions, you can join the technical communication group

Our goal is to continuously improve our own technical capabilities and place greater emphasis on the customer's product experience.

Supplier Information

Email:ZevonEduBot@ttbridge.com

Tel:17734786008

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