Hiwonder JetAuto ROS robot car powered by Jetson Nano with LiDAR for SLAM mapping and navigation (Starter Kit/SLAMTEC A1 LiDAR)

Note: This is the JetAuto starter kit equipped with SLAMTEC A1 LiDAR. There are six different versions available. Please refer to the following instructions for other versions of JetAuto

Supported by NVIDIA Jetson Nano and based on ROS

Optional depth camera for 3D visual mapping and navigation

Optional 7-inch touch screen for parameter monitoring and debugging

Optional 6 microphone arrays for voice interaction

Product Description:

JetAuto is an entry-level ROS educational robot supported by Jetson Nano. JetAuto is equipped with a laser radar, depth camera, and 7-inch screen, providing various functions such as robot motion control, map drawing and navigation, and human feature recognition.

1) 360 ° all-round movement

JetAuto is equipped with four universal Mecanum wheels, which can move 360 °, with multiple modes of movement (forward, horizontal, diagonal, rotation) and excellent performance, allowing it to challenge various complex routes.

2) Equipped with LiDAR and supporting SLAM mapping navigation

JetAuto is equipped with LiDAR, which can achieve SLAM mapping and navigation, supporting path planning, fixed-point navigation, and dynamic obstacle avoidance.

3) DC gear motor

It provides strong power, has a high-precision encoder, and includes a protective end shell to ensure extended service life.

4) 7-inch high-definition LCD touch screen

The screen resolution is 1024 x 600 pixels, compatible with NVIDIA, allowing you to freely monitor and debug various parameters of the robot.

5) 240 ° high-performance gimbal

JetAuto's high-precision pendulum suspension structure can balance the forces acting on the four wheels, allowing it to adapt well to uneven road surfaces.

6) 6-channel far-field microphone array

The 6-channel microphone array and speakers support functions such as sound source localization, voice recognition control, and voice navigation.

1. Lidar mapping and navigation

JetAuto is equipped with LiDAR, which supports path planning, fixed-point navigation, navigation obstacle avoidance, and multiple algorithm mapping, achieving radar guarding and tracking functions.

1) Lidar positioning

By combining the independently developed Lidar high-precision encoder and IMU accelerometer sensor data, JetAuto can achieve precise surveying and navigation.

2) Various 2D LiDAR surveying methods

JetAuto adopts various mapping algorithms such as Gmapping, HectorKarto, and Cartographer, and supports functions such as path planning, fixed-point navigation, and navigation obstacle avoidance.

3) Multi point navigation, TEB path planning

JetAtuo uses LiDAR to detect the surrounding environment and supports robot applications such as fixed-point navigation and multi-point continuous navigation.

4) RRT autonomous exploration map

JetAuto adopts the RRT algorithm, which can independently complete exploration and mapping, save maps, and return to the starting point without manual control.

5) Dynamic obstacle avoidance

Support TEB path planning, monitor obstacles in real-time during navigation, and re plan the route to avoid obstacles and continue driving.

6) Lidar tracking

Lidar enables robots to track targets by scanning moving objects ahead.

2. 3D Vision AI upgrade interaction

JetAuto is equipped with a 3D depth camera, which supports 3D visual mapping and navigation, and can obtain 3D point cloud images. Through deep learning, more AI visual interactive gameplay can be achieved.

1) 3D depth camera

JeAuto is equipped with Astra Pro Plus depth camera, which can effectively perceive environmental changes and achieve intelligent AI interaction with humans.

2) RTAB-VSLAM 3D Visual Mapping and Navigation

JetAuto utilizes the RTAB SLAM algorithm to create 3D color maps, enabling navigation and obstacle avoidance in 3D environments, and supporting global positioning within the map.

3）ORBSLAM2 + ORBSLAM3

ORB-SLAM is an open-source SLAM framework for monocular, binocular, and RGB-D cameras, capable of real-time calculation of camera trajectories and reconstruction of 3D surrounding environments, and obtaining the true size of objects in RGB-D mode.

4) Depth map data, point cloud

Through the corresponding API, JetAuto can obtain depth maps, color images, and point clouds of the camera.

3. Deep learning and autonomous driving

Through JetAuto, you can design an autonomous driving scenario to put ROS into practice, allowing you to better understand the core functions of autonomous driving.

1) Road sign detection

By training a deep learning model library, JetAuto can achieve autonomous driving based on Al vision.

2) Lane keeping

JetAuto can recognize both lanes and maintain a safe distance from them.

3) Automatic parking

Combined with deep learning algorithms, JetAuto can recognize parking signs and automatically drive into parking spaces.

4) Transform decision-making

JetAuto will determine traffic conditions and decide whether to turn based on lanes, road signs, and traffic signals.

4. MediaPipe development, AI interaction upgrade

JetAuto utilizes the MediaPipe development framework to implement functions such as human recognition, fingertip recognition, face detection, and 3D detection.

1) Fingertip trajectory recognition

2) Human body recognition

3) 3D inspection

4) 3D face detection

5. AI visual interaction

By integrating artificial intelligence, JetAuto can achieve KCF target tracking, route tracking, color/label recognition and tracking, YOLO object recognition, and more.

1) KCF target tracking:

By relying on the KCF filtering algorithm, robots can track selected targets.

2) Eye tracking:

JetAuto supports custom color selection, and robots can recognize color lines and follow them.

3) Color/label recognition and tracking

JetAuto can recognize and track specified colors, and can simultaneously recognize multiple April labels and their coordinates.

4) YOLO object recognition

Utilize YOLO network algorithm and deep learning model library for object recognition.

6. 6CH far-field microphone array

This 6CH far-field microphone array excels in far-field sound source localization, speech recognition, and speech interaction. Compared with ordinary microphone modules, it can achieve more advanced functions.

1) Sound source localization:

By using a 6-microphone array, high-precision positioning of noise reduction sources can be achieved, and with radar distance recognition, Hiwonder can be summoned from any location.

2) TTS voice broadcast

The text content released by ROS can be directly converted into voice broadcasts for easy interaction design.

3) Voice interaction

Combining speech recognition with TTS voice broadcasting to achieve voice interaction and support the expansion of iFlytek's online voice dialogue function.

4) Voice navigation

Use voice commands to control Hiwonder to reach any designated location on the map, similar to the voice control scenario of a food delivery robot.

7. Formation of Interconnection

JetAuto can achieve multi aircraft formation performances and artificial intelligence games through multi aircraft communication and navigation technology.

1) Multi car navigation

JetAuto relies on multi machine communication to achieve multi vehicle navigation, path planning, and intelligent obstacle avoidance.

2) Intelligent formation

JetAuto batches can maintain formation during movement, including horizontal lines, vertical lines, and triangles.

3) Group control

Only one wireless controller is needed to control a set of JetAuto, executing actions uniformly and simultaneously

8. ROS Robot Operating System

ROS is an open-source robot meta operating system that provides basic services such as hardware abstraction, underlying device control, commonly used function implementation, inter process message passing, package management, as well as tools and library functions required for cross computer code retrieval, compilation, writing, and execution. Its aim is to provide code reuse support for robot development.

9. Pavilion simulation

JetAuto is built on the Robot Operating System (ROS) and integrated with Gazebo simulation. This makes it possible to easily control robots in simulated environments, which helps with algorithm pre validation to prevent potential errors. Gazebo provides visual data that allows you to observe the motion trajectory of each endpoint and center. This visual feedback helps to enhance the algorithm.

1) Vehicle simulation control:

Through robot simulation control, algorithm validation for mapping and navigation can be carried out, improving the iteration speed of the algorithm and reducing the cost of repeated experiments.

2) Rviz showcases URDF model

Provide accurate URDF models and observe the mapping and navigation effects through Rviz visualization tools for easy debugging and algorithm improvement.

10. Multiple control methods

1）WonderAi APP

2) Map navigation application (Android only)

3) Wireless controller