- Powered by NVIDIA Jetson Nano and based on the Robot Operating System (ROS)
- Supports a depth camera and Lidar for mapping and navigation
- 7 inch touch screen for parameter monitoring and debugging
- 6 microphone array for voice interaction
- Three versions available: JetAuto Starter Kit (with Lidar), JetAuto Standard Kit (with Lidar and Depth Camera), JetAuto Advanced Kit (with Lidar, Depth Camera, LCD Screen, and 6 microphone Array)
The Hiwonder Jetauto ROS Robot Car w/ Jetson Nano, Lidar Depth Camera & Screen (Advanced Kit)is equipped with NVIDIA Jetson Nano, a high performance encoder motor, a rotatable pan tilt, a Lidar, a 3D depth camera, and a 7 inch screen, which opens up a variety of functionalities. Robot motion control, mapping and navigation, path planning, tracking and obstacle avoidance, autonomous driving, human feature recognition, somatosensory interaction and voice interaction can all be achieved!
The diverse combination of hardware makes JetAuto an ideal platform to learn and verify robotic SLAM functions, as well as to get a solution for ROS development. A wide range of ROS learning materials and tutorials are provided to help you get started quickly!
NVIDIA Jetson Nano Control System:
NVIDIA Jetson Nano is able to run mainstream deep learning frameworks, such as TensorFlow, PyTorch, Caffe/ Caffe2, Keras, and MXNet, providing powerful computing power for massive AI projects. Powered by Jetson Nano, JetAuto can implement image recognition, object detection and positioning, pose estimation, semantic segmentation, and intelligent analysis, among other powerful functions.
Lidar Functions:
2D Lidar Mapping and Navigation: JetAuto is equipped with a high performance Lidar that supports mapping with various algorithms, including Gmapping, Hector, Karto, and Cartographer. Additionally, path planning, fixed point navigation, and obstacle avoidance during navigation can be implemented.
Single Point Navigation, Multi Point Navigation: JetAuto employs Lidar to detect the surroundings in real time to achieve single point navigation as well as multi point navigation.
TEB Path Planning, Obstacle Avoidance: It supports TEB path planning, and is able to monitor obstacles in real time during navigation. As a result, it is able to replan the route to avoid the obstacle and continue moving.
RRT Autonomous Exploration Mapping: Adopting the RRT algorithm, JetAuto can complete exploration mapping, save the map, and drive back to the starting point autonomously, so there is no need for manual control.
Lidar Tracking: By scanning the front moving object, Lidar enables the robot to track targets.
Lidar Guarding: It can guard the surroundings and sound an alarm when detecting intruders.
Depth Camera:
RTAB VSLAM 3D Mapping and Navigation: The depth camera supports 3D mapping in two ways, pure RTAB vision and fusion of vision and Lidar, which allows JetAuto to navigate and avoid obstacles in 3D maps, as well as to relocate globally.
ORBSLAM2 + ORBSLAM3: ORB SLAM is an open source SLAM framework for monocular, binocular, and RGB D cameras, which is capable of computing the camera trajectory in real time and reconstructing 3D surroundings. Under RGB D mode, the real dimension of the object can be acquired.
Depth Map Data, Point Cloud: Through the corresponding API, JetAuto can get depth map, color image, and point cloud of the camera.
Deep Learning, Autonomous Driving:
With JetAuto, you can design an autonomous driving scenario to put ROS into practice, which enables you to better understand the core functions of autonomous driving.
Road Sign Detection: Through training the deep learning model library, JetAuto can realize autonomous driving with AI vision.
Lane Keeping: JetAuto is able to recognize the lanes on both sides to maintain a safe distance between it and the lanes.
Automatic Parking: By combining a deep learning algorithm, JetAuto can recognize the parking sign, then steer itself into the slot automatically.
Turning Decision Making: According to the lanes, road signs, and traffic lights, JetAuto will estimate the traffic and decide whether to turn.
MediaPipe Development, Upgraded AI Interaction:
Based on the MediaPipe framework, JetAuto can carry out human body recognition, fingertip detection, face detection, 3D detection, and more.
Fingertip Trajectory Recognition
Human Body Recognition
3D Detection
3D Face Detection
AI Deep Learning Framework: Utilizing the YOLO network algorithm and deep learning model library, JetAuto can recognize objects.
KCF Target Tracking: Relying on the KCF filtering algorithm, the robot can track the selected target.
Color/Tag Recognition and Tracking: JetAuto is able to recognize and track the designated color, and can recognize multiple April Tags and their coordinates.
Augmented Reality (AR): After selecting the patterns on the APP, the patterns can be overlaid on the April Tag.
Far Field Microphone Array:
This 6 microphone array is adept at far field sound source localization, voice recognition, and voice interaction. Compared to an ordinary microphone module, it can implement more advanced functions.
Sound Source Localization
Voice Interaction
Voice Navigation
Interconnected Motorcade:
Thanks to multi machine communication, JetAuto can achieve multi vehicle navigation, path planning, and smart obstacle avoidance.
Intelligent Formation: A batch of JetAuto cars can maintain the formation, including horizontal line, vertical line, and triangle, during movement.
Group Control: A group of JetAuto can be controlled by only one wireless handle to perform actions uniformly and simultaneously.
ROS Robot Operating System:
ROS is an open source meta operating system for robots. It provides some basic services, such as hardware abstraction, low level device control, implementation of commonly used functionality, message passing between processes, and package management. It also offers the tools and library functions needed to obtain, compile, write, and run code across computers. It is designed to provide code reuse support for robotics research and development.
Gazebo Simulation:
JetAuto employs the ROS framework and supports Gazebo simulation. Gazebo provides a novel approach to control JetAuto and verify the algorithm in a simulated environment, which reduces experimental requirements and improves efficiency.
JetAuto Simulation Control: The kinematics algorithm can be verified in simulation to speed up algorithm iteration and reduce the experiment cost.
Visual Data: rviz can visualize the mapping and navigation result, which facilitates debugging and improving the algorithm.
Various Control Methods:
1) WonderAi APP
2) Map Nav APP (Android Only)
3) Wireless Handle
