- Supports both ROS1 and ROS2, with main control options including Raspberry Pi 5, Jetson Nano, and Jetson Orin Nano
- Powered by NVIDIA Jetson Nano, JetRover supports deep learning frameworks, MediaPipe development, and YOLO model training for advanced ROS learning
- Equipped with a 3D depth camera and Lidar, JetRover handles multi-point navigation, TEB path planning, and dynamic obstacle avoidance using advanced algorithms like gmapping, hector, and cartographer
- Featuring a 6DOF arm with a 35KG torque servo and an HD camera, JetRover excels in precise object manipulation tasks
- The ultimate kit includes a circular microphone array for voice-controlled mapping, navigation, and robot arm operation
Product Description
JetRover is a composite ROS robot developed by Hiwonder for ROS education scenarios. It supports three motion chassis: Mecanum wheel, Ackerman steering, and crawler. It is equipped with NVIDIA Jetson Nano, high-performance magnetic encoding motor, and 6-degree-of-freedom robotic arm. High-performance hardware configurations such as lidar, 3D depth camera, 7-inch LCD screen, and far-field microphone array can realize robot motion control, mapping navigation, path planning, tracking and obstacle avoidance, autonomous driving, 3D grabbing, navigation and handling, and somatosensory Interaction, far-field voice interaction, group control formation and other applications.
1)6DOF Robot Arm, Intelligent Bus Servo
JetRover is equipped with a 6DOF robot arm and a high-torque bus high-voltage servo, which greatly extends the robot's endurance.
2)LiDAR SLAM Mapping Navigation
JetRover is equipped with lidar, which can realize SLAM mapping and navigation, and supports path planning, fixed-point navigation and dynamic obstacle avoldance.
3)Depth Vision First-Person View
JetRover is equipped with a 6-degree-of-freedom robotic arm, equipped with a high-performance 3D depth camera at the end, which can realize target recognition, tracking and grabbing.
4)6CH Far-field Microphone Array
The 6CH Far-field Microphone Array and speakers support sound source positioning, voice recognition control, voice navigation and other functions.
1. Supports Multiple Chassis Expansions
The JetRover composite robot adapts to the motion characteristics of a variety of chassis, supporting Mecanum wheel, Ackerman steering, and tank chassis to be switched at will. Users can adapt according to their own needs.
2. A Variety of Chassis for Your Choice
JetRover supports three structures of sports chassis: Mecanum wheel, Ackerman steering, and crawler track. Each chassis has its own unique sports characteristics, and users can choose according to their own needs.
1)Mecanum wheel chassis, 360° omnidirectional movement
The Mecanum wheel is a classic omnidirectional wheel.By simply cooperating with the rotation speed and steering of each wheel, it can synthesize torque in any direction and achieve all-round motion of the chassis in a plane.
2)Ackermann chassis, front wheel steering
Ackermann steering is steering based on the differential turning angle of the inner and outer wheels. The JetRover robot car adopts a chassis with 100% Ackerman rate. When turning, the inner wheel rotation angle is greater than the outer wheel rotation angle.
3)Tank chassis differential speed operation
The tank chassis is easy to control and has good ground passability, and is widely used in the field of transportation. JetRover's tank chassis is composed of nylon tank tracks, encoded motors, driving wheels, road wheels, idlers and supporting pulleys. The traveling direction and turning angle of the chassis can be freely controlled.
3. Lidar Mapping Navigation
JetRover is equipped with lidar,which supports path planning, fixed-point navigation, navigation and obstacle avoidance, multiple algorithm mapping, and realizes lidar guard and lidar tracking functions.
1)Various 2D Lidar Mapping Methods
JetRover utilizes various mapping algorithms, such as Gmapping, Hector, Karto, and Cartographer. In addition,it supports path planning,fixed-point navigation, and obstacle avoidance during navigation.
2)Fixed-point Navigation Multi-point Navigation
The robot detects the surrounding environment through lidar and supports common navigation scenarios for commercial robots such as fixed-point navigation, multi-point continuous navigation, andmulti-point circular navigation.
3)TEB Path Planning,Dynamic Obstacle Avoidance
Supports A* global path planning, TEB/DWA multiple local path planning algorithms, detects obstacles in real time during navigation,and re-plans the path to avoid obstacles.
4)RRT Autonomous Exploration Mapping
Without human intervention,JetRover uses the RRT algorithm to independently explore and complete mapping,save the map,and return to the starting point.
5)Lidar Guarding
Guard the surroundings and ring the alarm when detecting intruder.
6)Lidar Tracking
By scanning the front moving object, Lidar makes robot capable of target tracking.
4. Al AutonomousNavigationandTransportation
JetRover-M1 can use LiDAR for SLAM mapping navigation in the built closed environment identify items through 3D first vision, use the inverse kinematics algorithm of the robotic arm to achieve item grabbing, and then use TEB path planning to autonomously Identify the target location and complete autonomous navigation and transportation.
1)Mapping Navigation
2)Target Grabbing
3)Path Planning
4)Autonomous Transportation
5. 3D Vision Al Upgraded Interaction
Equipped with a DaBai depth camera,JetRover can effectively perceive environmental changes, aloing for intelligent Al interaction with humans.
1)RTAB-VSLAM 3D Vision Mapping and Navigation
JetRover utilizes RTAB SLAM algorithm to generate a detailed 3D colored map enabling efficient navigation and obstacle avoidance in complex 3D environments. Additionally, JetRover offers robust support for globallocalization within the created map.
2) ORBSLAM2+ORBSLAM3
ORB-SLAM is an open-source SLAM framework for monocular binocular and RGB-D cameras which is ableto compute the camera trajectory in real time and reconstruct 3D surroundings. And under RGB-D modethe real dimension of the object can be acquired
3)Depth Map Data，Point Cloud
Through the corresponding API JetRover can get depth map color image and point cloud of the camera.
4) Edge Detection
Depth vision allows you to obtain table depth data allowing you to detect the edge of the table.
5) Cross The Single-plank Bridge
Through the 3D depth camera on the robotic arm, the road ahead can be detected, and the body's driving speed can be adjusted to achieve bridge deck driving.
6.Deep LearningA Autonomous Driving
In the ROS system, JetRover has deployed the deep learning framework PyTorch，the open source image processing library OpenCV, the target detection algorithm YOLOv5and the high-performance inference acceleration engine TensorRT to help users who want to explore the field of autonomous driving technology easily enjoy Al autonomous driving.
1)Road Sign Detection
Through training the deep learning model library, JetRover can realize the autonomous driving function with Al vision.
2)Lane Keeping
JetRover is capable of recognizing the lanes on both sides to maintain safe distance between it and the lanes.
3)Autonomous parking
MakingCombined with deep learning algorithms to simulate real scenarios side parking and warehousing can be achieved through Ackerman steering.
4)Turning Decision
According to the lanes road signs and traffic lightsJetRover will estimate the traffic and decide whether to turn.
7.AI Vision Interaction
By incorporating artificial intelligence, JetRover can implement KCF target tracking. Al deep learning, color/ target recognition and tracking AR augmented reality, etc.
1) KCF Target Tracking
Relying on KCF filtering algorithm the robot can track the selected target.
2) Vision Line Following
JetRover supports custom color selection and the robot car identify color lines and follow them.
3) Color Recognition and Tracking
JetRover is able to recognize and track the designated color and can recognize multiple April Tags and their coordinates at the same time.
4) AR Augmented Reality
Select the corresponding graphics through the APP, and let the graphics be presented on theApril Tag code through AR enhancement technology.
5)MediaPipe Development, Upgraded Al Interaction
JetRover is able to recognize and track the designated color, and can recognize multiple April Tags and their coordinates at the same time.
6) AI Deep Learning Framework
Utilize YOLO network algorithm and deep learning model library to recognize the objects.
8. 6CH Far-field Microphone Array Functions
1)Sound Source Localization
Through the 6-microphone arrayhigh-precision positioning of noise reduction sources is achieved.With lidar distance recognition. Hiwonder can be summoned at any location.
2)TTS Voice Broadcast
The text content published by ROS can be directly converted into voice broadcast to facilitate interactive design.
3)Voice Interaction
Speech recognition and TTS voice broadcast are combined to realize voice interaction and support the expansion of iFlytek's online voice conversation 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.
9. Interconnected Formation
Through multi-aircraft communication and navigator technology.
JetRover can realize multi-aircraft formation performances and artificial intelligence games.
1) Multi-vehicle Navigation
Depending on multi-machine communication JetRover can achieve multi-vehicle navigation path planning and smart obstacle.
2) Intelligent Formation
A batch of jetRover can maintain the formation including horizontal line，vertical line and triangle during moving.
3) Group Control
A group of JetRover can be controlled by only one wireless handle to perform actions uniformly and simultaneously
