- Developed for ROS education, features Python support and deep learning with MediaPipe for AI projects like object recognition and voice interaction
- With a 6DOF design and 35KG torque servos, JetArm includes an HD camera for first-person object gripping
- Equipped with a 3D depth camera, JetArm utilizes RGB+D fusion for flexible 3D grabbing and AI applications
- JetArm ultimate kit features a microphone array and speaker for voice-controlled tasks, including navigation and gripping
- Provides multiple control methods, like WonderAi app (compatible with iOS and Android system), wireless handle, Robot Operating System (ROS) and keyboard
Product Description
JetArm is a desktop-level AI vision robotic arm developed by Hiwonder for ROS education scenarios. It is equipped with a 3D depth camera, combines 3D vision technology with robotic arm control, and is equipped with high-torque intelligent bus servos, NVIDIA Jetson Nano master control, High-performance hardware such as a 7-inch touch screen, far-field microphone array, and speakers can not only realize 3D motion control of the robot, but also identify, track, and grab target objects in 3D scene.
1) Depth Vision, 3D Scene Flexible Grabbing
The end of the JetArm robot arm is equipped with a high-performance 3D depth camera, which can realize target recognition, tracking and grabbing. Through RGB+D fusion detection, JetArm can also realize flexible grabbing in 3D scene.
2) All-metal Structure, Bearing Base
The body of the robot arm adopts an all-metal structure, and the surface is anodized, making it exquisite and beautiful. The base uses industrial-grade bearings to meet high-demand grabbing projects.
3) Wrapped Structure Design, Beautiful Wiring
JetArm adopts a wrapped structure design, and the wiring of the servo can be hidden inside the fuselage, making the outside of the fuselage clean and tidy.
4) Circular Microphone Array
The circular microphone array is divided into a microphone array and a module motherboard. It has stronger overall performance and a sound pickup range of up to 10m.
1. 3D Depth Vision Al Upgraded Interaction
Equipped with a Gemini plus 3D depth camera, JetArm can effectively perceive environmental changes, allowing for intelligent Al interaction with humans.
1) RGB+D Detection, 3D Scene Flexible Grabbing
JetArm's 3D depth camera can fuse RGB information and depth information, and can perceive the color and pointcloud depth data of objects, enriching the geometric expression of abject spatial information.Through the inverse kinematics algoritm, JetArm can realize high-level Al projects such as flexible grabbing, sorting, and transportation in 3D scene.
2) 3D Depth Point Cloud Recognition
Through the corresponding APl of the depth camera, JetArm can obtain the depth map, color map and point cloud map of the detection environment, and then obtain the RGB data, position coordinates, and depth information of the target item to achieve shape recognition, color sorting, height measurrement, material detection, etc.
3) Target Object Shape Recognition
By obtaining the depth point cloud data of the object, the shape of the object can be identified and the analysis results are transmitted to the robot arm.
4) Regional Target Height Measurement
By obtaining the depth point cloud data of the object, the height of the object can be identified, thereby realizing the game of removing highly abnormal objects.
2. Al Vision Recognition Target Tracking
JetArm's 3D depth camera is equipped with an RGB lens. The robot arm uses OpencV as the image processing library, supports Al intelligent image recognition, and can realize a varietyof intelligent vision gamep such as color recognition and tag recognition.
1) Color Sorting
JetArm can recognize and sort color blocks of different colors.In addition to standard colors, JetArm can also recognize a variety of custom colors.
2) Tag Recognition, Intelligent Stacking
JetArm can recognize different AprilTags and determine the position of the tag block to achieve intelligent stacking.
3) Target Tracking
JetArm can locate and track targets, cnd we can also use machine learning to let JetArm track more trained target items.
3. Upgraded Inverse Kinematics Algorithm
JetArm has a high-level inverse kinematics algorithm, which can move to any coordinate in 3D scene, and the path planning of the robot arm can also be realized by Python programming.
1) Target Detection, Joint Adaptive Adjustment
JetArm can detect target items within the recognition area and calculate the position coordinates and placement angle of the target item. Combined with the inverse kinematics algorithm of the robot arm, each joint angle is adaptively adjusted to achieve free grabbing.
2) 3D Scene Motion Control
JetArm can use inverse kinematics algorithms to achieve linear motion and path planning in 3D scene.
3) Provides Source Code for DH Model and Inverse Kinematics
Provide the inverse kinematics analysis, coordinate DH model and inverse kinematics function source code of the JetArm robot arm, and input the end coordinates of the robot arm greatly shortening the project development time.
4. Deep Learning Model Training
JetArm uses neural networks such as GoogLeNet, Yolo, and mtcnn, which can perform deep learning on the target to generate a trained model.
1) Waste Sorting
JetArm's kit is equipped with garbage pattern blocks. By loading the corresponding model, JetArm can quickly recognize different garbage and place it in the corresponding classification area.
2) Item Sorting
By training models of daily items and generating correspond-ing models, with the support of depth camera, JetArm can quickly recognize and grab corresponding items by obtaining the depth information of the items.
3) MediaPipe Development, Upgraded Al Interaction
JetArm utilizes MediaPipe development framework to accomplish various functions, such as human body recognition, fingertip recognition, face detection, and 3D detection.
4) Fingertip Trajectory Control
Based on the detection ofthe distance between fingertips, JetAmm can perform correspending actions.
5. Gazebo Simulation
The JetArm robotic arm is developed using the ROS framework and supports GAZEBO simulation. The robotic arm is controlled and algorithm verified in a virtual environment, which reduces the requirements for the experimental environment and improves experimental efficiency.
6. Provide Multi-platform SDK
Provides a multi-platform (Windows/Android/Linux) software development kit that can quickly obtain depth/RGB/skeleton and other information recognized by the camera. It has built-in filtering algorithms to facilitate secondary development.
7. Various Control Methods
1) WonderAi App
2) PC Software
3) Wireless Handle
