This world is weird, but only you understand my cuteness--From XR Phage ROS Hexapod robot.
XiaoR GEEK Phage ROS Hexapod programmable robot development kits with Jetson Nano
Dimensions: 513.5mm x 486.69mm x 198.84mm
Weight: 3.0kg
Main controller: Jetson Nano
RAM: 4 GB 64-bit LPDDR4 25.6 GB/s
Interface: UART/SPI/I2C/GPIO
Main control system: Ubuntu18.04
Power supply voltage: 2S lithium battery 7~8.4V
Working current: 9A
Maximum speed: 20cm/second
Battery capacity: 3000mAh
Battery life: about 30 minutes
Standby time: 7.5 hours
Servo model: XR-S270
Degree of freedom: 18 DOF (serial servo) + 1 DOF gimbal (PWM servo)
Charging time: 7-10 hours
Gait Algorithm: Inverse Kinematics Solution
Radar model: Silan A1
Radar detection range: 12m and a half, 360-degree ranging range, 8000 times/second measurement frequency
Camera model: DaBai DCW
Camera resolution: 1920x1080
Camera pixels: 2 million
Depth camera field of view (FOV): H79° V62° D91°±3°
Gyroscope model: 6-axis gyroscope, model mpu6050
Control distance: 20M
Control method: 2.4G wireless handle
Control terminal: handle, PC computer
Programming language: Python/C++
Built-in framework: ROS melodic
Built-in gait functions: stop, standby, forward, backward, left rotation, right rotation, left translation, right translation
2.4G wireless remote control: The robot generates a WiFi signal after it is turned on, and can be remotely controlled by a PC or a wireless handle.
Real-time video transmission: Real-time transmission of video footage captured by the car's camera via WiFi to the computer control software interface, and also supports depth point cloud data viewing
AI vision function: Face recognition: recognize faces and turn your head following the face
Edge detection: edge detection through built-in algorithm
Tag recognition: Aruco codes can be recognized through the camera, and pictures can be attached to the corresponding tags to achieve AR augmented reality.
Somatosensory control: Gesture recognition is realized through the built-in mediapipe framework, and the robot is controlled to make corresponding movements through corresponding gestures.
KCF target tracking: Through the camera screen, the user selects the corresponding object, and the object tracking function can be realized through the KCF algorithm
Silan A1 Radar: Radar Obstacle Avoidance: Through the ROS framework, the Silan A1 radar can achieve radar obstacle avoidance.
Radar following: Through the ROS framework, the equipped Silan A1 radar can realize the radar following function.
Lidar mapping: Equipped with Silan A1 radar and using the hector algorithm, lidar mapping can be achieved indoors
Lidar navigation: Equipped with Silan A1 radar and using the hector algorithm to realize lidar navigation
Depth camera: Visual SLAM: Equipped with Obi Zhongguang Dabai DCW camera, which can perform visual mapping and navigation
6-axis gyroscope body self-balancing: By acquiring data from the six-axis gyroscope in real time, the body self-balancing can be achieved
Cluster control: This robot is equipped with a ROS system. Multiple bionic robots can achieve cluster control and group control formation functions through the ROS framework.
More details， welcome to visit our Official website：www.xiaorgeek.net/products/xi...
Any qeury，please feel free to contact with us email：service@xiaorgeek.com
WhatsAPP：+8613879477201
#bionic #hexapod #educational #ros #jetsonnano #programming #codingrobot #smartrobot
26 окт 2023
