主控制器 Raspberry Pi 4B(4GB RAM)或 Raspberry Pi 3B+(根据版本)
操作系统 Ubuntu 20.04 + ROS Noetic(官方预装镜像)
尺寸/重量 Burger: 138mm×178mm×192mm, 0.8kg
Waffle: 281mm×306mm×141mm, 1.2kg
供电方式 12V 锂电池(续航2-4小时)或DC适配器
运动系统
驱动电机 直流减速电机(Burger: 0.3Nm扭矩;Waffle: 0.7Nm扭矩)
编码器 磁编码器(分辨率:0.01mm/脉冲)
轮径/轮距 Burger: Φ66mm/160mm
Waffle: Φ81mm/287mm
最大速度 Burger: 0.22m/s
Waffle: 0.26m/s
传感器套件
激光雷达 LDS-01(360°扫描,5.5m测距,采样率2kHz)
IMU MPU9250(9轴:加速度计+陀螺仪+磁力计)
摄像头 Raspberry Pi Camera V2(可选,800万像素)
开发工具
rqt_graph 可视化ROS节点通信关系
Gazebo仿真 提供TurtleBot3高精度仿真模型(支持SLAM训练)
OpenCR固件 开源控制板程序(支持Arduino IDE开发)
典型性能指标
SLAM建图 10m×10m环境,精度±5cm(使用cartographer)
路径跟踪 直线误差<2cm(速度0.15m/s时)
最大负载 Burger: 1kg
Waffle: 3kg(含扩展结构)
Host controller Raspberry Pi 4B (4GB RAM) or Raspberry Pi 3B+ (depending on version)
OS Ubuntu 20.04 + ROS Noetic (official pre-installed image)
Size/Weight Burger: 138mm×178mm×192mm, 0.8kg
Waffle: 281mm×306mm×141mm, 1.2kg
Power supply 12V Li-ion battery (2-4 hours battery life) or DC adapter
Motion System
Drive motor DC geared motor (Burger: 0.3Nm torque; Waffle: 0.7Nm torque)
Encoder Magnetic encoder (resolution: 0.01mm/pulse)
Wheel diameter/wheel pitch Burger: Φ66mm/160mm
Waffle: Φ81mm/287mm
Maximum speed Burger: 0.22m/s
Waffle: 0.26m/s
Sensor package
LIDAR LDS-01 (360° scanning, 5.5m ranging, 2kHz sampling rate)
IMU MPU9250 (9-axis: accelerometer + gyroscope + magnetometer)
Camera Raspberry Pi Camera V2 (optional, 8MP)
Development Tools
rqt_graph Visualization of ROS node communication relationships
Gazebo Simulation Provides TurtleBot3 high-precision simulation model (supports SLAM training)
OpenCR firmware Open source control panel program (supports Arduino IDE development)
Typical Performance Indicators
SLAM map building 10m×10m environment, accuracy ±5cm (using cartographer)
Path tracking Straight line error <2cm (at speed 0.15m/s)
Maximum load Burger: 1kg
Waffle: 3kg (with extension structure)
TurtleBot3 Waffi PI可以用于SLAM、导航和集成,适合于家庭服务机器人。可以运行SLAM算法来构建地图,并且实现室内的运路径规划和避障功能。还可以通过笔记本电脑,手柄或者基于安卓智能手机远程控制。同时,可以集成机械手,通过集成机械手来抓取物体。
TurtleBot3 Waffi PI can be used for SLAM, navigation and integration for home service robots. It can run SLAM algorithms to build maps and enable indoor path planning and obstacle avoidance. It can also be remotely controlled from a laptop, joystick or Android-based smartphone. At the same time, it can be integrated with a robot to grasp objects through an integrated manipulator.
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