# Yahboom Integration Summary
## Overview
Yahboom Robotics platforms have been integrated into the robotics-mcp server and robotics-webapp repositories. This provides comprehensive support for modern ROS2-based robots with multimodal AI capabilities.
## What Was Integrated
### 1. MCP Server Integration (`robotics-mcp/docs/YAHBOOM_INTEGRATION.md`)
#### Core Components Added:
- **ROS2 Bridge Pattern**: Seamless integration with existing ROS1 infrastructure
- **Unified Robot Control**: Yahboom support integrated into `robot_control` portmanteau tool
- **Virtual Yahboom Support**: Yahboom robots added to `vbot_crud` for virtual testing
- **Multimodal AI Support**: Vision, voice, and text processing via AI queries
- **Sensor Integration**: Camera, LiDAR, IMU data handling
- **Navigation System**: SLAM and autonomous navigation
#### Integrated Operations Available:
```python
# Physical Yahboom control via robot_control tool
await robot_control(robot_id="yahboom_01", action="get_status")
await robot_control(robot_id="yahboom_01", action="home_patrol")
await robot_control(robot_id="yahboom_01", action="ai_query", query="What's in front of me?")
# Virtual Yahboom creation via vbot_crud tool
await vbot_crud(operation="create", robot_type="yahboom", platform="unity")
```
#### ROS2 Launch Integration:
- Complete launch files for Yahboom robots
- Navigation stack integration
- Sensor data publishing
- AI service integration
### 2. Webapp Integration (`robotics-webapp/docs/YAHBOOM_WEBAPP_INTEGRATION.md`)
#### Frontend Components:
- **YahboomRobotCard**: Robot status and control interface
- **YahboomControlPanel**: Manual and autonomous control
- **YahboomSensorDisplay**: Real-time sensor visualization
- **YahboomAIModal**: AI assistant interface
#### Backend Services:
- **YahboomWebappService**: Robot connection management
- **WebSocket Handler**: Real-time command/response
- **REST API**: Status monitoring and control
- **State Management**: React context for Yahboom robots
#### UI Features:
- Real-time sensor data visualization
- LiDAR scan display
- Camera feed streaming
- AI conversation interface
- Navigation controls
## Supported Yahboom Models
### Primary Focus - ROSMASTER M1
- **Price**: €282 (~$300)
- **Features**: Multimodal AI, ROS2, SLAM navigation
- **Integration Level**: Full MCP and webapp support
### Additional Supported Models
- **DOGZILLA Series**: Quadruped robots with AI
- **DOFBOT Series**: Robotic arms with vision
- **ROSMASTER X3 PLUS**: Premium ROS platform
- **JetCobot**: 7-axis collaborative arms
### Autonomous Charging Stations (Critical for Long-Run Operation)
**Available Platforms with Auto-Docking:**
1. **Dreame D20 Pro** (~$300)
- ✅ **Auto-empty base station** (charging + dust bin emptying)
- ✅ **Auto-return on low battery** (20% threshold)
- ✅ **Weeks-long autonomous operation**
- ✅ **Multi-floor mapping** + room segmentation
- ✅ **LiDAR navigation** with obstacle avoidance
- ✅ **Full MCP integration** via `robot_control.return_to_dock`
2. **Moorebot Scout** (~$200)
- ✅ **Auto-docking station** included
- ✅ **Auto-return on low battery**
- ⚠️ **Alignment issues common** (may need calibration)
- ✅ **Indoor patrol robot** with 360° camera
- ✅ **ROS 1.4** (legacy but functional)
3. **Yahboom ROSMASTER Series** (M1/X3/X3 Plus)
- ❌ **No auto-docking station** (manual charging required)
- ❌ **No autonomous return-to-dock**
- ✅ **Superior ROS2/AI capabilities**
- ✅ **Better for manipulation tasks** (with arm addon)
**Recommendation for Autonomous Operation:**
```
Primary Robot: Dreame D20 Pro ($300) - Autonomous vacuum + auto-docking
Secondary Robot: Moorebot Scout ($200) - Patrol robot + auto-docking
AI Platform: Yahboom ROSMASTER M1 ($300) - ROS2 AI tasks
Total: ~$800 for weeks-long unsupervised operation
```
### Generic "Dumb" Robot Platform Support (High Potential)
**ROS-on-PC Architecture Benefits:**
- ✅ **Run ROS2 on powerful PC** (your development machine)
- ✅ **Cheap robot chassis** ($30-100) handle only motors/sensors
- ✅ **Easy hardware swapping** without software changes
- ✅ **Superior debugging** (full access to ROS logs/tools)
- ✅ **Cost-effective scaling** (reuse PC for multiple robots)
**Compatible "Dumb" Robot Platforms:**
- **Elegoo Smart Robot Car V4.0**: $70 (Amazon link), Arduino UNO + motor drivers + ultrasonic + IR + line tracking
- **Elegoo Smart Robot Car Kit**: $30-60, Basic Arduino robot with motor shield
- **Waveshare Alphabot2**: $60-80, Raspberry Pi Zero W + motor HAT + camera
- **SunFounder PiCar-X**: $100-130, includes Raspberry Pi + servo steering
- **Generic Arduino robot kits**: $40-80 with motor shields + sensors
- **ESP32-based robot kits**: $50-90 with WiFi motor control
**Integration Strategy:**
1. **PC runs ROS2** with navigation stack, SLAM, AI processing
2. **Robot handles** basic motor PWM + sensor reading
3. **Communication** via serial (USB), WiFi, or Bluetooth
4. **ROS bridge** translates high-level commands to low-level hardware control
**Development Advantages:**
- ✅ **Zero robot downtime** during software development
- ✅ **Full ROS ecosystem** (RViz, Gazebo, MoveIt) on PC
- ✅ **Easy testing** with virtual robots before hardware
- ✅ **Hardware abstraction** - swap robots without code changes
### ROS-on-PC Architecture Implementation
**Your Robotics-MCP is Perfect for This!**
The MCP server can easily be extended to support "dumb" robot communication protocols:
1. **Serial Bridge**: USB/serial communication with Arduino/ESP32 robots
2. **WiFi Bridge**: TCP/UDP communication with WiFi-enabled robot controllers
3. **Bluetooth Bridge**: Direct Bluetooth LE communication
4. **ROS Serial**: Standard ROS serial protocol for simple robots
**Example Implementation:**
```python
# robotics-mcp/src/robotics_mcp/clients/generic_robot_client.py
class GenericRobotClient:
"""ROS-on-PC client for dumb robot hardware"""
def __init__(self, serial_port="/dev/ttyUSB0", baud_rate=115200):
self.serial = serial.Serial(serial_port, baud_rate)
# Robot just needs to understand: velocity commands + sensor data
def send_motor_commands(self, left_vel: float, right_vel: float):
"""Send PWM values to robot motors"""
# Convert ROS Twist messages to simple motor commands
cmd = f"MOTORS,{left_vel},{right_vel}\n"
self.serial.write(cmd.encode())
def read_sensors(self) -> dict:
"""Read ultrasonic/IR sensors from robot"""
if self.serial.in_waiting:
data = self.serial.readline().decode().strip()
# Parse: "SENSORS,front:25,left:30,right:28"
return self.parse_sensor_data(data)
return {}
```
**Robot Hardware Requirements (Minimal):**
- ✅ **Motor drivers** (L298N, TB6612, or similar)
- ✅ **Microcontroller** (Arduino UNO, ESP32, Raspberry Pi Pico)
- ✅ **Basic sensors** (ultrasonic, IR, wheel encoders)
- ✅ **Communication** (Serial, WiFi, or Bluetooth)
- ❌ **No ROS required** on robot hardware!
### Practical Implementation Guide
**Step 1: Choose Cheap Robot Hardware**
- **Elegoo Smart Robot Car V4.0** (~$35): Arduino UNO + motor shield + ultrasonic
- **DIY Arduino Robot** (~$40): Arduino + motor driver + chassis kit
- **ESP32 Robot Kit** (~$50): WiFi-enabled with motor control
**Step 2: Robot Firmware (Simple Serial Protocol)**
```cpp
// Arduino firmware for "dumb" robot
void setup() {
Serial.begin(115200);
// Setup motor pins, sensor pins
pinMode(MOTOR_LEFT_IN1, OUTPUT);
pinMode(TRIGGER_PIN, OUTPUT);
pinMode(ECHO_PIN, INPUT);
}
void loop() {
if (Serial.available()) {
String cmd = Serial.readStringUntil('\n');
if (cmd.startsWith("MOTORS,")) {
// Parse: "MOTORS,0.5,-0.3"
int comma1 = cmd.indexOf(',');
int comma2 = cmd.indexOf(',', comma1 + 1);
float left_vel = cmd.substring(comma1 + 1, comma2).toFloat();
float right_vel = cmd.substring(comma2 + 1).toFloat();
setMotorSpeeds(left_vel, right_vel);
}
}
// Send sensor data every 100ms
if (millis() % 100 == 0) {
float distance = getUltrasonicDistance();
Serial.print("SENSORS,front:");
Serial.println(distance);
}
}
```
**Step 3: PC-Side ROS Integration**
```python
# ROS node that communicates with "dumb" robot
import rclpy
import serial
from geometry_msgs.msg import Twist
from sensor_msgs.msg import Range
class DumbRobotBridge(rclpy.node.Node):
def __init__(self):
super().__init__('dumb_robot_bridge')
self.serial = serial.Serial('/dev/ttyUSB0', 115200)
# ROS subscribers/publishers
self.cmd_vel_sub = self.create_subscription(
Twist, '/cmd_vel', self.cmd_vel_callback, 10)
self.range_pub = self.create_publisher(Range, '/ultrasonic', 10)
# Timer for sensor reading
self.create_timer(0.1, self.read_sensors)
def cmd_vel_callback(self, msg: Twist):
# Convert ROS Twist to simple motor commands
left_vel = msg.linear.x - msg.angular.z * 0.1 # Differential drive
right_vel = msg.linear.x + msg.angular.z * 0.1
cmd = f"MOTORS,{left_vel},{right_vel}\n"
self.serial.write(cmd.encode())
def read_sensors(self):
if self.serial.in_waiting:
data = self.serial.readline().decode().strip()
if data.startswith("SENSORS,front:"):
distance = float(data.split(':')[1])
range_msg = Range()
range_msg.range = distance / 100.0 # Convert cm to meters
self.range_pub.publish(range_msg)
```
**Cost Comparison:**
- **"Smart" Robot** (ROSMaster M1): $300 + development time
- **"Dumb" Robot** (Elegoo Amazon model): $70 + 1-2 days development
- **ROS-on-PC**: Reuse existing powerful PC instead of robot SBC
### Elegoo Smart Robot Car Integration Guide
**Perfect ROS-on-PC Test Platform!**
**Hardware Specs (Amazon Model - €70):**
- ✅ **Arduino UNO R3** microcontroller
- ✅ **L298N motor driver** (controls 2 DC motors)
- ✅ **Ultrasonic sensor** (HC-SR04 distance measurement)
- ✅ **IR sensors** (obstacle detection, 3x front)
- ✅ **Line tracking sensors** (for following lines)
- ✅ **Bluetooth module** (HC-05 for smartphone control)
- ✅ **Battery holder** (6x AA batteries)
- ✅ **Chassis + wheels** (4-wheel drive ready)
**ROS-on-PC Integration Plan:**
1. **Hardware Modifications:**
- Remove Bluetooth module (not needed for ROS)
- Keep ultrasonic and IR sensors
- Use Arduino serial pins for PC communication
2. **Arduino Firmware (Simple Protocol):**
```cpp
// Elegoo ROS Bridge Firmware
#define MOTOR_ENA 5 // PWM speed control
#define MOTOR_ENB 6 // PWM speed control
#define MOTOR_IN1 7 // Direction control
#define MOTOR_IN2 8 // Direction control
#define MOTOR_IN3 9 // Direction control
#define MOTOR_IN4 11 // Direction control
#define TRIG_PIN 3 // Ultrasonic trigger
#define ECHO_PIN 4 // Ultrasonic echo
#define IR_LEFT 12 // IR obstacle sensors
#define IR_CENTER 13
#define IR_RIGHT 2
void setup() {
Serial.begin(115200);
// Motor pins setup
pinMode(MOTOR_ENA, OUTPUT);
pinMode(MOTOR_ENB, OUTPUT);
pinMode(MOTOR_IN1, OUTPUT);
pinMode(MOTOR_IN2, OUTPUT);
pinMode(MOTOR_IN3, OUTPUT);
pinMode(MOTOR_IN4, OUTPUT);
// Sensor pins setup
pinMode(TRIG_PIN, OUTPUT);
pinMode(ECHO_PIN, INPUT);
pinMode(IR_LEFT, INPUT);
pinMode(IR_CENTER, INPUT);
pinMode(IR_RIGHT, INPUT);
}
void loop() {
// Check for PC commands
if (Serial.available()) {
String cmd = Serial.readStringUntil('\n');
processCommand(cmd);
}
// Send sensor data every 100ms
static unsigned long lastSensorTime = 0;
if (millis() - lastSensorTime > 100) {
sendSensorData();
lastSensorTime = millis();
}
}
void processCommand(String cmd) {
if (cmd.startsWith("MOTORS,")) {
// Parse: "MOTORS,left_speed,right_speed"
int commaIndex = cmd.indexOf(',');
float leftSpeed = cmd.substring(7, commaIndex).toFloat();
float rightSpeed = cmd.substring(commaIndex + 1).toFloat();
setMotorSpeed(leftSpeed, rightSpeed);
}
}
void sendSensorData() {
// Ultrasonic distance (cm)
float ultrasonic = getUltrasonicDistance();
// IR sensors (0 = obstacle, 1 = clear)
int irLeft = digitalRead(IR_LEFT);
int irCenter = digitalRead(IR_CENTER);
int irRight = digitalRead(IR_RIGHT);
// Send: "SENSORS,ultra:25.5,ir:1,0,1"
Serial.print("SENSORS,ultra:");
Serial.print(ultrasonic);
Serial.print(",ir:");
Serial.print(irLeft);
Serial.print(",");
Serial.print(irCenter);
Serial.print(",");
Serial.println(irRight);
}
float getUltrasonicDistance() {
digitalWrite(TRIG_PIN, LOW);
delayMicroseconds(2);
digitalWrite(TRIG_PIN, HIGH);
delayMicroseconds(10);
digitalWrite(TRIG_PIN, LOW);
long duration = pulseIn(ECHO_PIN, HIGH);
float distance = duration * 0.034 / 2; // cm
return distance;
}
void setMotorSpeed(float left, float right) {
// Convert -1.0 to 1.0 range to PWM (0-255)
int leftPWM = abs(left) * 255;
int rightPWM = abs(right) * 255;
// Left motor direction
if (left >= 0) {
digitalWrite(MOTOR_IN1, HIGH);
digitalWrite(MOTOR_IN2, LOW);
} else {
digitalWrite(MOTOR_IN1, LOW);
digitalWrite(MOTOR_IN2, HIGH);
}
// Right motor direction
if (right >= 0) {
digitalWrite(MOTOR_IN3, HIGH);
digitalWrite(MOTOR_IN4, LOW);
} else {
digitalWrite(MOTOR_IN3, LOW);
digitalWrite(MOTOR_IN4, HIGH);
}
// Set speeds
analogWrite(MOTOR_ENA, leftPWM);
analogWrite(MOTOR_ENB, rightPWM);
}
```
3. **ROS PC Integration:**
```python
# robotics-mcp/src/robotics_mcp/clients/elegoo_client.py
import serial
import asyncio
from typing import Dict, Optional
class ElegooClient:
def __init__(self, port="/dev/ttyUSB0", baud_rate=115200):
self.serial = serial.Serial(port, baud_rate, timeout=1)
self.last_sensor_data = {}
async def connect(self) -> bool:
"""Connect to Elegoo robot"""
try:
# Test connection
self.serial.write(b"PING\n")
response = self.serial.readline().decode().strip()
return response == "PONG"
except:
return False
async def set_motors(self, left_speed: float, right_speed: float):
"""Set motor speeds (-1.0 to 1.0)"""
cmd = f"MOTORS,{left_speed},{right_speed}\n"
self.serial.write(cmd.encode())
async def get_sensor_data(self) -> Dict:
"""Get latest sensor readings"""
# This would be called by a background task reading serial
return self.last_sensor_data
def _parse_sensor_data(self, data: str):
"""Parse sensor data from Arduino"""
if data.startswith("SENSORS,"):
parts = data[8:].split(',')
self.last_sensor_data = {
'ultrasonic': float(parts[0].split(':')[1]),
'ir_left': int(parts[1]),
'ir_center': int(parts[2]),
'ir_right': int(parts[3])
}
```
4. **MCP Integration:**
```python
# Add to robot_control.py
@self.mcp.tool()
async def robot_control(robot_id: str, action: str, **params):
if robot_id.startswith("elegoo_"):
# Handle Elegoo robots
if action == "move":
linear_x = params.get("linear_x", 0.0)
angular_z = params.get("angular_z", 0.0)
# Differential drive calculation
left_speed = linear_x - angular_z * 0.1
right_speed = linear_x + angular_z * 0.1
await elegoo_client.set_motors(left_speed, right_speed)
return format_success_response("Elegoo motors set", {"left": left_speed, "right": right_speed})
elif action == "get_status":
sensors = await elegoo_client.get_sensor_data()
return format_success_response("Elegoo sensor data", sensors)
```
## Technical Architecture
### MCP Server Architecture
```
robotics-mcp/
├── src/robotics_mcp/tools/
│ ├── yahboom_bridge.py # ROS2 bridge
│ └── yahboom_tools.py # MCP tools
├── docs/
│ └── YAHBOOM_INTEGRATION.md # Integration guide
└── config/
└── yahboom_config.yaml # Robot configurations
```
### Webapp Architecture
```
robotics-webapp/
├── src/components/robots/yahboom/
│ ├── YahboomRobotCard.tsx
│ ├── YahboomControlPanel.tsx
│ ├── YahboomSensorDisplay.tsx
│ └── YahboomAIModal.tsx
├── src/contexts/
│ └── YahboomContext.tsx
├── backend/
│ ├── yahboom_service.py
│ └── yahboom_websocket.py
└── docs/
└── YAHBOOM_WEBAPP_INTEGRATION.md
```
## Integration Benefits
### For MCP Server
1. **Expanded Robot Support**: From Moorebot-only to multi-platform
2. **Modern ROS2**: Future-proof robotics framework
3. **AI Integration**: Multimodal capabilities beyond basic control
4. **Research Applications**: Suitable for academic and industrial use
### For Webapp
1. **Unified Interface**: Control Yahboom robots alongside virtual robots
2. **Real-time Visualization**: Sensor data and AI analysis display
3. **AI Assistant**: Natural language robot interaction
4. **Scalable Architecture**: Support for multiple concurrent robots
## Configuration Examples
### MCP Server Configuration
```yaml
# config/yahboom_config.yaml
yahboom:
robots:
- id: "rosmaster_m1_001"
name: "Lab Robot"
model: "rosmaster_m1"
ip: "192.168.1.100"
port: 9090
ai_enabled: true
navigation_enabled: true
```
### Webapp Configuration
```typescript
// Robot configuration in webapp
const yahboomConfig = {
id: 'yahboom-001',
name: 'ROSMASTER M1',
model: 'rosmaster_m1',
config: {
ip: '192.168.1.100',
port: 9090
}
};
```
## Testing and Validation
### MCP Server Testing
```bash
# Test Yahboom tools
python -m pytest tests/unit/test_yahboom_tools.py -v
# Test ROS2 integration
python -m pytest tests/integration/test_yahboom_integration.py -v
```
### Webapp Testing
```bash
# Test frontend components
npm test -- --testPathPattern=yahboom
# Test backend services
python -m pytest backend/tests/test_yahboom_service.py -v
```
## Deployment
### Docker Integration
```dockerfile
# Multi-stage build for Yahboom integration
FROM ros:humble-ros-base AS ros-base
RUN apt-get update && apt-get install -y ros-humble-navigation2
FROM python:3.11-slim AS python-base
RUN pip install yahboom-sdk yahboom-ai
FROM python-base AS final
COPY --from=ros-base /opt/ros /opt/ros
COPY . /app
WORKDIR /app
CMD ["python", "-m", "robotics_mcp.server"]
```
### Production Setup
1. **Hardware**: Connect Yahboom robot to network
2. **ROS2**: Install ROS2 Humble on control machine
3. **SDK**: Install Yahboom Python SDK
4. **Configuration**: Update robot IP addresses
5. **Testing**: Validate all tools and webapp components
## Future Enhancements
### Planned Features
1. **Multi-Robot Coordination**: Swarm robotics support
2. **Advanced AI Models**: Custom model training integration
3. **Cloud Integration**: Remote robot monitoring
4. **VR Integration**: Virtual reality robot control
### Expansion Opportunities
1. **DOGZILLA Integration**: Quadruped locomotion research
2. **DOFBOT Integration**: Manipulation and grasping
3. **JetCobot Integration**: Industrial applications
## Summary
The Yahboom integration significantly expands the robotics ecosystem from a single Moorebot Scout to a comprehensive multi-platform robotics framework. Key achievements:
- ✅ **Unified robot control** - Yahboom integrated into existing `robot_control` portmanteau tool
- ✅ **Virtual Yahboom support** - Yahboom robots added to `vbot_crud` for virtual testing
- ✅ **Multimodal AI capabilities** - Vision, voice, and text processing via AI queries
- ✅ **Complete ROS2 client** - Full ROS2 bridge with navigation, camera, and arm control
- ✅ **Modern conversational AI** - Rich response formats with safety warnings and recommendations
- ✅ **Multimodal AI** capabilities beyond basic robot control
- ✅ **Research-grade platform** suitable for academic and industrial applications
This integration positions the robotics platform as a comprehensive solution for both educational and professional robotics applications, with Yahboom providing the modern ROS2 and AI capabilities that complement the existing virtual robotics infrastructure.
