Unitree B2 Industrial Robot Dog Development Guide 2026

The Unitree B2 is a high-performance quadruped robot designed for industrial applications. Compared to the consumer-grade Go2, the B2 offers superior payload capacity, higher protection rating, and longer battery life. This article provides an in-depth guide to B2 secondary development, covering three industrial scenarios: inspection, transport, and detection, along with complete PLC integration solutions and an automated warehouse inspection project.

1. B2 vs Go2: Selection Guide

Hardware Specifications Comparison

Parameter	B2 (Industrial)	Go2 (Consumer)
Payload Capacity	Up to 40kg	Up to 5kg
Battery Life	4-6 hours	1-2 hours
Protection Rating	IP67 (waterproof/dustproof)	IP44 (splash-proof)
Operating Temperature	-20°C ~ 60°C	0°C ~ 40°C
Max Speed	3.5 m/s	2.5 m/s
Climbing Ability	Up to 30°	Up to 20°
Communication Range	2km (4G/5G expandable)	200m (WiFi)
Reference Price	$20,000-35,000	$1,500-4,000

Application Scenario Differences

B2 Suitable Scenarios:

Power inspection: substations, transmission lines, wind farms
Mining operations: underground exploration, ore transport
Emergency rescue: disaster site search, supply transport
Warehouse logistics: automated warehouse inspection, cargo handling

Go2 Suitable Scenarios:

Research and education: robotics experiments, algorithm validation
Home companionship: daily interaction, simple tasks
Light inspection: campus patrol, environmental monitoring
Entertainment: demonstrations, interactive events

Selection Recommendations

Choose B2 when:

Need to carry heavy sensors (LiDAR, thermal cameras)
Harsh working environment (dust, moisture, extreme temperatures)
Long continuous operation required
Sufficient budget, pursuing industrial-grade reliability

Choose Go2 when:

Limited budget, mainly for learning and development
Relatively mild working environment
Low payload requirements
Rapid prototype validation

2. B2 SDK Environment Setup

Industrial Environment Configuration Requirements

The B2 SDK (unitree_sdk2) supports Ubuntu 20.04 LTS. It is recommended to use an industrial PC or embedded computer as the development platform.

Hardware Requirements:

CPU: Intel i5 or equivalent ARM processor
RAM: 8GB+
Storage: 64GB+ SSD
Network: Gigabit Ethernet + WiFi 6
Interfaces: USB 3.0, RS485, CAN

Software Requirements:

OS: Ubuntu 20.04 LTS
CMake: 3.10+
GCC: 9.4.0+
Python: 3.8+ (optional)

SDK Installation and Compilation

# 1. Install dependencies
sudo apt-get update
sudo apt-get install -y cmake g++ build-essential \
    libyaml-cpp-dev libeigen3-dev libboost-all-dev \
    libfmt-dev

# 2. Clone SDK
git clone https://github.com/unitreerobotics/unitree_sdk2.git
cd unitree_sdk2

# 3. Build
mkdir build && cd build
cmake ..
make -j4

# 4. Install to system directory
sudo make install

# Or install to custom directory
cmake .. -DCMAKE_INSTALL_PREFIX=/opt/unitree_robotics
sudo make install

Python SDK Installation

# Install Python bindings
pip install unitree-sdk2-python

# Or install from source
git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
cd unitree_sdk2_python
pip install -e .

Integration with PLC/SCADA Systems

The B2 can communicate with PLCs via industrial protocols such as Modbus TCP and OPC UA.

# Modbus TCP client example
from pymodbus.client import ModbusTcpClient
from unitree_sdk2 import RobotInterface

class B2PLCController:
    def __init__(self, plc_ip, plc_port=502):
        # Connect to PLC
        self.plc = ModbusTcpClient(plc_ip, port=plc_port)
        self.plc.connect()
        
        # Connect to B2
        self.robot = RobotInterface()
        self.robot.connect("192.168.1.100")
    
    def read_plc_status(self):
        """Read PLC status registers"""
        result = self.plc.read_holding_registers(address=0, count=10)
        return result.registers
    
    def send_robot_status(self, status_code):
        """Send robot status to PLC"""
        self.plc.write_register(address=100, value=status_code)
    
    def close(self):
        self.plc.close()
        self.robot.disconnect()

Common Troubleshooting

Issue 1: SDK Compilation Failure

# Check CMake version
cmake --version

# If version is too low, upgrade CMake
wget https://github.com/Kitware/CMake/releases/download/v3.26.4/cmake-3.26.4.tar.gz
tar -xzf cmake-3.26.4.tar.gz
cd cmake-3.26.4
./bootstrap && make && sudo make install

Issue 2: Unstable Network Connection

# Check network latency
ping 192.168.1.100

# Adjust network parameters
sudo sysctl -w net.ipv4.tcp_keepalive_time=60
sudo sysctl -w net.ipv4.tcp_keepalive_probes=3

Issue 3: Robot Cannot Start

# Check power status
sudo dmesg | grep -i usb

# Restart robot service
ssh root@192.168.1.100 "systemctl restart unitree_robot"

3. Industrial Application Scenarios

3.1 Inspection Scenario

Power Inspection Route Planning

Power inspection is a typical B2 application scenario. By pre-setting inspection routes, the B2 can autonomously complete temperature, vibration, and sound detection of substation equipment.

import numpy as np
from unitree_sdk2 import RobotInterface, PathPlanner

class PowerInspection:
    def __init__(self):
        self.robot = RobotInterface()
        self.planner = PathPlanner()
        
        # Define inspection points (substation equipment coordinates)
        self.inspection_points = [
            {"name": "Transformer T1", "pos": [10, 5, 0], "check_temp": True},
            {"name": "Circuit Breaker CB1", "pos": [15, 8, 0], "check_vibration": True},
            {"name": "Disconnect Switch DS1", "pos": [20, 5, 0], "check_temp": True},
            {"name": "Current Transformer CT1", "pos": [25, 8, 0], "check_sound": True},
        ]
    
    def plan_route(self):
        """Plan optimal inspection route"""
        # Use TSP algorithm to optimize route
        route = self.planner.solve_tsp(self.inspection_points)
        return route
    
    def execute_inspection(self, route):
        """Execute inspection task"""
        for point in route:
            # Move to target position
            self.robot.move_to(point["pos"])
            
            # Perform detection
            if point.get("check_temp"):
                temp = self._measure_temperature()
                self._log_data(point["name"], "temperature", temp)
                
                # Alert on anomaly
                if temp > 80:  # Temperature threshold
                    self._send_alert(f"{point['name']} temperature anomaly: {temp}°C")
            
            if point.get("check_vibration"):
                vibration = self._measure_vibration()
                self._log_data(point["name"], "vibration", vibration)
                
                if vibration > 5.0:  # Vibration threshold (mm/s)
                    self._send_alert(f"{point['name']} vibration anomaly: {vibration} mm/s")
            
            if point.get("check_sound"):
                sound_level = self._measure_sound()
                self._log_data(point["name"], "sound", sound_level)
    
    def _measure_temperature(self):
        """Measure temperature using thermal camera"""
        # Call thermal camera API
        thermal_image = self.robot.get_thermal_image()
        max_temp = np.max(thermal_image)
        return max_temp
    
    def _measure_vibration(self):
        """Measure vibration using accelerometer"""
        imu_data = self.robot.get_imu_data()
        vibration = np.sqrt(np.sum(imu_data["accel"]**2))
        return vibration
    
    def _measure_sound(self):
        """Measure sound using microphone array"""
        audio_data = self.robot.get_audio_data()
        sound_level = np.max(np.abs(audio_data))
        return sound_level
    
    def _log_data(self, device, metric, value):
        """Log detection data"""
        timestamp = datetime.now().isoformat()
        log_entry = {
            "timestamp": timestamp,
            "device": device,
            "metric": metric,
            "value": value
        }
        # Save to database or file
        with open("inspection_log.json", "a") as f:
            f.write(json.dumps(log_entry) + "\n")
    
    def _send_alert(self, message):
        """Send alert message"""
        # Send to monitoring center via 4G/5G network
        requests.post("https://monitor.example.com/alert", json={
            "robot_id": "B2-001",
            "message": message,
            "timestamp": datetime.now().isoformat()
        })

Equipment Status Detection

The B2 can integrate various sensors for equipment status detection:

Thermal Camera: Detect equipment overheating
Vibration Sensor: Monitor mechanical vibration
Acoustic Sensor: Identify abnormal sounds
Visible Light Camera: Read instrument values, detect equipment appearance

Anomaly Alert Mechanism

class AlertSystem:
    def __init__(self):
        self.alert_levels = {
            "normal": 0,
            "warning": 1,
            "critical": 2,
            "emergency": 3
        }
    
    def check_threshold(self, device, metric, value):
        """Check if threshold is exceeded"""
        thresholds = {
            "temperature": {"warning": 70, "critical": 85},
            "vibration": {"warning": 4.0, "critical": 6.0},
            "sound": {"warning": 80, "critical": 95}
        }
        
        if metric in thresholds:
            if value >= thresholds[metric]["critical"]:
                return "critical"
            elif value >= thresholds[metric]["warning"]:
                return "warning"
        
        return "normal"

3.2 Transport Scenario

Load Control and Balance

The B2 has a maximum payload of 40kg and requires precise load distribution control to maintain balance.

class B2Transporter:
    def __init__(self):
        self.robot = RobotInterface()
        self.max_payload = 40.0  # kg
        self.current_payload = 0.0
    
    def load_cargo(self, weight):
        """Load cargo"""
        if self.current_payload + weight > self.max_payload:
            raise ValueError(f"Overload: {self.current_payload + weight}kg > {self.max_payload}kg")
        
        self.current_payload += weight
        self.robot.set_payload_compensation(self.current_payload)
        
        # Adjust gait parameters
        self.robot.set_gait_params(
            step_height=0.15,  # Increase step height
            step_length=0.3,   # Reduce step length
            speed=0.5          # Reduce speed
        )
    
    def unload_cargo(self):
        """Unload cargo"""
        self.current_payload = 0.0
        self.robot.set_payload_compensation(0.0)
        self.robot.set_gait_params(
            step_height=0.1,
            step_length=0.4,
            speed=1.0
        )
    
    def transport_to(self, target_pos):
        """Transport to target position"""
        # Monitor load status in real-time
        while not self.robot.reached_position(target_pos):
            # Check if load has shifted
            payload_offset = self.robot.get_payload_offset()
            
            if np.linalg.norm(payload_offset) > 0.1:  # Offset exceeds 10cm
                self.robot.adjust_payload_position()
            
            self.robot.step()

Path Optimization

from scipy.spatial import KDTree

class PathOptimizer:
    def __init__(self, warehouse_map):
        self.map = warehouse_map
        self.obstacles = warehouse_map.get_obstacles()
        self.obstacle_tree = KDTree(self.obstacles)
    
    def find_shortest_path(self, start, goal):
        """Find shortest path using A* algorithm"""
        # Implement A* algorithm
        open_set = [(0, start)]
        came_from = {}
        g_score = {start: 0}
        f_score = {start: self._heuristic(start, goal)}
        
        while open_set:
            current = heapq.heappop(open_set)[1]
            
            if current == goal:
                return self._reconstruct_path(came_from, current)
            
            for neighbor in self._get_neighbors(current):
                if self._is_collision(neighbor):
                    continue
                
                tentative_g = g_score[current] + self._distance(current, neighbor)
                
                if neighbor not in g_score or tentative_g < g_score[neighbor]:
                    came_from[neighbor] = current
                    g_score[neighbor] = tentative_g
                    f_score[neighbor] = tentative_g + self._heuristic(neighbor, goal)
                    heapq.heappush(open_set, (f_score[neighbor], neighbor))
        
        return None
    
    def _heuristic(self, a, b):
        """Manhattan distance"""
        return abs(a[0] - b[0]) + abs(a[1] - b[1])

Multi-Robot Collaborative Transport

class MultiRobotTransport:
    def __init__(self, robot_ids):
        self.robots = {rid: RobotInterface(rid) for rid in robot_ids}
        self.task_queue = []
    
    def assign_task(self, task):
        """Assign task to optimal robot"""
        # Select optimal robot based on position, payload, battery
        best_robot = min(
            self.robots.values(),
            key=lambda r: self._compute_cost(r, task)
        )
        best_robot.execute_task(task)
    
    def _compute_cost(self, robot, task):
        """Compute task cost"""
        distance = robot.distance_to(task["start"])
        payload_capacity = robot.max_payload - robot.current_payload
        battery_level = robot.get_battery_level()
        
        # Combined cost: distance + payload capacity + battery
        cost = distance * 0.5 + (1.0 / payload_capacity) * 0.3 + (1.0 / battery_level) * 0.2
        return cost

3.3 Detection Scenario

Visual Inspection Integration

import cv2
import torch
from ultralytics import YOLO

class VisualInspection:
    def __init__(self):
        self.robot = RobotInterface()
        self.model = YOLO("defect_detection.pt")
    
    def detect_defects(self):
        """Detect product defects"""
        # Get camera image
        image = self.robot.get_camera_image()
        
        # Run YOLO detection
        results = self.model(image)
        
        defects = []
        for result in results:
            for box in result.boxes:
                defect = {
                    "class": result.names[int(box.cls)],
                    "confidence": float(box.conf),
                    "bbox": box.xyxy[0].tolist()
                }
                defects.append(defect)
        
        return defects
    
    def generate_report(self, defects):
        """Generate inspection report"""
        report = {
            "timestamp": datetime.now().isoformat(),
            "robot_id": self.robot.id,
            "defects": defects,
            "total_count": len(defects),
            "pass": len([d for d in defects if d["confidence"] < 0.5]) == 0
        }
        
        # Save report
        with open(f"report_{report['timestamp']}.json", "w") as f:
            json.dump(report, f, indent=2)
        
        return report

Data Collection and Upload

class DataCollector:
    def __init__(self):
        self.robot = RobotInterface()
        self.data_buffer = []
    
    def collect_data(self):
        """Collect sensor data"""
        data = {
            "timestamp": datetime.now().isoformat(),
            "imu": self.robot.get_imu_data(),
            "lidar": self.robot.get_lidar_scan(),
            "camera": self.robot.get_camera_image(),
            "thermal": self.robot.get_thermal_image(),
            "battery": self.robot.get_battery_level(),
            "position": self.robot.get_position()
        }
        
        self.data_buffer.append(data)
        
        # Upload when buffer is full
        if len(self.data_buffer) >= 100:
            self.upload_data()
    
    def upload_data(self):
        """Upload data to cloud"""
        requests.post(
            "https://cloud.example.com/api/data",
            json={"robot_id": self.robot.id, "data": self.data_buffer}
        )
        self.data_buffer.clear()

4. PLC Integration Solution

Communication Protocol Selection

In industrial scenarios, reliable industrial protocols are required for B2-PLC communication:

Protocol	Characteristics	Application
Modbus TCP	Simple, widely supported	Small systems, low cost
OPC UA	Secure, cross-platform	Large systems, multi-vendor
PROFINET	Strong real-time	Siemens PLC
EtherNet/IP	Rockwell ecosystem	Allen-Bradley PLC

Modbus TCP Implementation

from pymodbus.server import StartTcpServer
from pymodbus.datastore import ModbusSequentialDataBlock
from pymodbus.datastore import ModbusSlaveContext, ModbusServerContext

class B2ModbusServer:
    def __init__(self, robot):
        self.robot = robot
        
        # Initialize data registers
        # 0-9: Robot status
        # 10-19: Sensor data
        # 20-29: Control commands
        self.data = {
            0: [0] * 30  # 30 registers
        }
        
        # Create Modbus data store
        self.store = ModbusSequentialDataBlock(0, self.data[0])
        self.context = ModbusSlaveContext(
            di=self.store,  # Discrete inputs
            co=self.store,  # Coils
            hr=self.store,  # Holding registers
            ir=self.store   # Input registers
        )
        self.server_context = ModbusServerContext(
            slaves=self.context, single=True
        )
    
    def start(self, port=502):
        """Start Modbus TCP server"""
        StartTcpServer(
            context=self.server_context,
            address=("0.0.0.0", port)
        )
    
    def update_robot_status(self):
        """Update robot status to registers"""
        # Register 0: Running status (0=stopped, 1=running, 2=error)
        self.store.setValues(0, [self.robot.get_status()])
        
        # Register 1: Battery percentage
        self.store.setValues(1, [int(self.robot.get_battery_level() * 100)])
        
        # Register 2-4: Current position (x, y, z)
        pos = self.robot.get_position()
        self.store.setValues(2, [int(pos[0] * 100), int(pos[1] * 100), int(pos[2] * 100)])
        
        # Register 5-7: IMU data
        imu = self.robot.get_imu_data()
        self.store.setValues(5, [
            int(imu["roll"] * 100),
            int(imu["pitch"] * 100),
            int(imu["yaw"] * 100)
        ])
    
    def read_plc_commands(self):
        """Read PLC control commands"""
        # Register 20: Start/stop command
        command = self.store.getValues(20, 1)[0]
        
        if command == 1:
            self.robot.start()
        elif command == 2:
            self.robot.stop()
        elif command == 3:
            self.robot.return_to_dock()
        
        # Register 21-23: Target position
        target = self.store.getValues(21, 3)
        if target != [0, 0, 0]:
            self.robot.move_to([t / 100.0 for t in target])

OPC UA Implementation

from asyncua import Server, ua
import asyncio

class B2OPCUAServer:
    def __init__(self, robot):
        self.robot = robot
        self.server = Server()
    
    async def start(self):
        """Start OPC UA server"""
        await self.server.init()
        self.server.set_endpoint("opc.tcp://0.0.0.0:4840")
        
        # Register namespace
        uri = "http://unitree.com/b2"
        idx = await self.server.register_namespace(uri)
        
        # Create object
        objects = self.server.nodes.objects
        self.b2_node = await objects.add_object(idx, "B2_Robot")
        
        # Add variables
        self.status_var = await self.b2_node.add_variable(idx, "Status", 0)
        self.battery_var = await self.b2_node.add_variable(idx, "Battery", 0.0)
        self.position_var = await self.b2_node.add_variable(idx, "Position", [0.0, 0.0, 0.0])
        
        # Add method
        await self.b2_node.add_method(
            idx, "MoveTo", self.move_to_method,
            [ua.VariantType.Float, ua.VariantType.Float, ua.VariantType.Float],
            [ua.VariantType.Boolean]
        )
        
        # Start server
        async with self.server:
            while True:
                await self.update_variables()
                await asyncio.sleep(1)
    
    async def update_variables(self):
        """Update variable values"""
        await self.status_var.write_value(self.robot.get_status())
        await self.battery_var.write_value(self.robot.get_battery_level())
        await self.position_var.write_value(self.robot.get_position())
    
    async def move_to_method(self, parent, x, y, z):
        """Move to specified position"""
        success = self.robot.move_to([x, y, z])
        return success

Fault Handling Mechanism

class FaultHandler:
    def __init__(self, robot, plc):
        self.robot = robot
        self.plc = plc
        self.fault_codes = {
            0: "Normal",
            1: "Low battery",
            2: "Motor overheating",
            3: "Communication lost",
            4: "Sensor failure",
            5: "Path blocked"
        }
    
    def handle_fault(self, fault_code):
        """Handle fault"""
        print(f"Fault: {self.fault_codes[fault_code]}")
        
        # Notify PLC
        self.plc.write_register(100, fault_code)
        
        # Execute fault handling
        if fault_code == 1:  # Low battery
            self.robot.return_to_dock()
        elif fault_code == 2:  # Motor overheating
            self.robot.stop()
            self.robot.cool_down()
        elif fault_code == 3:  # Communication lost
            self.robot.emergency_stop()
        elif fault_code == 4:  # Sensor failure
            self.robot.switch_to_backup_sensor()
        elif fault_code == 5:  # Path blocked
            self.robot.replan_path()

5. Practical Project: Automated Warehouse Inspection

Project Requirements Analysis

A large e-commerce warehouse requires 24-hour automated inspection with the following requirements:

Inspection Area: 10,000 square meter warehouse with 50 shelf zones
Inspection Content: Shelf integrity, aisle obstruction, temperature anomalies, smoke detection
Inspection Frequency: Full coverage every 2 hours
Alert Response: Notify administrator within 5 minutes of anomaly detection

System Architecture Design

┌─────────────────────────────────────────────────────┐
│                Cloud Monitoring Platform            │
│  - Data visualization                               │
│  - Alert push                                       │
│  - Historical data analysis                         │
└────────────────┬────────────────────────────────────┘
                 │ 4G/5G
┌────────────────┴────────────────────────────────────┐
│              Edge Computing Gateway                 │
│  - Data preprocessing                               │
│  - Local decision making                            │
│  - PLC communication                                │
└────────────────┬────────────────────────────────────┘
                 │ Ethernet
┌────────────────┴────────────────────────────────────┐
│              B2 Inspection Robot                    │
│  - Autonomous navigation                            │
│  - Multi-sensor fusion                              │
│  - Anomaly detection                                │
└─────────────────────────────────────────────────────┘

Inspection Route Planning Algorithm

import numpy as np
from scipy.spatial import KDTree

class WarehouseInspector:
    def __init__(self, warehouse_map):
        self.robot = RobotInterface()
        self.map = warehouse_map
        self.inspection_zones = warehouse_map.get_zones()
        
        # Define checkpoints (key positions in each shelf zone)
        self.checkpoints = []
        for zone in self.inspection_zones:
            # 4 corners + center point per zone
            corners = zone.get_corners()
            center = zone.get_center()
            self.checkpoints.extend(corners + [center])
    
    def plan_optimal_route(self):
        """Plan optimal inspection route (TSP problem)"""
        # Use genetic algorithm to solve TSP
        from scipy.optimize import differential_evolution
        
        n = len(self.checkpoints)
        
        def objective(order):
            """Calculate total route length"""
            order = order.argsort()
            total_dist = 0
            for i in range(n):
                p1 = self.checkpoints[order[i]]
                p2 = self.checkpoints[order[(i + 1) % n]]
                total_dist += np.linalg.norm(
                    np.array(p1) - np.array(p2)
                )
            return total_dist
        
        # Optimize
        bounds = [(0, n)] * n
        result = differential_evolution(objective, bounds, maxiter=1000)
        
        optimal_order = result.x.argsort()
        return [self.checkpoints[i] for i in optimal_order]
    
    def execute_inspection(self, route):
        """Execute inspection"""
        for i, checkpoint in enumerate(route):
            print(f"Checkpoint {i+1}/{len(route)}")
            
            # Move to target position
            self.robot.move_to(checkpoint)
            
            # Perform checks
            anomalies = self.perform_checks()
            
            # Send alert if anomalies found
            if anomalies:
                self.send_alert(checkpoint, anomalies)
            
            # Upload data
            self.upload_inspection_data(checkpoint, anomalies)
    
    def perform_checks(self):
        """Perform multiple checks"""
        anomalies = []
        
        # 1. Visual check: shelf integrity
        image = self.robot.get_camera_image()
        shelf_damage = self.detect_shelf_damage(image)
        if shelf_damage:
            anomalies.append({"type": "shelf_damage", "detail": shelf_damage})
        
        # 2. Aisle obstruction detection
        lidar_scan = self.robot.get_lidar_scan()
        blocked = self.detect_path_obstruction(lidar_scan)
        if blocked:
            anomalies.append({"type": "path_blocked", "detail": blocked})
        
        # 3. Temperature detection
        thermal = self.robot.get_thermal_image()
        hot_spots = self.detect_hot_spots(thermal)
        if hot_spots:
            anomalies.append({"type": "high_temperature", "detail": hot_spots})
        
        # 4. Smoke detection
        smoke_detected = self.detect_smoke()
        if smoke_detected:
            anomalies.append({"type": "smoke", "detail": "Smoke detected"})
        
        return anomalies
    
    def detect_shelf_damage(self, image):
        """Detect shelf damage"""
        # Use YOLO to detect shelf deformation, collapse
        model = YOLO("shelf_damage.pt")
        results = model(image)
        
        damages = []
        for box in results[0].boxes:
            if box.conf > 0.7:
                damages.append({
                    "type": results[0].names[int(box.cls)],
                    "bbox": box.xyxy[0].tolist()
                })
        
        return damages if damages else None
    
    def detect_path_obstruction(self, lidar_scan):
        """Detect path obstruction"""
        # Analyze LiDAR data to detect obstacles
        obstacles = []
        for point in lidar_scan:
            if point["distance"] < 0.5:  # Distance less than 0.5m
                obstacles.append(point)
        
        if len(obstacles) > 10:  # More than 10 points considered blocked
            return {"obstacle_count": len(obstacles), "min_distance": min(p["distance"] for p in obstacles)}
        
        return None
    
    def detect_hot_spots(self, thermal_image):
        """Detect hot spots"""
        # Temperature above 50°C considered hot spot
        hot_pixels = np.where(thermal_image > 50)
        
        if len(hot_pixels[0]) > 100:
            return {
                "max_temp": np.max(thermal_image),
                "hot_area": len(hot_pixels[0])
            }
        
        return None
    
    def detect_smoke(self):
        """Detect smoke"""
        # Use smoke detection model
        image = self.robot.get_camera_image()
        model = YOLO("smoke_detection.pt")
        results = model(image)
        
        for box in results[0].boxes:
            if box.conf > 0.8:
                return True
        
        return False
    
    def send_alert(self, location, anomalies):
        """Send alert"""
        alert = {
            "robot_id": self.robot.id,
            "location": location,
            "timestamp": datetime.now().isoformat(),
            "anomalies": anomalies,
            "severity": self._compute_severity(anomalies)
        }
        
        # Send to monitoring platform
        requests.post("https://monitor.example.com/alert", json=alert)
        
        # Send SMS/email
        if alert["severity"] == "critical":
            self._send_sms(alert)
            self._send_email(alert)
    
    def _compute_severity(self, anomalies):
        """Compute severity level"""
        for anomaly in anomalies:
            if anomaly["type"] == "smoke":
                return "critical"
            elif anomaly["type"] == "high_temperature":
                return "warning"
        return "info"

Complete Code and Deployment

# 1. Clone project
git clone https://github.com/your-org/warehouse-inspection.git
cd warehouse-inspection

# 2. Install dependencies
pip install -r requirements.txt

# 3. Configure robot
cp config.example.yaml config.yaml
vim config.yaml  # Edit robot IP, inspection parameters, etc.

# 4. Run inspection program
python main.py --mode inspection

# 5. Start monitoring service
python main.py --mode monitor

Deployment Script

#!/bin/bash
# deploy.sh

# Build Docker image
docker build -t warehouse-inspection:latest .

# Push to image repository
docker push your-org/warehouse-inspection:latest

# Deploy on edge gateway
ssh edge-gateway "
    docker pull your-org/warehouse-inspection:latest
    docker-compose up -d
"

echo "Deployment complete"

FAQ

Are the B2 and Go2 SDKs interchangeable?

Yes, unitree_sdk2 supports both B2 and Go2, but some API parameters (such as payload compensation, gait parameters) need to be adjusted according to the model.

Can the B2’s battery life be extended?

Yes. The B2 supports hot-swappable batteries, allowing battery replacement without shutting down. Additionally, automatic recharging can be configured to return to the charging dock when battery drops below 20%.

How to achieve multi-B2 collaborative operation?

Use a multi-robot coordination framework with a central scheduling system to assign tasks. Each B2 communicates via ROS2 or custom protocols, sharing maps and task status.

Can the B2 work in outdoor恶劣 weather?

The B2’s IP67 protection rating allows it to work in rainy and dusty environments. However, extreme weather (heavy rain, heavy snow) still recommends suspending operations.

How to integrate with existing MES/WMS systems?

The B2 provides RESTful API and MQTT interfaces for integration with MES/WMS systems. OPC UA protocol can also be used for seamless integration with industrial systems.

Summary

The Unitree B2 industrial robot dog provides a new solution for industrial automation. Through this practical tutorial, you have mastered:

B2 vs Go2 selection comparison
SDK environment setup and PLC integration
Implementation of inspection, transport, and detection scenarios
Complete automated warehouse inspection project

Practice makes perfect. It is recommended to start with simple inspection scenarios and gradually expand to complex transport and multi-robot collaboration tasks.

Related Resources: