How to Build a Grass-Cutting Robot for 1-Acre Yard

#!/usr/bin/env python3
import rospy
from geometry_msgs.msg import Twist
from sensor_msgs.msg import NavSatFix
import numpy as np

class AutonomousMower:
    def __init__(self):
        rospy.init_node('autonomous_mower')
        
        # Publishers
        self.cmd_vel_pub = rospy.Publisher('/cmd_vel', Twist, queue_size=10)
        self.cutting_pub = rospy.Publisher('/cutting_motor', Bool, queue_size=1)
        
        # Subscribers
        rospy.Subscriber('/gps/fix', NavSatFix, self.gps_callback)
        rospy.Subscriber('/imu/data', Imu, self.imu_callback)
        
        # State variables
        self.current_position = None
        self.target_waypoints = []
        self.cutting_pattern = 'spiral'  # or 'parallel_lines'
        
    def generate_coverage_path(self, yard_boundary):
        """Generate efficient mowing pattern"""
        if self.cutting_pattern == 'spiral':
            return self.generate_spiral_pattern(yard_boundary)
        else:
            return self.generate_parallel_pattern(yard_boundary)
    
    def navigate_to_waypoint(self, target):
        """Move robot to specific GPS coordinate"""
        current = self.current_position
        
        # Calculate distance and bearing
        distance = self.calculate_distance(current, target)
        bearing = self.calculate_bearing(current, target)
        
        # Generate motor commands
        cmd = Twist()
        if distance > 0.5:  # meters
            cmd.linear.x = min(0.3, distance * 0.1)  # max 0.3 m/s
            cmd.angular.z = self.normalize_angle(bearing - self.current_heading)
        else:
            cmd.linear.x = 0
            cmd.angular.z = 0
            
        self.cmd_vel_pub.publish(cmd)
        
    def safety_check(self):
        """Continuous safety monitoring"""
        # Check for obstacles
        if self.obstacle_detected():
            self.emergency_stop()
            return False
            
        # Check battery level
        if self.battery_level < 20:
            self.return_to_dock()
            return False
            
        # Check weather conditions
        if self.is_raining() or self.grass_too_wet():
            self.suspend_operation()
            return False
            
        return True

import cv2
import numpy as np
from ultralytics import YOLO

class SafetyVision:
    def __init__(self):
        # Load pre-trained YOLO model
        self.model = YOLO('yolov8n.pt')
        self.safety_classes = ['person', 'dog', 'cat', 'bird']
        
    def detect_humans_pets(self, frame):
        """Detect humans and pets in camera frame"""
        results = self.model(frame)
        
        detected_objects = []
        for r in results:
            boxes = r.boxes
            for box in boxes:
                # Get class name
                cls = int(box.cls[0])
                class_name = self.model.names[cls]
                
                if class_name in self.safety_classes:
                    confidence = float(box.conf[0])
                    if confidence > 0.5:  # High confidence threshold
                        detected_objects.append({
                            'class': class_name,
                            'confidence': confidence,
                            'bbox': box.xyxy[0].tolist()
                        })
        
        return detected_objects
    
    def calculate_safe_distance(self, objects):
        """Calculate if robot should stop"""
        for obj in objects:
            # Estimate distance from bounding box size
            bbox_area = self.get_bbox_area(obj['bbox'])
            estimated_distance = 1000 / np.sqrt(bbox_area)  # approximation
            
            if estimated_distance < 3.0:  # Stop if within 3 meters
                return False, f"Safety stop: {obj['class']} detected"
                
        return True, "Clear to proceed"