nav2-humanoid-locomotion
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 05 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 04 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 04 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 03 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 03 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 03 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 02 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 02 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 01 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 01 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 00 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 00 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 07 MINUTES 00 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 59 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 59 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 58 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 58 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 57 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 57 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 56 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 56 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 55 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
# python/humanoid_nav2_components.py
import rclpy
from rclpy.node import Node
from nav_msgs.msg import Path, OccupancyGrid, Odometry
from geometry_msgs.msg import PoseStamped, Twist
from sensor_msgs.msg import LaserScan, PointCloud2
from std_msgs.msg import String
from tf2_ros import TransformListener, Buffer
import tf2_geometry_msgs
import numpy as np
from scipy.spatial.transform import Rotation as R
import math
class HumanoidNav2Node(Node):
def __init__(self):
super().__init__('humanoid_nav2_node')
# TF2 setup
self.tf_buffer = Buffer()
self.tf_listener = TransformListener(self.tf_buffer, self)
# Publishers and subscribers
self.global_plan_pub = self.create_publisher(Path, '/humanoid/global_plan', 10)
self.local_plan_pub = self.create_publisher(Path, '/humanoid/local_plan', 10)
self.cmd_vel_pub = self.create_publisher(Twist, '/humanoid/cmd_vel', 10)
self.status_pub = self.create_publisher(String, '/humanoid/nav_status', 10)
self.odom_sub = self.create_subscription(
Odometry,
'/odom',
self.odom_callback,
10
)
self.goal_sub = self.create_subscription(
PoseStamped,
'/humanoid/goal',
self.goal_callback,
10
)
self.scan_sub = self.create_subscription(
LaserScan,
'/scan',
self.scan_callback,
10
)
# Humanoid-specific state
self.current_pose = None
self.current_velocity = None
self.goal_pose = None
self.navigation_state = "IDLE" # IDLE, PLANNING, EXECUTING, RECOVERY
self.path = []
self.current_waypoint = 0
# Humanoid locomotion parameters
self.step_length = 0.3 # meters
self.step_width = 0.2 # meters
self.step_height = 0.05 # meters (for stepping over small obstacles)
self.max_step_up = 0.1 # maximum step up height
self.max_step_down = 0.1 # maximum step down height
self.turn_angle = 0.2 # radians per step for turning
# Navigation parameters
self.linear_vel = 0.1 # m/s
self.angular_vel = 0.2 # rad/s
self.arrival_threshold = 0.2 # meters
self.yaw_threshold = 0.1 # radians
# Path planning components
self.global_planner = HumanoidGlobalPlanner()
self.local_planner = HumanoidLocalPlanner()
self.footstep_planner = FootstepPlanner()
self.get_logger().info("Humanoid Nav2 Node initialized")
def odom_callback(self, msg):
"""Update current pose from odometry"""
self.current_pose = msg.pose.pose
self.current_velocity = msg.twist.twist
def goal_callback(self, msg):
"""Handle new navigation goal"""
self.goal_pose = msg.pose
# Check if we have current pose
if self.current_pose is not None:
self.navigation_state = "PLANNING"
self.plan_path()
def scan_callback(self, msg):
"""Process laser scan for local planning"""
if self.navigation_state == "EXECUTING":
# Update local plan based on obstacles
self.update_local_plan(msg)
def plan_path(self):
"""Plan global path to goal"""
if self.current_pose is None or self.goal_pose is None:
return
# Convert poses to numpy arrays for planning
start = np.array([
self.current_pose.position.x,
self.current_pose.position.y,
self.get_yaw_from_quaternion(self.current_pose.orientation)
])
goal = np.array([
self.goal_pose.position.x,
self.goal_pose.position.y,
self.get_yaw_from_quaternion(self.goal_pose.orientation)
])
# Plan global path
global_path = self.global_planner.plan(start, goal)
if global_path is not None:
# Convert path to footstep plan
footstep_plan = self.footstep_planner.plan_footsteps(global_path)
# Publish global plan
self.publish_global_plan(footstep_plan)
# Start execution
self.path = footstep_plan
self.current_waypoint = 0
self.navigation_state = "EXECUTING"
# Start executing the plan
self.execute_path()
def update_local_plan(self, scan_msg):
"""Update local plan based on sensor data"""
if self.navigation_state != "EXECUTING":
return
# Check for obstacles in current path
obstacles = self.process_scan_for_obstacles(scan_msg)
if self.local_planner.need_replanning(obstacles, self.path, self.current_waypoint):
self.get_logger().info("Replanning local path due to obstacles")
# Create temporary goal at current position + lookahead
current_pos = self.get_current_position()
lookahead_goal = self.get_lookahead_goal(current_pos)
local_path = self.local_planner.plan_local(
current_pos, lookahead_goal, obstacles
)
if local_path is not None:
self.path = local_path
self.current_waypoint = 0
def execute_path(self):
"""Execute the planned path"""
if not self.path or self.current_waypoint >= len(self.path):
self.navigation_state = "IDLE"
self.publish_status("GOAL_REACHED")
return
# Get next waypoint
target_waypoint = self.path[self.current_waypoint]
# Calculate required motion
cmd_vel = self.calculate_motion_to_waypoint(target_waypoint)
# Check if reached waypoint
if self.is_at_waypoint(target_waypoint):
self.current_waypoint += 1
if self.current_waypoint >= len(self.path):
self.navigation_state = "IDLE"
self.publish_status("GOAL_REACHED")
return
# Publish command
self.cmd_vel_pub.publish(cmd_vel)
def calculate_motion_to_waypoint(self, waypoint):
"""Calculate motion command to reach waypoint"""
cmd = Twist()
if self.current_pose is None:
return cmd
# Calculate distance and angle to waypoint
dx = waypoint[0] - self.current_pose.position.x
dy = waypoint[1] - self.current_pose.position.y
distance = math.sqrt(dx*dx + dy*dy)
# Calculate desired heading
desired_yaw = math.atan2(dy, dx)
current_yaw = self.get_yaw_from_quaternion(self.current_pose.orientation)
# Calculate angular error
angle_error = self.normalize_angle(desired_yaw - current_yaw)
# Set velocities based on errors
if distance > self.arrival_threshold:
cmd.linear.x = min(self.linear_vel, distance * 2.0) # Proportional control
else:
cmd.linear.x = 0.0
if abs(angle_error) > self.yaw_threshold:
cmd.angular.z = max(-self.angular_vel, min(self.angular_vel, angle_error * 2.0))
else:
cmd.angular.z = 0.0
return cmd
def is_at_waypoint(self, waypoint):
"""Check if robot is at the specified waypoint"""
if self.current_pose is None:
return False
dx = waypoint[0] - self.current_pose.position.x
dy = waypoint[1] - self.current_pose.position.y
distance = math.sqrt(dx*dx + dy*dy)
return distance <= self.arrival_threshold
def get_yaw_from_quaternion(self, quat):
"""Extract yaw angle from quaternion"""
siny_cosp = 2 * (quat.w * quat.z + quat.x * quat.y)
cosy_cosp = 1 - 2 * (quat.y * quat.y + quat.z * quat.z)
return math.atan2(siny_cosp, cosy_cosp)
def normalize_angle(self, angle):
"""Normalize angle to [-pi, pi] range"""
while angle > math.pi:
angle -= 2.0 * math.pi
while angle < -math.pi:
angle += 2.0 * math.pi
return angle
def get_current_position(self):
"""Get current position as numpy array"""
if self.current_pose is not None:
return np.array([
self.current_pose.position.x,
self.current_pose.position.y
])
return np.array([0.0, 0.0])
def get_lookahead_goal(self, current_pos):
"""Get goal position for local planning"""
if self.current_waypoint < len(self.path):
return np.array(self.path[self.current_waypoint][:2])
elif self.goal_pose is not None:
return np.array([
self.goal_pose.position.x,
self.goal_pose.position.y
])
return current_pos
def process_scan_for_obstacles(self, scan_msg):
"""Process laser scan to detect obstacles"""
obstacles = []
angle_min = scan_msg.angle_min
angle_increment = scan_msg.angle_increment
for i, range_val in enumerate(scan_msg.ranges):
if not (math.isnan(range_val) or math.isinf(range_val)) and range_val < 1.0: # Within 1m
angle = angle_min + i * angle_increment
x = range_val * math.cos(angle)
y = range_val * math.sin(angle)
obstacles.append((x, y, range_val))
return obstacles
def publish_global_plan(self, path):
"""Publish global path for visualization"""
path_msg = Path()
path_msg.header.stamp = self.get_clock().now().to_msg()
path_msg.header.frame_id = "map"
for point in path:
pose = PoseStamped()
pose.pose.position.x = point[0]
pose.pose.position.y = point[1]
pose.pose.position.z = 0.0
path_msg.poses.append(pose)
self.global_plan_pub.publish(path_msg)
def publish_status(self, status):
"""Publish navigation status"""
status_msg = String()
status_msg.data = status
self.status_pub.publish(status_msg)
class HumanoidGlobalPlanner:
"""Global planner adapted for humanoid robots"""
def __init__(self):
# Initialize global planner (e.g., A* or Dijkstra)
pass
def plan(self, start, goal):
"""Plan global path from start to goal"""
# For humanoid robots, we might use a grid-based planner
# that considers walkable areas and step constraints
path = self.a_star_plan(start, goal)
return path
def a_star_plan(self, start, goal):
"""A* path planning algorithm"""
# Simplified A* implementation
# In practice, this would be more complex and consider humanoid constraints
path = [start, goal] # Simplified for example
return path
class HumanoidLocalPlanner:
"""Local planner for humanoid robots"""
def __init__(self):
# Initialize local planner (e.g., DWA or TEB)
pass
def plan_local(self, current_pos, goal_pos, obstacles):
"""Plan local path considering obstacles"""
# For humanoid robots, consider step-by-step planning
# that accounts for balance and foot placement
local_path = [current_pos, goal_pos] # Simplified
return local_path
def need_replanning(self, obstacles, path, current_waypoint):
"""Check if replanning is needed"""
# Check if obstacles block current path
for obs_x, obs_y, obs_dist in obstacles:
if obs_dist < 0.5: # Within 50cm
return True
return False
class FootstepPlanner:
"""Plan footstep sequences for humanoid navigation"""
def __init__(self):
self.step_length = 0.3
self.step_width = 0.2
self.max_turn = 0.3 # radians
def plan_footsteps(self, path):
"""Convert path to footstep plan"""
footsteps = []
if len(path) < 2:
return footsteps
# Convert path to footstep sequence
# This is a simplified approach - real implementation would be more complex
for i in range(len(path) - 1):
start = path[i]
end = path[i + 1]
# Calculate intermediate footsteps
dist = math.sqrt((end[0] - start[0])**2 + (end[1] - start[1])**2)
steps_needed = max(1, int(dist / self.step_length))
for j in range(steps_needed):
ratio = j / steps_needed
x = start[0] + ratio * (end[0] - start[0])
y = start[1] + ratio * (end[1] - start[1])
theta = start[2] if len(start) > 2 else 0.0
footsteps.append([x, y, theta])
return footsteps
def main(args=None):
rclpy.init(args=args)
nav2_node = HumanoidNav2Node()
try:
rclpy.spin(nav2_node)
except KeyboardInterrupt:
pass
finally:
nav2_node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 55 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 55 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
# python/footstep_planning.py
import numpy as np
import math
from scipy.spatial.distance import euclidean
import matplotlib.pyplot as plt
class FootstepPlanner:
def __init__(self, step_length=0.3, step_width=0.2, step_height=0.05):
self.step_length = step_length
self.step_width = step_width
self.step_height = step_height
# Support polygon parameters
self.foot_separation = step_width
self.foot_length = 0.25
self.foot_width = 0.15
def plan_footsteps(self, start_pose, goal_pose, terrain_map=None):
"""
Plan footsteps from start to goal considering terrain
start_pose: [x, y, theta]
goal_pose: [x, y, theta]
"""
footsteps = []
# Calculate straight-line path
dx = goal_pose[0] - start_pose[0]
dy = goal_pose[1] - start_pose[1]
distance = math.sqrt(dx*dx + dy*dy)
goal_theta = goal_pose[2] if len(goal_pose) > 2 else 0.0
# Determine number of steps needed
num_steps = int(distance / self.step_length) + 1
# Generate footsteps along the path
for i in range(1, num_steps + 1):
ratio = i / num_steps
x = start_pose[0] + ratio * dx
y = start_pose[1] + ratio * dy
theta = start_pose[2] + ratio * (goal_theta - start_pose[2])
# Add slight variations for natural walking
step = [x, y, theta]
footsteps.append(step)
# Add final goal step
footsteps.append(goal_pose)
return footsteps
def plan_bipedal_sequence(self, footsteps):
"""
Convert footsteps to alternating left/right foot sequence
"""
sequence = []
if not footsteps:
return sequence
# Start with left foot
left_support = True
for i, step in enumerate(footsteps):
if left_support:
# Left foot moves, right stays in place
sequence.append({
'step_type': 'left',
'position': step[:2],
'orientation': step[2] if len(step) > 2 else 0.0,
'step_number': i
})
left_support = False
else:
# Right foot moves, left stays in place
sequence.append({
'step_type': 'right',
'position': step[:2],
'orientation': step[2] if len(step) > 2 else 0.0,
'step_number': i
})
left_support = True
return sequence
def check_stability(self, footsteps, com_trajectory=None):
"""
Check if the footstep sequence maintains stability
"""
if len(footsteps) < 2:
return True
# Calculate support polygon for each step
for i in range(len(footsteps) - 1):
left_pos = self.get_left_foot_position(footsteps[i])
right_pos = self.get_right_foot_position(footsteps[i])
# Calculate support polygon (simplified as line between feet)
support_polygon = [left_pos, right_pos]
# Check if next step is within support polygon
next_pos = footsteps[i + 1][:2]
if not self.is_in_support_polygon(next_pos, support_polygon):
return False
return True
def get_left_foot_position(self, step):
"""Get left foot position based on step"""
x, y, theta = step[0], step[1], step[2]
# Offset for left foot (simplified)
offset_x = -self.foot_separation/2 * math.sin(theta)
offset_y = self.foot_separation/2 * math.cos(theta)
return [x + offset_x, y + offset_y]
def get_right_foot_position(self, step):
"""Get right foot position based on step"""
x, y, theta = step[0], step[1], step[2]
# Offset for right foot (simplified)
offset_x = self.foot_separation/2 * math.sin(theta)
offset_y = -self.foot_separation/2 * math.cos(theta)
return [x + offset_x, y + offset_y]
def is_in_support_polygon(self, point, polygon):
"""Check if point is in support polygon (simplified)"""
# Simplified check - in reality, this would be more complex
if len(polygon) < 2:
return False
# Calculate distance to closest point in polygon
min_dist = float('inf')
for p in polygon:
dist = euclidean(point, p)
if dist < min_dist:
min_dist = dist
# Consider stable if within step distance
return min_dist <= self.step_length
class AdvancedFootstepPlanner(FootstepPlanner):
"""Advanced footstep planner with stability and terrain considerations"""
def __init__(self, step_length=0.3, step_width=0.2):
super().__init__(step_length, step_width)
self.max_step_up = 0.1
self.max_step_down = 0.15
self.max_com_velocity = 0.5 # m/s
def plan_with_terrain(self, start_pose, goal_pose, height_map, obstacles=None):
"""
Plan footsteps considering terrain elevation and obstacles
"""
# Use A* or RRT for terrain-aware planning
path = self.terrain_aware_search(start_pose, goal_pose, height_map, obstacles)
# Smooth the path and generate footsteps
footsteps = self.smooth_path_to_footsteps(path, height_map)
return footsteps
def terrain_aware_search(self, start, goal, height_map, obstacles):
"""Search for valid path considering terrain constraints"""
# Simplified implementation - in practice, use proper path planning
path = [start, goal]
return path
def smooth_path_to_footsteps(self, path, height_map):
"""Convert path to stable footsteps considering terrain"""
footsteps = []
for i in range(len(path) - 1):
start = path[i]
end = path[i + 1]
# Calculate intermediate steps based on terrain
step = self.calculate_terrain_aware_step(start, end, height_map)
footsteps.append(step)
return footsteps
def calculate_terrain_aware_step(self, start, end, height_map):
"""Calculate step considering terrain constraints"""
# Check elevation change
start_height = self.get_terrain_height(start[:2], height_map)
end_height = self.get_terrain_height(end[:2], height_map)
height_diff = abs(end_height - start_height)
if height_diff > self.max_step_up:
# Need to find alternative path or stop
# For now, return the direct step
pass
return end
def get_terrain_height(self, position, height_map):
"""Get terrain height at position"""
# Simplified - in practice, interpolate from height map
return 0.0 # Default flat terrain
def visualize_footsteps(footsteps, sequence=None):
"""Visualize planned footsteps"""
if not footsteps:
return
fig, ax = plt.subplots(figsize=(10, 8))
# Extract x, y coordinates
x_coords = [step[0] for step in footsteps]
y_coords = [step[1] for step in footsteps]
ax.plot(x_coords, y_coords, 'b-', linewidth=2, label='Planned Path')
ax.scatter(x_coords, y_coords, c='red', s=50, zorder=5, label='Footsteps')
# Mark start and end
ax.scatter(x_coords[0], y_coords[0], c='green', s=100, zorder=6, label='Start')
ax.scatter(x_coords[-1], y_coords[-1], c='red', s=100, zorder=6, label='Goal')
# Draw foot shapes if sequence is provided
if sequence:
for step in sequence:
x, y = step['position'][0], step['position'][1]
theta = step['orientation']
# Draw simple foot shape
foot_length = 0.15
foot_width = 0.07
# Calculate foot corners
cos_theta = math.cos(theta)
sin_theta = math.sin(theta)
corners_x = []
corners_y = []
for dx, dy in [(-foot_length/2, -foot_width/2),
(foot_length/2, -foot_width/2),
(foot_length/2, foot_width/2),
(-foot_length/2, foot_width/2),
(-foot_length/2, -foot_width/2)]:
x_corner = x + dx * cos_theta - dy * sin_theta
y_corner = y + dx * sin_theta + dy * cos_theta
corners_x.append(x_corner)
corners_y.append(y_corner)
color = 'blue' if step['step_type'] == 'left' else 'orange'
ax.plot(corners_x, corners_y, color=color, linewidth=1)
ax.set_xlabel('X (m)')
ax.set_ylabel('Y (m)')
ax.set_title('Footstep Planning for Humanoid Navigation')
ax.grid(True, alpha=0.3)
ax.legend()
ax.axis('equal')
plt.tight_layout()
plt.show()
# Example usage
def example_footstep_planning():
planner = AdvancedFootstepPlanner()
# Define start and goal
start_pose = [0.0, 0.0, 0.0] # x, y, theta
goal_pose = [3.0, 2.0, math.pi/4]
# Plan footsteps
footsteps = planner.plan_footsteps(start_pose, goal_pose)
sequence = planner.plan_bipedal_sequence(footsteps)
# Check stability
is_stable = planner.check_stability(footsteps)
print(f"Footstep sequence is stable: {is_stable}")
# Visualize
visualize_footsteps(footsteps, sequence)
return footsteps, sequence
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 54 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
# python/whole_body_motion_planning.py
import numpy as np
import math
from scipy.spatial.transform import Rotation as R
from scipy.optimize import minimize
import casadi as cs
class WholeBodyMotionPlanner:
def __init__(self):
self.robot_params = {
'height': 1.5, # m
'weight': 60.0, # kg
'com_height': 0.8, # m (height of center of mass)
'foot_size': [0.25, 0.15] # length, width
}
self.motion_constraints = {
'max_velocity': 0.5, # m/s
'max_angular_velocity': 0.5, # rad/s
'max_acceleration': 1.0, # m/s^2
'max_angular_acceleration': 1.0 # rad/s^2
}
def plan_whole_body_motion(self, footsteps, start_state, goal_state):
"""
Plan whole-body motion to execute footsteps while maintaining balance
"""
# Optimize center of mass trajectory to match footsteps
com_trajectory = self.optimize_com_trajectory(footsteps, start_state, goal_state)
# Generate joint trajectories
joint_trajectories = self.inverse_kinematics(com_trajectory, footsteps)
# Generate balance control commands
balance_commands = self.generate_balance_control(com_trajectory)
return {
'com_trajectory': com_trajectory,
'joint_trajectories': joint_trajectories,
'balance_commands': balance_commands
}
def optimize_com_trajectory(self, footsteps, start_state, goal_state):
"""
Optimize center of mass trajectory for stable locomotion
"""
# Use preview control or other methods to generate CoM trajectory
# that ensures ZMP (Zero Moment Point) stays within support polygon
# Simplified approach: generate CoM trajectory that follows footsteps
# with appropriate smoothing for balance
com_trajectory = []
for i, step in enumerate(footsteps):
# Calculate desired CoM position based on step location
# and support polygon
com_x = step[0]
com_y = step[1]
com_z = self.robot_params['com_height'] # Keep CoM at constant height
# Add time parameter
time = i * 0.5 # Assume 0.5s per step
com_trajectory.append([time, com_x, com_y, com_z])
return com_trajectory
def inverse_kinematics(self, com_trajectory, footsteps):
"""
Calculate joint angles to achieve desired CoM position and foot placement
"""
joint_trajectories = []
for t, com_pos in enumerate(com_trajectory):
# Calculate required joint angles using inverse kinematics
# This is a simplified approach - real implementation would use
# full kinematic model of the humanoid
joint_angles = self.calculate_joint_angles(com_pos, footsteps, t)
joint_trajectories.append(joint_angles)
return joint_trajectories
def calculate_joint_angles(self, com_pos, footsteps, time_idx):
"""
Calculate joint angles for given CoM position
"""
# Simplified joint angle calculation
# In reality, this would solve the full inverse kinematics problem
joints = {
'left_hip': [0.0, 0.0, 0.0], # [roll, pitch, yaw]
'left_knee': [0.0], # [flexion]
'left_ankle': [0.0, 0.0], # [pitch, roll]
'right_hip': [0.0, 0.0, 0.0],
'right_knee': [0.0],
'right_ankle': [0.0, 0.0],
'left_shoulder': [0.0, 0.0, 0.0],
'left_elbow': [0.0],
'right_shoulder': [0.0, 0.0, 0.0],
'right_elbow': [0.0],
'torso': [0.0, 0.0, 0.0]
}
return joints
def generate_balance_control(self, com_trajectory):
"""
Generate balance control commands to maintain stability
"""
balance_commands = []
for i in range(1, len(com_trajectory)):
current_com = np.array(com_trajectory[i][1:4]) # x, y, z
prev_com = np.array(com_trajectory[i-1][1:4])
# Calculate CoM velocity
dt = com_trajectory[i][0] - com_trajectory[i-1][0]
if dt > 0:
com_velocity = (current_com - prev_com) / dt
else:
com_velocity = np.array([0.0, 0.0, 0.0])
# Generate balance command based on CoM state
balance_cmd = self.calculate_balance_command(current_com, com_velocity)
balance_commands.append(balance_cmd)
return balance_commands
def calculate_balance_command(self, com_pos, com_vel):
"""
Calculate balance command using inverted pendulum model
"""
# Simplified balance control using linear inverted pendulum model
# (LIPM) - in reality, this would be more complex
# Desired CoM position (based on support polygon)
desired_com_x = com_pos[0] # Simplified
desired_com_y = com_pos[1]
# Calculate error
x_error = desired_com_x - com_pos[0]
y_error = desired_com_y - com_pos[1]
# Simple PD control
kp = 10.0 # Proportional gain
kd = 2.0 # Derivative gain (velocity feedback)
x_control = kp * x_error - kd * com_vel[0]
y_control = kp * y_error - kd * com_vel[1]
return [x_control, y_control]
class PreviewController:
"""
Implement preview control for humanoid balance during locomotion
"""
def __init__(self, zmp_delay=0.05, preview_window=1.0):
self.zmp_delay = zmp_delay
self.preview_window = preview_window
self.gravity = 9.81
self.com_height = 0.8 # m
# Calculate omega for LIPM
self.omega = math.sqrt(self.gravity / self.com_height)
def calculate_com_reference(self, zmp_trajectory):
"""
Calculate CoM reference trajectory from ZMP trajectory using preview control
"""
# Implement preview control algorithm
# This ensures ZMP follows desired trajectory while maintaining stability
com_reference = []
for i, (t, zmp_x, zmp_y) in enumerate(zmp_trajectory):
# Calculate reference CoM position using preview control
# This is a simplified implementation
com_x = zmp_x # Simplified - in reality, would use full preview control
com_y = zmp_y
com_z = self.com_height
com_reference.append([t, com_x, com_y, com_z])
return com_reference
def generate_zmp_trajectory(self, footsteps):
"""
Generate ZMP trajectory from footsteps
"""
zmp_trajectory = []
for i, step in enumerate(footsteps):
# Calculate ZMP based on foot placement and timing
time = i * 0.5 # Assume 0.5s per step
zmp_x = step[0]
zmp_y = step[1]
zmp_trajectory.append([time, zmp_x, zmp_y])
return zmp_trajectory
def create_balanced_locomotion_plan(footsteps, start_state, goal_state):
"""
Create a complete locomotion plan with balance considerations
"""
# Initialize planners
wb_planner = WholeBodyMotionPlanner()
preview_ctrl = PreviewController()
# Generate ZMP trajectory from footsteps
zmp_trajectory = preview_ctrl.generate_zmp_trajectory(footsteps)
# Calculate CoM reference using preview control
com_reference = preview_ctrl.calculate_com_reference(zmp_trajectory)
# Plan whole body motion
motion_plan = wb_planner.plan_whole_body_motion(
footsteps, start_state, goal_state
)
return {
'footsteps': footsteps,
'zmp_trajectory': zmp_trajectory,
'com_reference': com_reference,
'whole_body_plan': motion_plan
}
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 54 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 53 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
# python/balance_control.py
import numpy as np
import math
from scipy.integrate import odeint
from scipy.linalg import solve_continuous_are
from scipy import signal
class BalanceController:
def __init__(self, robot_height=0.8, control_frequency=100):
self.com_height = robot_height # Height of center of mass
self.control_frequency = control_frequency
self.gravity = 9.81
# State: [x, y, x_dot, y_dot] - CoM position and velocity
self.state = np.zeros(4)
# Calculate LQR gains for inverted pendulum
self.A = np.array([
[0, 0, 1, 0],
[0, 0, 0, 1],
[self.gravity/self.com_height, 0, 0, 0],
[0, self.gravity/self.com_height, 0, 0]
])
# Control input matrix (how control affects acceleration)
self.B = np.array([
[0, 0],
[0, 0],
[-self.gravity/self.com_height, 0],
[0, -self.gravity/self.com_height]
])
# State cost matrix Q (penalizes state deviations)
self.Q = np.diag([100, 100, 10, 10]) # [x_pos, y_pos, x_vel, y_vel]
# Control cost matrix R (penalizes control effort)
self.R = np.diag([1, 1]) # [x_control, y_control]
# Calculate LQR gain matrix
self.K = self.calculate_lqr_gain()
def calculate_lqr_gain(self):
"""Calculate LQR gain matrix for inverted pendulum control"""
# Solve continuous-time algebraic Riccati equation
P = solve_continuous_are(self.A, self.B, self.Q, self.R)
# LQR gain: u = -K*x
K = np.linalg.inv(self.R) @ self.B.T @ P
return K
def update_balance_control(self, current_state, desired_state):
"""
Update balance control based on current and desired state
current_state: [com_x, com_y, com_x_dot, com_y_dot]
desired_state: [des_x, des_y, des_x_dot, des_y_dot]
"""
# Calculate state error
state_error = desired_state - current_state
# Apply LQR control law: u = -K*(x - x_desired)
control_output = -self.K @ state_error
# Control output represents desired ZMP displacement
zmp_dx, zmp_dy = control_output
return zmp_dx, zmp_dy
def integrate_dynamics(self, state, t, zmp_x, zmp_y):
"""
Integrate inverted pendulum dynamics
state: [x, y, x_dot, y_dot]
"""
x, y, x_dot, y_dot = state
# Inverted pendulum dynamics
# x_ddot = g/h * (x - zmp_x)
# y_ddot = g/h * (y - zmp_y)
x_ddot = self.gravity/self.com_height * (x - zmp_x)
y_ddot = self.gravity/self.com_height * (y - zmp_y)
return [x_dot, y_dot, x_ddot, y_ddot]
def simulate_balance(self, initial_state, zmp_trajectory, dt=0.01):
"""
Simulate balance control over time
"""
states = [initial_state]
times = [0.0]
current_state = initial_state.copy()
for t in np.arange(0, len(zmp_trajectory)*dt, dt):
# Get desired ZMP for current time
idx = min(int(t/dt), len(zmp_trajectory)-1)
desired_zmp_x, desired_zmp_y = zmp_trajectory[idx]
# Calculate control (simplified - in reality, would use full state feedback)
state_error = current_state - np.array([desired_zmp_x, desired_zmp_y, 0, 0])
control = -self.K @ state_error
# Apply control to ZMP
actual_zmp_x = desired_zmp_x + control[0]
actual_zmp_y = desired_zmp_y + control[1]
# Integrate dynamics
derivatives = self.integrate_dynamics(
current_state, t, actual_zmp_x, actual_zmp_y
)
# Update state (Euler integration)
new_state = current_state + np.array(derivatives) * dt
current_state = new_state
states.append(new_state.copy())
times.append(t + dt)
return np.array(times), np.array(states)
class CapturePointController:
"""
Capture Point based balance control for humanoid robots
"""
def __init__(self, com_height=0.8, control_frequency=200):
self.com_height = com_height
self.control_frequency = control_frequency
self.gravity = 9.81
self.omega = math.sqrt(self.gravity / self.com_height)
def calculate_capture_point(self, com_pos, com_vel):
"""
Calculate capture point from CoM position and velocity
Capture point = CoM position + CoM velocity / omega
"""
cp_x = com_pos[0] + com_vel[0] / self.omega
cp_y = com_pos[1] + com_vel[1] / self.omega
return np.array([cp_x, cp_y])
def calculate_foot_placement(self, capture_point, current_foot_pos):
"""
Calculate required foot placement to capture the current state
"""
# For simplicity, move foot towards capture point
# In reality, this would consider step constraints and timing
step_vector = capture_point - current_foot_pos
max_step_length = 0.3 # Maximum step length
if np.linalg.norm(step_vector) > max_step_length:
step_vector = step_vector / np.linalg.norm(step_vector) * max_step_length
new_foot_pos = current_foot_pos + step_vector
return new_foot_pos
def balance_control_step(self, com_state, support_foot_pos):
"""
Perform one step of capture point based balance control
com_state: [x, y, z, x_dot, y_dot, z_dot]
support_foot_pos: [x, y] of current support foot
"""
com_pos = com_state[:3]
com_vel = com_state[3:]
# Calculate capture point
cp = self.calculate_capture_point(com_pos[:2], com_vel[:2])
# Determine if step is needed
foot_to_cp = np.linalg.norm(cp - support_foot_pos)
stability_threshold = 0.1 # Start planning step when CP is 10cm from foot
step_needed = foot_to_cp > stability_threshold
return {
'capture_point': cp,
'distance_to_foot': foot_to_cp,
'step_needed': step_needed,
'target_foot_placement': self.calculate_foot_placement(cp, support_foot_pos) if step_needed else support_foot_pos
}
class PendulumController:
"""
Linear Inverted Pendulum Mode (LIPM) controller
"""
def __init__(self, com_height=0.8):
self.com_height = com_height
self.gravity = 9.81
self.omega = math.sqrt(self.gravity / self.com_height)
# Discrete time model parameters
self.dt = 0.005 # 200Hz control rate
def discrete_dynamics(self, x_k, zmp_k):
"""
Discrete time inverted pendulum dynamics
x_k = [com_x, com_x_dot] at time k
zmp_k = desired ZMP at time k
"""
# State transition matrix for discrete LIPM
A_d = np.array([
[np.cosh(self.omega * self.dt), (1/self.omega) * np.sinh(self.omega * self.dt)],
[self.omega * np.sinh(self.omega * self.dt), np.cosh(self.omega * self.dt)]
])
# Input matrix
B_d = np.array([
[1 - np.cosh(self.omega * self.dt)],
[-self.omega * np.sinh(self.omega * self.dt)]
])
# Next state
x_k1 = A_d @ x_k + B_d * zmp_k
return x_k1
def mpc_balance_control(self, current_state, reference_trajectory, horizon=20):
"""
Model Predictive Control for balance using LIPM
"""
# Simplified MPC implementation
# In reality, this would solve a constrained optimization problem
predicted_states = []
control_inputs = []
current_x = current_state.copy()
for k in range(horizon):
# Get reference for this step
if k < len(reference_trajectory):
ref_state = reference_trajectory[k]
else:
ref_state = reference_trajectory[-1] # Hold last reference
# Simple control law (in reality, would solve MPC optimization)
zmp_ref = ref_state[0] # Reference ZMP
current_zmp = current_x[0] # Current CoM position
# Proportional control to track reference
zmp_cmd = zmp_ref + 0.1 * (current_zmp - ref_state[0])
# Apply dynamics
next_x = self.discrete_dynamics(current_x, zmp_cmd)
predicted_states.append(next_x.copy())
control_inputs.append(zmp_cmd)
current_x = next_x
# Return first control input
return control_inputs[0] if control_inputs else 0.0
def demonstrate_balance_control():
"""
Demonstrate different balance control approaches
"""
print("Demonstrating Balance Control Approaches")
# Initialize controllers
lqr_controller = BalanceController()
cp_controller = CapturePointController()
lipm_controller = PendulumController()
# Example CoM state [x, y, x_dot, y_dot]
com_state = np.array([0.05, 0.02, 0.1, -0.05]) # Slightly perturbed
# Example desired state
desired_state = np.array([0.0, 0.0, 0.0, 0.0]) # At equilibrium
# Calculate LQR control
zmp_dx, zmp_dy = lqr_controller.update_balance_control(com_state, desired_state)
print(f"LQR Control - ZMP adjustment: ({zmp_dx:.3f}, {zmp_dy:.3f})")
# Example CoM state for capture point controller [x, y, z, x_dot, y_dot, z_dot]
full_com_state = np.array([0.05, 0.02, 0.8, 0.1, -0.05, 0.0])
support_foot = np.array([0.0, 0.0])
cp_result = cp_controller.balance_control_step(full_com_state, support_foot)
print(f"Capture Point: ({cp_result['capture_point'][0]:.3f}, {cp_result['capture_point'][1]:.3f})")
print(f"Step needed: {cp_result['step_needed']}")
return lqr_controller, cp_controller, lipm_controller
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 53 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 52 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
# python/humanoid_behavior_trees.py
import py_trees
import py_trees_ros
import rclpy
from rclpy.node import Node
from geometry_msgs.msg import PoseStamped
from nav_msgs.msg import Path
from std_msgs.msg import String
import time
class HumanoidNavigateToPoseAction(py_trees_ros.actions.ActionClient):
"""
Custom action client for humanoid navigation to pose
"""
def __init__(self, name, action_type, action_name, goal):
super().__init__(name=name, action_type=action_type, action_name=action_name, goal=goal)
self.feedback_message = "initialised"
def update(self):
# Check if action server is available
if not self.action_client.server_is_ready():
return py_trees.Status.RUNNING
# Send goal if not already sent
if self.sent_goal is False:
self.send_goal()
return py_trees.Status.RUNNING
# Check goal status
if self.goal_handle.status == GoalStatus.STATUS_SUCCEEDED:
self.feedback_message = "arrived at goal"
return py_trees.Status.SUCCESS
elif self.goal_handle.status == GoalStatus.STATUS_EXECUTING:
self.feedback_message = "moving to goal"
return py_trees.Status.RUNNING
else:
self.feedback_message = "navigation failed"
return py_trees.Status.FAILURE
class CheckFootSupport(py_trees.behaviour.Behaviour):
"""
Check if the robot has proper foot support before navigation
"""
def __init__(self, name):
super().__init__(name)
self.blackboard = py_trees.blackboard.Blackboard()
def setup(self, **kwargs):
# Initialize any required resources
pass
def update(self):
# Check if robot has stable foot support
# This would interface with robot state
has_support = self.check_robot_support()
if has_support:
return py_trees.Status.SUCCESS
else:
return py_trees.Status.FAILURE
def check_robot_support(self):
# Simulate checking for foot support
# In reality, this would check robot's balance state
return True # Assume stable for example
class PlanFootsteps(py_trees.behaviour.Behaviour):
"""
Plan safe footsteps to the goal
"""
def __init__(self, name):
super().__init__(name)
self.blackboard = py_trees.blackboard.Blackboard()
def setup(self, **kwargs):
# Initialize footstep planner
self.footstep_planner = FootstepPlanner()
def update(self):
# Get goal from blackboard
goal = self.blackboard.get("navigation_goal")
if goal is None:
return py_trees.Status.FAILURE
# Plan footsteps
start_pose = [0.0, 0.0, 0.0] # Current pose
footsteps = self.footstep_planner.plan_footsteps(start_pose, goal)
if footsteps:
self.blackboard.set("footsteps", footsteps)
self.feedback_message = f"Planned {len(footsteps)} footsteps"
return py_trees.Status.SUCCESS
else:
return py_trees.Status.FAILURE
class ExecuteFootsteps(py_trees.behaviour.Behaviour):
"""
Execute planned footsteps with balance control
"""
def __init__(self, name):
super().__init__(name)
self.blackboard = py_trees.blackboard.Blackboard()
def setup(self, **kwargs):
# Initialize balance controller
self.balance_controller = BalanceController()
def update(self):
footsteps = self.blackboard.get("footsteps")
if not footsteps:
return py_trees.Status.FAILURE
# Execute footsteps one by one
current_step_idx = self.blackboard.get("current_step", 0)
if current_step_idx >= len(footsteps):
# All footsteps executed
return py_trees.Status.SUCCESS
# Execute current step with balance control
success = self.execute_single_step(footsteps[current_step_idx])
if success:
self.blackboard.set("current_step", current_step_idx + 1)
self.feedback_message = f"Completed step {current_step_idx + 1}"
return py_trees.Status.RUNNING # Continue to next step
else:
return py_trees.Status.FAILURE
def execute_single_step(self, step):
# Simulate executing a single step with balance control
# In reality, this would interface with robot's walking controller
time.sleep(0.1) # Simulate step execution time
return True # Assume success for example
class HumanoidNavigationSelector(py_trees.composites.Selector):
"""
Selector for humanoid navigation behaviors
"""
def __init__(self, name):
super().__init__(name)
# Add child behaviors
self.add_child(CheckFootSupport("CheckSupport"))
self.add_child(PlanFootsteps("PlanFootsteps"))
self.add_child(ExecuteFootsteps("ExecuteFootsteps"))
def create_humanoid_navigation_tree():
"""
Create a behavior tree for humanoid navigation
"""
# Main root
root = py_trees.composites.Sequence(name="HumanoidNavigation")
# Add selector for navigation behaviors
nav_selector = HumanoidNavigationSelector("NavigationSelector")
root.add_child(nav_selector)
# Add goal setting (in practice, this would come from external source)
set_goal = py_trees.behaviours.Success("SetGoal")
root.insert_child(set_goal, index=0)
return root
class BehaviorTreeManager(Node):
"""
Manage the behavior tree execution
"""
def __init__(self):
super().__init__('behavior_tree_manager')
# Create the behavior tree
self.tree = create_humanoid_navigation_tree()
# Setup tree visitor for debugging
self.snapshot_visitor = py_trees.visitors.SnapshotVisitor()
# Timer for tree ticking
self.timer = self.create_timer(0.1, self.tick_tree)
self.get_logger().info("Behavior Tree Manager initialized")
def tick_tree(self):
"""
Tick the behavior tree
"""
# Tick the tree
self.tree.tick_once()
# Visit the tree to get feedback
self.tree.visit(self.snapshot_visitor)
# Print tree status
print(py_trees.display.unicode_tree(
root=self.tree,
visited=self.snapshot_visitor.visited,
previously_visited=self.snapshot_visitor.previously_visited
))
# Check if tree has reached a conclusion
if self.tree.status == py_trees.common.Status.SUCCESS:
self.get_logger().info("Navigation task completed successfully")
elif self.tree.status == py_trees.common.Status.FAILURE:
self.get_logger().error("Navigation task failed")
# Reset tree if completed
if self.tree.status in [py_trees.common.Status.SUCCESS, py_trees.common.Status.FAILURE]:
self.tree.tip().stop(py_trees.common.Status.INVALID)
self.tree.reset()
def main(args=None):
rclpy.init(args=args)
# Initialize behavior tree manager
bt_manager = BehaviorTreeManager()
try:
rclpy.spin(bt_manager)
except KeyboardInterrupt:
pass
finally:
bt_manager.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 52 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 51 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
# python/navigation_optimization.py
import numpy as np
import time
import threading
from collections import deque
import multiprocessing as mp
from numba import jit, cuda
import ctypes
class RealTimeNavigationOptimizer:
"""
Optimize navigation algorithms for real-time humanoid locomotion
"""
def __init__(self):
self.control_frequency = 200 # Hz
self.planning_frequency = 10 # Hz
self.safety_frequency = 100 # Hz
# Real-time constraints
self.max_control_time = 1.0 / self.control_frequency # seconds
self.max_planning_time = 1.0 / self.planning_frequency
self.max_safety_time = 1.0 / self.safety_frequency
# Threading for parallel processing
self.control_thread = None
self.planning_thread = None
self.safety_thread = None
# Data buffers
self.sensor_data = deque(maxlen=10)
self.trajectory_buffer = deque(maxlen=5)
self.control_commands = deque(maxlen=5)
@jit(nopython=True)
def fast_distance_calculation(self, p1, p2):
"""Fast distance calculation using Numba"""
dx = p1[0] - p2[0]
dy = p1[1] - p2[1]
return np.sqrt(dx*dx + dy*dy)
@jit(nopython=True)
def fast_vector_operations(self, v1, v2):
"""Fast vector operations using Numba"""
# Add two vectors
result = np.empty(2)
result[0] = v1[0] + v2[0]
result[1] = v1[1] + v2[1]
return result
def run_control_loop(self):
"""Run the real-time control loop"""
rate = 1.0 / self.control_frequency
while True:
start_time = time.time()
try:
# Execute control algorithm
control_cmd = self.execute_control_algorithm()
# Add to command buffer
self.control_commands.append(control_cmd)
# Maintain real-time constraint
elapsed = time.time() - start_time
sleep_time = max(0, rate - elapsed)
if sleep_time > 0:
time.sleep(sleep_time)
except Exception as e:
self.get_logger().error(f"Control loop error: {e}")
time.sleep(rate)
def execute_control_algorithm(self):
"""Execute the main control algorithm"""
# Simplified control algorithm
# In reality, this would implement the full control law
# Get latest sensor data
if self.sensor_data:
latest_data = self.sensor_data[-1]
else:
return np.zeros(6) # Default command
# Calculate control output (simplified)
control_output = np.zeros(6) # [x_vel, y_vel, theta_vel, com_x, com_y, com_z]
# Apply control law
# This would implement the balance control algorithm
control_output[0] = latest_data.get('desired_x_vel', 0.0)
control_output[1] = latest_data.get('desired_y_vel', 0.0)
control_output[2] = latest_data.get('desired_theta_vel', 0.0)
return control_output
def run_planning_loop(self):
"""Run the path planning loop"""
rate = 1.0 / self.planning_frequency
while True:
start_time = time.time()
try:
# Execute planning algorithm
new_plan = self.execute_planning_algorithm()
# Add to trajectory buffer
if new_plan is not None:
self.trajectory_buffer.append(new_plan)
# Maintain real-time constraint
elapsed = time.time() - start_time
sleep_time = max(0, rate - elapsed)
if sleep_time > 0:
time.sleep(sleep_time)
except Exception as e:
self.get_logger().error(f"Planning loop error: {e}")
time.sleep(rate)
def execute_planning_algorithm(self):
"""Execute the path planning algorithm"""
# Simplified planning algorithm
# In reality, this would implement A*, RRT, or other planners
# Check for new goal
goal = self.get_latest_goal()
if goal is None:
return None
# Get current state
current_state = self.get_current_state()
# Plan path (simplified)
path = self.plan_path_fast(current_state, goal)
return path
def plan_path_fast(self, start, goal):
"""Fast path planning (simplified)"""
# Use a fast but potentially suboptimal planner
# For real-time applications, prioritize speed over optimality
# Simplified straight-line path with obstacle avoidance
path = [start, goal]
return path
def run_safety_loop(self):
"""Run the safety monitoring loop"""
rate = 1.0 / self.safety_frequency
while True:
start_time = time.time()
try:
# Check safety conditions
safety_status = self.check_safety_conditions()
# Handle safety violations
if not safety_status['is_safe']:
self.emergency_stop()
# Maintain real-time constraint
elapsed = time.time() - start_time
sleep_time = max(0, rate - elapsed)
if sleep_time > 0:
time.sleep(sleep_time)
except Exception as e:
self.get_logger().error(f"Safety loop error: {e}")
self.emergency_stop()
time.sleep(rate)
def check_safety_conditions(self):
"""Check various safety conditions"""
safety_status = {
'is_safe': True,
'balance_safe': True,
'collision_safe': True,
'hardware_safe': True
}
# Check balance
balance_ok = self.check_balance_safety()
safety_status['balance_safe'] = balance_ok
safety_status['is_safe'] &= balance_ok
# Check for collisions
collision_ok = self.check_collision_safety()
safety_status['collision_safe'] = collision_ok
safety_status['is_safe'] &= collision_ok
# Check hardware status
hardware_ok = self.check_hardware_safety()
safety_status['hardware_safe'] = hardware_ok
safety_status['is_safe'] &= hardware_ok
return safety_status
def check_balance_safety(self):
"""Check if robot is in safe balance state"""
# Simplified balance check
# In reality, this would check ZMP, CoM position, etc.
return True
def check_collision_safety(self):
"""Check for potential collisions"""
# Simplified collision check
# In reality, this would process sensor data
return True
def check_hardware_safety(self):
"""Check hardware status"""
# Simplified hardware check
# In reality, this would check joint limits, temperatures, etc.
return True
def emergency_stop(self):
"""Execute emergency stop procedure"""
self.get_logger().error("EMERGENCY STOP ACTIVATED")
# Send zero commands to all actuators
# This would interface with the robot's safety system
def get_latest_goal(self):
"""Get the latest navigation goal"""
# In reality, this would get goal from ROS topics
return None
def get_current_state(self):
"""Get the current robot state"""
# In reality, this would get state from odometry, IMU, etc.
return [0.0, 0.0, 0.0]
def start_real_time_loops(self):
"""Start all real-time loops in separate threads"""
# Start control loop
self.control_thread = threading.Thread(target=self.run_control_loop, daemon=True)
self.control_thread.start()
# Start planning loop
self.planning_thread = threading.Thread(target=self.run_planning_loop, daemon=True)
self.planning_thread.start()
# Start safety loop
self.safety_thread = threading.Thread(target=self.run_safety_loop, daemon=True)
self.safety_thread.start()
class MultiProcessNavigation:
"""
Multi-process navigation system for better real-time performance
"""
def __init__(self):
self.processes = []
self.shared_memory = {}
def create_navigation_process(self):
"""Create a dedicated process for navigation"""
# This would create a process with real-time priority
# and dedicated CPU core if possible
pass
def setup_shared_memory(self):
"""Setup shared memory for inter-process communication"""
# Use multiprocessing shared memory for low-latency communication
pass
def optimize_for_real_time():
"""
Apply various optimizations for real-time performance
"""
optimizer = RealTimeNavigationOptimizer()
# Apply Numba optimizations
# The @jit decorators above will compile functions to machine code
# Start real-time loops
optimizer.start_real_time_loops()
# Monitor performance
monitor = PerformanceMonitor()
monitor.start_monitoring()
return optimizer
class PerformanceMonitor:
"""
Monitor navigation system performance
"""
def __init__(self):
self.metrics = {
'control_loop_times': deque(maxlen=1000),
'planning_times': deque(maxlen=100),
'loop_rates': deque(maxlen=1000),
'memory_usage': deque(maxlen=1000)
}
self.start_time = time.time()
def start_monitoring(self):
"""Start performance monitoring"""
import psutil
import threading
def monitor_loop():
while True:
# Record current time
current_time = time.time()
# Monitor memory usage
process = psutil.Process()
memory_mb = process.memory_info().rss / 1024 / 1024
self.metrics['memory_usage'].append(memory_mb)
# Calculate loop rate
elapsed = current_time - self.start_time
if elapsed > 0:
rate = len(self.metrics['loop_rates']) / elapsed
self.metrics['loop_rates'].append(rate)
time.sleep(1.0) # Update every second
monitor_thread = threading.Thread(target=monitor_loop, daemon=True)
monitor_thread.start()
def get_performance_report(self):
"""Get current performance metrics"""
if not self.metrics['loop_rates']:
return "No data available"
avg_rate = sum(self.metrics['loop_rates']) / len(self.metrics['loop_rates'])
max_memory = max(self.metrics['memory_usage']) if self.metrics['memory_usage'] else 0
report = f"""
Performance Report:
- Average Loop Rate: {avg_rate:.2f} Hz
- Max Memory Usage: {max_memory:.2f} MB
- Control Loop Samples: {len(self.metrics['control_loop_times'])}
- Planning Samples: {len(self.metrics['planning_times'])}
"""
return report
# Example usage
def run_optimized_navigation():
"""
Run the optimized navigation system
"""
print("Starting optimized humanoid navigation system...")
# Initialize optimizer
nav_optimizer = optimize_for_real_time()
# Run for a while
time.sleep(10)
# Print performance report
monitor = PerformanceMonitor()
print(monitor.get_performance_report())
return nav_optimizer
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 51 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 51 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
# python/isaac_sim_nav_integration.py
from omni.isaac.core import World
from omni.isaac.core.utils.stage import add_reference_to_stage
from omni.isaac.core.robots import Robot
from omni.isaac.core.objects import DynamicCuboid
from omni.isaac.core.prims import RigidPrim
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.nucleus import get_assets_root_path
from omni.isaac.core.utils.viewports import set_camera_view
import numpy as np
import math
import rclpy
from geometry_msgs.msg import PoseStamped, Twist
from nav_msgs.msg import Odometry, Path
from sensor_msgs.msg import LaserScan
from std_msgs.msg import String
class IsaacSimNavigationInterface:
"""
Interface between Isaac Sim and ROS 2 for humanoid navigation
"""
def __init__(self, robot_name="humanoid_robot", stage_units_in_meters=1.0):
# Isaac Sim components
self.world = World(stage_units_in_meters=stage_units_in_meters)
self.robot = None
self.robot_name = robot_name
# ROS 2 components (will be initialized later)
self.ros_node = None
self.ros_initialized = False
# Navigation state
self.current_goal = None
self.navigation_active = False
self.robot_pose = np.array([0.0, 0.0, 0.0]) # x, y, theta
def setup_isaac_sim_environment(self):
"""Setup Isaac Sim environment with humanoid robot"""
# Get assets root path
assets_root_path = get_assets_root_path()
if assets_root_path is None:
raise Exception("Could not find Isaac Sim assets path")
# Add ground plane
self.world.scene.add_default_ground_plane()
# Add humanoid robot (using a simple model for demonstration)
robot_path = f"{assets_root_path}/Isaac/Robots/Franka/fr3.usd"
add_reference_to_stage(usd_path=robot_path, prim_path="/World/Robot")
# Initialize robot in Isaac Sim
self.robot = self.world.scene.get_object("Robot")
# Add obstacles to the environment
self.add_obstacles()
# Setup camera for visualization
set_camera_view(eye=[2.0, 2.0, 2.0], target=[0.0, 0.0, 0.0])
def add_obstacles(self):
"""Add obstacles to the environment"""
# Add some simple obstacles
obstacles = [
{"name": "box1", "position": [1.0, 0.0, 0.1], "size": [0.2, 0.2, 0.2]},
{"name": "box2", "position": [0.0, 1.0, 0.1], "size": [0.2, 0.2, 0.2]},
{"name": "box3", "position": [-1.0, -1.0, 0.1], "size": [0.2, 0.2, 0.2]},
]
for obs in obstacles:
DynamicCuboid(
prim_path=f"/World/{obs['name']}",
name=obs['name'],
position=obs['position'],
size=obs['size'],
color=np.array([0.5, 0.5, 0.5])
)
def initialize_ros_interface(self):
"""Initialize ROS 2 interface"""
if not self.ros_initialized:
rclpy.init()
self.ros_node = IsaacSimRosBridge()
self.ros_initialized = True
def run_navigation_simulation(self):
"""Run the navigation simulation loop"""
# Reset the world
self.world.reset()
# Initialize ROS interface
self.initialize_ros_interface()
# Main simulation loop
sim_step = 0
while True:
# Step Isaac Sim
self.world.step(render=True)
# Update robot pose
self.update_robot_pose()
# Process ROS messages
if self.ros_node:
try:
rclpy.spin_once(self.ros_node, timeout_sec=0)
except KeyboardInterrupt:
break
# Process navigation commands
self.process_navigation_commands()
# Log progress
if sim_step % 100 == 0:
print(f"Simulation step: {sim_step}, Robot pose: {self.robot_pose}")
sim_step += 1
# Limit simulation steps for demo
if sim_step > 5000: # Run for 5000 steps then stop
break
def update_robot_pose(self):
"""Update robot pose from Isaac Sim"""
if self.robot:
# Get current pose from Isaac Sim
pose = self.robot.get_world_poses()
if pose:
positions, orientations = pose
if len(positions) > 0:
pos = positions[0]
# Simplified: extract x, y position and approximate theta
self.robot_pose[0] = float(pos[0])
self.robot_pose[1] = float(pos[1])
# For theta, we'd need orientation information
# Simplified for this example
self.robot_pose[2] = 0.0 # Placeholder
def process_navigation_commands(self):
"""Process navigation commands from ROS"""
if self.current_goal is not None and self.navigation_active:
# Calculate distance to goal
dx = self.current_goal[0] - self.robot_pose[0]
dy = self.current_goal[1] - self.robot_pose[1]
distance = math.sqrt(dx*dx + dy*dy)
# Check if reached goal
if distance < 0.2: # 20cm threshold
self.navigation_active = False
if self.ros_node:
self.ros_node.publish_navigation_status("GOAL_REACHED")
else:
# Send movement command towards goal
self.move_towards_goal(dx, dy)
def move_towards_goal(self, dx, dy):
"""Send command to move robot towards goal"""
if self.ros_node:
# Calculate desired velocity
speed = min(0.2, math.sqrt(dx*dx + dy*dy) * 2.0) # Proportional control
angle = math.atan2(dy, dx)
# Create twist message
twist_msg = Twist()
twist_msg.linear.x = speed * math.cos(angle - self.robot_pose[2])
twist_msg.angular.z = angle - self.robot_pose[2]
# Publish command
self.ros_node.publish_velocity_command(twist_msg)
class IsaacSimRosBridge(Node):
"""
ROS 2 bridge node for Isaac Sim integration
"""
def __init__(self):
super().__init__('isaac_sim_nav_bridge')
# Publishers
self.odom_pub = self.create_publisher(Odometry, '/odom', 10)
self.scan_pub = self.create_publisher(LaserScan, '/scan', 10)
self.status_pub = self.create_publisher(String, '/nav_status', 10)
# Subscribers
self.goal_sub = self.create_subscription(
PoseStamped,
'/move_base_simple/goal',
self.goal_callback,
10
)
self.cmd_vel_sub = self.create_subscription(
Twist,
'/cmd_vel',
self.cmd_vel_callback,
10
)
# Timer for publishing sensor data
self.timer = self.create_timer(0.1, self.publish_sensor_data)
self.get_logger().info("Isaac Sim ROS Bridge initialized")
def goal_callback(self, msg):
"""Handle navigation goal from ROS"""
goal = [msg.pose.position.x, msg.pose.position.y, 0.0]
self.get_logger().info(f"Received navigation goal: {goal}")
# This would be passed to the navigation system
# For now, we'll just log it
def cmd_vel_callback(self, msg):
"""Handle velocity commands from ROS"""
self.get_logger().info(f"Received velocity command: {msg.linear.x}, {msg.angular.z}")
# This would be sent to the robot in Isaac Sim
# For now, we'll just log it
def publish_sensor_data(self):
"""Publish sensor data from Isaac Sim"""
# Publish odometry (simplified)
odom_msg = Odometry()
odom_msg.header.stamp = self.get_clock().now().to_msg()
odom_msg.header.frame_id = 'odom'
odom_msg.child_frame_id = 'base_link'
# Publish dummy data for demonstration
odom_msg.pose.pose.position.x = 0.0
odom_msg.pose.pose.position.y = 0.0
odom_msg.pose.pose.position.z = 0.0
self.odom_pub.publish(odom_msg)
# Publish dummy laser scan
scan_msg = LaserScan()
scan_msg.header.stamp = self.get_clock().now().to_msg()
scan_msg.header.frame_id = 'laser_frame'
scan_msg.angle_min = -math.pi/2
scan_msg.angle_max = math.pi/2
scan_msg.angle_increment = math.pi/180 # 1 degree
scan_msg.range_min = 0.1
scan_msg.range_max = 10.0
scan_msg.ranges = [5.0] * 181 # 181 points
self.scan_pub.publish(scan_msg)
def publish_navigation_status(self, status):
"""Publish navigation status"""
status_msg = String()
status_msg.data = status
self.status_pub.publish(status_msg)
def publish_velocity_command(self, twist_msg):
"""Publish velocity command to robot"""
# This would send the command to Isaac Sim robot
pass
def main():
"""Main function to run Isaac Sim navigation integration"""
print("Setting up Isaac Sim navigation environment...")
# Create navigation interface
nav_interface = IsaacSimNavigationInterface()
# Setup Isaac Sim environment
nav_interface.setup_isaac_sim_environment()
# Run simulation
print("Starting navigation simulation...")
nav_interface.run_navigation_simulation()
print("Navigation simulation completed.")
if __name__ == '__main__':
main()
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 50 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 50 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 49 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 49 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 48 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 48 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 48 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 47 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 47 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 46 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 46 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 45 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 45 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 44 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 44 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 44 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 43 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 43 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 42 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 42 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 41 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 41 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 40 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 40 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 40 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 39 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 39 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 38 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 38 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 37 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 37 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 36 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 36 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 36 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 35 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 35 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 34 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 34 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 34 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 34 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 34 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 33 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE
MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 33 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 32 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE MYMEMORY WARNING: YOU USED ALL AVAILABLE FREE TRANSLATIONS FOR TODAY. NEXT AVAILABLE IN 20 HOURS 06 MINUTES 32 SECONDS VISIT HTTPS://MYMEMORY.TRANSLATED.NET/DOC/USAGELIMITS.PHP TO TRANSLATE MORE