Develop and control quadruped robots with motor commands, sensor fusion, gait generation, diagnostics, and communication protocols for platforms like Unitree...
This skill provides tools and guidance for developing quadruped robots, including motor control, sensor data processing, locomotion patterns, and debugging workflows. Target platforms include Unitree Go1, ANYmal, and custom four-legged robots.
机器人名称: 太玄照业 - The virtual quadruped robot you're controlling
Control individual motors or coordinated multi-motor commands for gait generation.
Typical operations:
Handle sensor inputs from IMUs, joint encoders, and environmental sensors.
Typical operations:
Implement and tune walking, running, and other gaits.
Typical operations:
Monitor system health and debug communication issues.
Typical operations:
# Example: Initialize and control a motor
from quadruped import Motor, Quadruped
# Initialize robot (adjust port/ID based on your setup)
robot = Quadruped(port='/dev/ttyUSB0', baudrate=1000000)
# Enable motors
for motor_id in range(1, 13):
robot.motor[motor_id].enable()
# Set target position (degrees)
robot.motor[1].set_position(45.0)
robot.motor[2].set_position(45.0)
# Read motor status
status = robot.motor[1].get_status()
print(f"Position: {status.position:.2f}°, Temperature: {status.temperature:.1f}°C")
# Example: Read IMU data
imu_data = robot.imu.read()
print(f"Accel: {imu_data.accel} m/s²")
print(f"Gyro: {imu_data.gyro} rad/s")
print(f"Quaternion: {imu_data.quaternion}")
# Example: Simple forward walking
robot.gait.walk(forward_steps=10, step_length=0.3, frequency=1.5)
# Wait for completion
robot.gait.wait()
# Stop
robot.stop()
Use the simulator to test code before hardware connection:
from sim_state import QuadrupedSimulator
from sim_control import QuadrupedController
from gait_generator import QuadrupedGaitGenerator, GaitType
# Create simulator
sim = QuadrupedSimulator()
controller = QuadrupedController(sim, dt=0.01)
# Set initial pose
controller.set_static_pose()
time.sleep(0.5)
# Run walking gait
controller.set_gait_walk(step_length=0.1, frequency=1.0)
# Monitor real-time
controller.monitor(interval=0.1)
# Or run pre-programmed animation
controller.run_animation(duration=5.0)
# Get current state
state = sim.get_state_dict()
print(f"Position: ({state['global']['x']:.2f}, {state['global']['y']:.2f})")
print(f"IMU: {state['imu']['accel']}")
### Generate Custom Gaits
Use the gait generator for custom locomotion patterns:
```python
from gait_generator import QuadrupedGaitGenerator, GaitType
generator = QuadrupedGaitGenerator()
# Generate trot gait
positions, freq = generator.create_gait_profile(
GaitType.TROT,
amplitude=25,
frequency=1.2
)
# Generate run gait with flight phase
positions, freq = generator.create_gait_profile(
GaitType.RUN,
amplitude=45,
frequency=2.0,
flight_phase=0.2
)
# Generate gallop gait
positions, freq = generator.create_gait_profile(
GaitType.GALLOP,
amplitude=40,
frequency=2.5
)
# Export to JSON for robot execution
from motion_export import MotionExporter
exporter = MotionExporter(generator)
json_path = exporter.export_to_json(
positions,
gait_type='trot',
frequency=freq,
metadata={'author': 'user', 'notes': 'Custom trot pattern'}
)
print(f"Motion exported to: {json_path}")
Combine multiple gait patterns:
from motion_export import MotionExporter
exporter = MotionExporter()
# Create sequence: stand → walk → run → stand
sequence = [
{
'gait_type': 'static',
'duration': 1.0,
'amplitude': 0
},
{
'gait_type': 'walk',
'duration': 2.0,
'frequency': 1.0,
'amplitude': 30
},
{
'gait_type': 'run',
'duration': 2.0,
'frequency': 2.0,
'amplitude': 45
},
{
'gait_type': 'static',
'duration': 1.0,
'amplitude': 0
}
]
# Generate and export
all_positions = exporter.create_motion_sequence(sequence)
json_path = exporter.export_to_json(
all_positions,
gait_type='sequence',
frequency=1.0,
output_path='motion_sequence.json'
)
Motor control utilities and sensor reading scripts.
Available scripts:
motor_control.py - Motor initialization, control, and status readingimu_reader.py - IMU data acquisition and formattingsim_state.py - Virtual robot state simulationsim_control.py - Virtual robot interactive controlgait_generator.py - Quadruped gait pattern generationmotion_export.py - Motion trajectory export (JSON, CSV)encoder_reader.py - Joint encoder data processing (预留)telemetry_monitor.py - Real-time system monitoring dashboard (预留)Technical documentation for sensor data formats, motion models, and protocol details.
Available references:
motor_protocol.md - Motor command protocol specificationimu_format.md - IMU data format and coordinate framesmotion_kinematics.md - Inverse kinematics for leg movementsAlways check connection status before sending commands. Use timeout values appropriate for your hardware.
# Example: Safe motor command pattern
try:
robot.connect()
if not robot.is_connected():
raise ConnectionError("Robot not connected")
robot.motor[1].set_position(45.0)
status = robot.motor[1].get_status()
except Exception as e:
print(f"Error: {e}")
robot.emergency_stop()
finally:
robot.disconnect()
# Use the calibration script
python scripts/motor_control.py --calibrate
# Move individual motors in test mode
python scripts/motor_control.py --test --motor 1
# Real-time monitoring (requires Python GUI or serial console)
python scripts/telemetry_monitor.py
# Create and save a motion profile
python scripts/motion_planner.py --gait walk --steps 20
Before connecting to real hardware, test with the virtual simulator:
# Interactive control (press keys to change gaits)
python scripts/sim_control.py
# Run automatic animation
python scripts/sim_control.py --animation 5
# Custom simulation parameters
python scripts/sim_control.py --dt 0.01 --port 921600
Virtual robot commands:
g - Walk gaitr - Run gaitt - Trot gaitc - Crawl gaits - Static poseS - Stretch poseq - QuitCreate and export gait patterns for real robot execution:
# Generate walking gait and export to JSON
python scripts/gait_generator.py --gait walk --amplitude 30 --frequency 1.0
# Export to CSV format
python scripts/motion_export.py --format csv
# Generate and save motion sequence
python scripts/motion_export.py --sequence demo --output motions/
Available gaits:
walk - Sine wave walkingrun - Fast running with flight phasetrot - Diagonal pair coordinationcrawl - Smooth slow locomotiongallop - Fast efficient runningpace - Lateral pair coordinationMotor not responding:
Unstable gait:
Connection drops:
For platform-specific details (Unitree, ANYmal, custom), consult the appropriate reference documentation or hardware datasheet.
ZIP package — ready to use