tomevault-io When to Use This Skill
- Writing unit tests for ROS1/ROS2 nodes
- Setting up integration tests with launch_testing
- Mocking hardware (sensors, actuators) for CI/CD
- Building simulation-based test suites
- Testing perception pipelines with ground truth
- Validating trajectory planners and controllers
- Setting up CI/CD pipelines for robotics packages
- Debugging flaky tests in robotics systems
The Robotics Testing Pyramid
╱╲
╱ ╲ Field Tests
╱ ╲ (Real robot, real environment)
╱──────╲
╱ ╲ Hardware-in-the-Loop (HIL)
╱ ╲ (Real hardware, controlled environment)
╱────────────╲
╱ ╲ Simulation Tests
╱ ╲ (Full sim, realistic physics)
╱──────────────────╲
╱ ╲ Integration Tests
╱ ╲ (Multi-node, message passing)
╱────────────────────────╲
╱ ╲ Unit Tests
╱____________________________╲ (Single function/class, fast, deterministic)
MORE tests at the bottom, FEWER at the top.
Bottom = fast, cheap, deterministic. Top = slow, expensive, realistic.
Unit Testing Patterns
Testing ROS2 Nodes with pytest
# test_perception_node.py
import pytest
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from my_pkg.perception_node import PerceptionNode
import numpy as np
@pytest.fixture(scope='module')
def ros_context():
"""Initialize ROS2 context once per test module"""
rclpy.init()
yield
rclpy.shutdown()
@pytest.fixture
def perception_node(ros_context):
"""Create a fresh perception node for each test"""
node = PerceptionNode()
yield node
node.destroy_node()
@pytest.fixture
def test_image():
"""Generate a synthetic test image"""
msg = Image()
msg.height = 256
msg.width = 256
msg.encoding = 'rgb8'
msg.step = 256 * 3
msg.data = np.random.randint(0, 255, (256, 256, 3),
dtype=np.uint8).tobytes()
return msg
class TestPerceptionNode:
def test_initialization(self, perception_node):
"""Node should initialize with correct default parameters"""
assert perception_node.get_parameter('confidence_threshold').value == 0.7
assert perception_node.get_parameter('rate_hz').value == 30.0
def test_parameter_validation(self, perception_node):
"""Node should reject invalid parameter values"""
from rcl_interfaces.msg import SetParametersResult
result = perception_node.set_parameters([
rclpy.parameter.Parameter('confidence_threshold',
value=-0.5) # Invalid!
])
assert not result[0].successful
def test_image_callback_publishes_detections(self, perception_node, test_image):
"""Processing an image should produce detection output"""
received = []
# Create a test subscriber
sub_node = Node('test_subscriber')
sub_node.create_subscription(
DetectionArray, '/perception/detections',
lambda msg: received.append(msg), 10)
# Simulate image callback
perception_node.image_callback(test_image)
# Spin briefly to allow message propagation
rclpy.spin_once(sub_node, timeout_sec=1.0)
rclpy.spin_once(perception_node, timeout_sec=1.0)
# Verify
assert len(received) > 0
sub_node.destroy_node()
def test_empty_image_handling(self, perception_node):
"""Node should handle empty/corrupted images gracefully"""
empty_msg = Image() # No data
# Should not crash
perception_node.image_callback(empty_msg)
Testing Pure Functions (No ROS Dependency)
# test_kinematics.py
import pytest
import numpy as np
from my_pkg.kinematics import (
forward_kinematics, inverse_kinematics,
quaternion_multiply, transform_point
)
class TestForwardKinematics:
@pytest.mark.parametrize("joint_angles,expected_pos", [
# Home position
(np.zeros(7), np.array([0.088, 0.0, 1.033])),
# Known calibrated pose
(np.array([0, -0.785, 0, -2.356, 0, 1.571, 0.785]),
np.array([0.307, 0.0, 0.59])),
])
def test_known_poses(self, joint_angles, expected_pos):
"""FK should match known calibrated positions"""
result = forward_kinematics(joint_angles)
np.testing.assert_allclose(result[:3], expected_pos, atol=0.01)
def test_fk_ik_roundtrip(self):
"""FK(IK(pose)) should return the original pose"""
original_pose = np.array([0.4, 0.1, 0.5, 1.0, 0.0, 0.0, 0.0])
joint_angles = inverse_kinematics(original_pose)
recovered_pose = forward_kinematics(joint_angles)
np.testing.assert_allclose(recovered_pose, original_pose, atol=1e-4)
def test_joint_limits_respected(self):
"""IK should not return angles outside joint limits"""
target = np.array([0.5, 0.2, 0.3, 1.0, 0.0, 0.0, 0.0])
joints = inverse_kinematics(target)
for i, (lo, hi) in enumerate(JOINT_LIMITS):
assert lo <= joints[i] <= hi, \
f"Joint {i}: {joints[i]} outside [{lo}, {hi}]"
class TestQuaternionMath:
def test_identity_multiply(self):
"""q * identity = q"""
q = np.array([0.5, 0.5, 0.5, 0.5])
identity = np.array([1.0, 0.0, 0.0, 0.0])
result = quaternion_multiply(q, identity)
np.testing.assert_allclose(result, q, atol=1e-10)
def test_inverse_multiply(self):
"""q * q_inv = identity"""
q = np.array([0.5, 0.5, 0.5, 0.5])
q_inv = np.array([0.5, -0.5, -0.5, -0.5])
result = quaternion_multiply(q, q_inv)
np.testing.assert_allclose(result, [1, 0, 0, 0], atol=1e-10)
@pytest.mark.parametrize("q", [
np.random.randn(4) for _ in range(20) # Random quaternions
])
def test_unit_quaternion_preserved(self, q):
"""Multiplication of unit quaternions should produce unit quaternion"""
q = q / np.linalg.norm(q) # Normalize
q2 = np.array([0.707, 0.707, 0, 0]) # 90° rotation
result = quaternion_multiply(q, q2)
assert abs(np.linalg.norm(result) - 1.0) < 1e-10
Property-Based Testing with Hypothesis
from hypothesis import given, strategies as st, settings
import hypothesis.extra.numpy as hnp
class TestTrajectoryInterpolation:
@given(
start=hnp.arrays(np.float64, (7,),
elements=st.floats(min_value=-3.14, max_value=3.14)),
end=hnp.arrays(np.float64, (7,),
elements=st.floats(min_value=-3.14, max_value=3.14)),
num_steps=st.integers(min_value=2, max_value=1000),
)
@settings(max_examples=200)
def test_interpolation_properties(self, start, end, num_steps):
"""Trajectory interpolation should satisfy mathematical properties"""
traj = linear_interpolate(start, end, num_steps)
# Property 1: Correct number of steps
assert len(traj) == num_steps
# Property 2: Starts at start, ends at end
np.testing.assert_allclose(traj[0], start, atol=1e-10)
np.testing.assert_allclose(traj[-1], end, atol=1e-10)
# Property 3: Monotonic progress (each step closer to goal)
for i in range(1, len(traj)):
dist_prev = np.linalg.norm(traj[i-1] - end)
dist_curr = np.linalg.norm(traj[i] - end)
assert dist_curr <= dist_prev + 1e-10
# Property 4: No jumps exceed max step size
diffs = np.diff(traj, axis=0)
max_step = np.max(np.abs(diffs))
expected_max = np.max(np.abs(end - start)) / (num_steps - 1)
assert max_step <= expected_max + 1e-10
@given(
points=hnp.arrays(np.float64, (3,),
elements=st.floats(min_value=-10, max_value=10, allow_nan=False)),
)
def test_transform_roundtrip(self, points):
"""Transform followed by inverse transform = identity"""
T = random_transform_matrix()
T_inv = np.linalg.inv(T)
transformed = transform_point(T, points)
recovered = transform_point(T_inv, transformed)
np.testing.assert_allclose(recovered, points, atol=1e-8)
Integration Testing
ROS2 Launch Testing
# test_integration.py
import pytest
import launch_testing
from launch import LaunchDescription
from launch_ros.actions import Node
import rclpy
import unittest
@pytest.mark.launch_test
def generate_test_description():
"""Launch the nodes we want to test"""
perception_node = Node(
package='my_pkg', executable='perception_node',
parameters=[{'use_sim_time': True}],
)
planner_node = Node(
package='my_pkg', executable='planner_node',
parameters=[{'use_sim_time': True}],
)
return LaunchDescription([
perception_node,
planner_node,
launch_testing.actions.ReadyToTest(),
])
class TestPerceptionPlannerIntegration(unittest.TestCase):
@classmethod
def setUpClass(cls):
rclpy.init()
cls.node = rclpy.create_node('integration_test')
@classmethod
def tearDownClass(cls):
cls.node.destroy_node()
rclpy.shutdown()
def test_perception_publishes_to_planner(self):
"""Perception detections should reach the planner"""
# Publish a test image
pub = self.node.create_publisher(Image, '/camera/image_raw', 10)
test_img = create_test_image_with_object()
pub.publish(test_img)
# Wait for planner output
received = []
sub = self.node.create_subscription(
Path, '/planner/path',
lambda msg: received.append(msg), 10)
end_time = self.node.get_clock().now() + rclpy.duration.Duration(seconds=5)
while self.node.get_clock().now() < end_time and not received:
rclpy.spin_once(self.node, timeout_sec=0.1)
self.assertGreater(len(received), 0, "Planner should produce a path")
self.assertGreater(len(received[0].poses), 0, "Path should have poses")
Mock Hardware Patterns
class MockCamera:
"""Mock camera for testing without hardware"""
def __init__(self, image_dir=None, resolution=(640, 480)):
self.resolution = resolution
self.frame_count = 0
if image_dir:
# Use pre-recorded test images
self.images = self._load_test_images(image_dir)
else:
# Generate synthetic images
self.images = None
def get_frame(self):
self.frame_count += 1
if self.images:
idx = self.frame_count % len(self.images)
return self.images[idx]
else:
return self._generate_synthetic_frame()
def _generate_synthetic_frame(self):
"""Generate a deterministic test frame with known objects"""
img = np.zeros((*self.resolution[::-1], 3), dtype=np.uint8)
# Draw a red rectangle (simulated object)
img[100:200, 150:250] = [255, 0, 0]
return img
class MockJointStatePublisher:
"""Publish deterministic joint states for testing"""
def __init__(self, node, trajectory=None):
self.pub = node.create_publisher(
JointState, '/joint_states', 10)
self.step = 0
if trajectory is not None:
self.trajectory = trajectory
else:
# Sinusoidal motion for testing
t = np.linspace(0, 2*np.pi, 100)
self.trajectory = np.column_stack([
0.1 * np.sin(t + i * 0.5) for i in range(7)
])
def publish_next(self):
msg = JointState()
msg.header.stamp = self.node.get_clock().now().to_msg()
msg.name = [f'joint_{i}' for i in range(7)]
idx = self.step % len(self.trajectory)
msg.position = self.trajectory[idx].tolist()
self.pub.publish(msg)
self.step += 1
Golden File Testing (Trajectory Regression)
class TestTrajectoryRegression:
"""Compare planner output against known-good trajectories"""
GOLDEN_DIR = Path(__file__).parent / 'golden_trajectories'
def test_straight_line_plan(self):
start = np.array([0.3, 0.0, 0.5])
goal = np.array([0.5, 0.2, 0.3])
trajectory = planner.plan(start, goal)
golden_file = self.GOLDEN_DIR / 'straight_line.npy'
if not golden_file.exists():
# First run: save as golden
np.save(golden_file, trajectory)
pytest.skip("Golden file created — re-run to test")
golden = np.load(golden_file)
np.testing.assert_allclose(trajectory, golden, atol=1e-4,
err_msg="Trajectory regression! Planner output changed.")
def test_obstacle_avoidance_plan(self):
start = np.array([0.3, 0.0, 0.5])
goal = np.array([0.5, 0.2, 0.3])
obstacles = [Sphere(center=[0.4, 0.1, 0.4], radius=0.05)]
trajectory = planner.plan(start, goal, obstacles=obstacles)
# Verify no collisions
for point in trajectory:
for obs in obstacles:
dist = np.linalg.norm(point[:3] - obs.center)
assert dist > obs.radius, \
f"Collision at {point[:3]}, dist={dist:.4f}"
Simulation Testing
class SimulationTestHarness:
"""Run behavior tests in simulation with deterministic physics"""
def __init__(self, sim_config):
self.sim = MuJoCoSimulator(sim_config)
self.sim.set_seed(42) # Deterministic physics
def test_pick_and_place(self):
"""Full pick-and-place task in simulation"""
# Setup scene
self.sim.reset()
self.sim.spawn_object('red_block', pose=[0.4, 0.1, 0.02])
# Run behavior tree
bt = create_pick_place_tree()
bt.setup(sim=self.sim)
max_steps = 1000
for step in range(max_steps):
bt.tick()
self.sim.step()
if bt.root.status == Status.SUCCESS:
break
# Verify outcome
block_pose = self.sim.get_object_pose('red_block')
target_pose = np.array([0.5, -0.1, 0.02])
assert np.linalg.norm(block_pose[:3] - target_pose) < 0.02, \
f"Block not at target: {block_pose[:3]} vs {target_pose}"
assert step < max_steps - 1, "Task did not complete in time"
def test_collision_safety(self):
"""Robot should never collide with table"""
self.sim.reset()
self.sim.spawn_object('obstacle', pose=[0.35, 0.0, 0.15])
trajectory = planner.plan_with_obstacle(
start=[0.3, -0.2, 0.3],
goal=[0.3, 0.2, 0.3])
for joints in trajectory:
self.sim.set_joint_positions(joints)
contacts = self.sim.get_contacts()
robot_contacts = [c for c in contacts
if 'robot' in c.body1 or 'robot' in c.body2]
assert len(robot_contacts) == 0, \
f"Robot collision detected: {robot_contacts}"
CI/CD Pipeline for Robotics
# .github/workflows/robotics_ci.yml
name: Robotics CI
on: [push, pull_request]
jobs:
unit-tests:
runs-on: ubuntu-22.04
container:
image: ros:humble-ros-base
steps:
- uses: actions/checkout@v4
- name: Install dependencies
run: |
apt-get update
rosdep install --from-paths src --ignore-src -y
pip install pytest hypothesis numpy
- name: Build
run: |
source /opt/ros/humble/setup.bash
colcon build --packages-select my_pkg
source install/setup.bash
- name: Unit tests
run: |
source install/setup.bash
colcon test --packages-select my_pkg
colcon test-result --verbose
integration-tests:
runs-on: ubuntu-22.04
container:
image: ros:humble-ros-base
needs: unit-tests
steps:
- uses: actions/checkout@v4
- name: Build full workspace
run: |
source /opt/ros/humble/setup.bash
colcon build
- name: Integration tests
run: |
source install/setup.bash
launch_test src/my_pkg/test/test_integration.py
simulation-tests:
runs-on: ubuntu-22.04
needs: integration-tests
steps:
- uses: actions/checkout@v4
- name: Setup MuJoCo
run: pip install mujoco
- name: Simulation tests
run: pytest tests/simulation/ -v --timeout=120
Testing Anti-Patterns
1. Testing with sleep()
# BAD: Flaky, slow, non-deterministic
def test_message_received():
pub.publish(msg)
time.sleep(2.0) # Hope it arrives!
assert received
# GOOD: Event-driven waiting with timeout
def test_message_received():
pub.publish(msg)
event = threading.Event()
sub = create_sub(callback=lambda m: event.set())
assert event.wait(timeout=5.0), "Message not received within timeout"
2. Not Testing Failure Cases
# BAD: Only test the happy path
# GOOD: Test failures explicitly
def test_planner_unreachable_goal(self):
"""Planner should return None for unreachable goals"""
result = planner.plan(start, unreachable_goal)
assert result is None
def test_perception_no_objects(self):
"""Perception should return empty list when no objects visible"""
empty_image = np.zeros((256, 256, 3), dtype=np.uint8)
detections = perception.detect(empty_image)
assert detections == []
3. Non-Deterministic Tests
# BAD: Random seed changes between runs
trajectory = planner.plan(start, goal) # Uses random sampling internally
# GOOD: Fix random seed for reproducibility
def test_rrt_planner(self):
np.random.seed(42)
trajectory = planner.plan(start, goal, seed=42)
assert len(trajectory) > 0
Source: Ming-Start/robotics-skills — distributed by TomeVault.