Foxglove

Collect data from the vehicle's sensors (LiDAR, cameras, IMU) and store it in MCAP format.

Load the data into Foxglove Studio and visualize the sensor data streams.

Use the timeline to scrub through the data and identify the point where the navigation error occurred.

Analyze the sensor data and identify the root cause of the error (e.g., sensor malfunction, incorrect mapping).

Record the arm's joint angles, velocities, and forces during operation.

Visualize the data in Foxglove Studio to identify areas for improvement.

Use the data to train a machine learning model to optimize the arm's movements.

Deploy the optimized model to the arm and monitor its performance.

Capture data from the robot's cameras and LiDAR sensors while it navigates the warehouse.

Annotate the data with ground truth labels to indicate the location of objects and obstacles.

Visualize the sensor data and annotations in Foxglove Studio.

Evaluate the performance of the perception algorithm by comparing its output to the ground truth labels.

Simulate the robot's environment and task using a physics engine.

Use Foxglove to visualize the robot's state, actions, and rewards during training.

Monitor the learning progress and adjust the training parameters as needed.

Deploy the trained policy to the real robot and evaluate its performance.

Collect data from the robots' sensors and systems (e.g., battery voltage, CPU usage, error logs).

Stream the data to a central server and visualize it in Foxglove Studio.

Set up alerts to notify operators of potential issues (e.g., low battery, high CPU usage).

Use the data to identify trends and patterns that can inform preventative maintenance strategies.

Record data from the vehicle's sensors during test drives.

Visualize the data in Foxglove Studio and compare it to simulation results.

Annotate the data with information about the driving environment and the vehicle's behavior.

Use the data to identify potential issues and refine the feature's design.

Collect data from the robot's sensors while it performs deliveries.

Visualize the robot's path and the obstacles it encounters in Foxglove Studio.

Analyze the data to identify areas where the path planning algorithm can be improved.

Implement the improvements and test them in simulation and on the real robot.