What are the most effective ways to visualize a robot's progress in real-time?

Creating a map is a simple and effective way to visualize a robot's progress and surroundings. You can use 2D or 3D maps to track the robot's location, trajectory, and environment. You can also add information such as sensor readings, obstacles, landmarks, and goals to the map. Depending on your application, you can choose different map formats, such as occupancy grids, point clouds, elevation maps, or semantic maps. You can use tools like ROS, Gazebo, or RViz to create and display maps from your robot's data. This can help you understand the robot's behavior and performance, and make informed decisions

RQT plots in the ROS environment are an extremely useful tool for tracking a robot's progress. To evaluate a robot's performance, various metrics/KPIs are typically utilized. Examples include controller errors such as cross-track and along-track errors, localization errors, and sensor covariance. To effectively use RQT plots, it is common practice to create a node that calculates and publishes the errors on a specific topic. The RQT plot tool then allows you to select and plot data on a custom topic, facilitating an in-depth analysis of the robot's performance metrics.

In mobile robotics, tracking error is good indicator for the robot progress. It is just the difference between reference and robots dynamics like x, y in 2d and the orientation angle, we can also use the linear and angular velocities that indicate robots progress as a dot.

Using graphs and charts is a common and powerful way to visualize a robot's progress and performance. You can plot various metrics and indicators over time, such as reward, loss, accuracy, speed, energy, distance, or any other relevant variable. You can compare different algorithms, models, or settings by using multiple lines, bars, or curves on the same graph or chart. This can help you analyze how the robot is learning, adapting, and performing in different situations and tasks. You can use tools like Matplotlib, Plotly, or TensorBoard to create and display graphs and charts from your robot's data, and make data-driven decisions.

Using images and videos is a third way to visualize a robot's progress and performance. You can capture and show what the robot is seeing and doing, and understand how it is perceiving and interacting with its environment, and how it is responding to various inputs and commands. You can use images and videos to show things like camera feeds, depth maps, segmentation masks, optical flow, or object detection. You can also use images and videos to demonstrate the robot's skills, behaviors, or outcomes. You can use tools like OpenCV, PyTorch, or TensorFlow to create and display images and videos from your robot's data, and share your results with others

Using dashboards and widgets is a fourth way to visualize a robot's progress and status. You can organize and present multiple types of visualizations in a single interface, and monitor and control the robot's parameters and actions in a convenient and customizable way. You can use dashboards and widgets to show things like maps, graphs, charts, images, videos, gauges, sliders, buttons, or text boxes. You can also use dashboards and widgets to interact with the robot, such as sending commands, changing settings, or receiving feedback. You can use tools like Jupyter, Streamlit, or Dash to create and display dashboards and widgets from your robot's data, and improve your workflow and productivity.

During our robotics project, we utilized a dashboard to track our autonomous robot's progress. With widgets displaying sensor data, navigation paths, and task status, it provided real-time insights. In a challenging indoor navigation task, the dashboard facilitated monitoring of the robot's location, obstacle detection, and battery levels. Its interactive features allowed us to adjust the robot's trajectory on-the-fly, ensuring successful mission completion.

Using virtual and augmented reality is a fifth and more advanced way to visualize a robot's progress and performance. You can immerse yourself in the robot's perspective and environment, and experience and evaluate its behavior and adaptation in a realistic and engaging way. You can use virtual and augmented reality to show things like 3D maps, images, videos, holograms, or animations. You can also use virtual and augmented reality to interact with the robot, such as moving, pointing, speaking, or gesturing. This can help you test, validate, and showcase your robot's capabilities and potential in various applications.

Two main methods to visualize a robot's progress in real-time are maps and overlays, which create a digital map of the robot's environment, and graphs and charts, which represent various metrics and performance indicators over time. Maps track the robot's location, path, and destination, while charts represent metrics like reward, error, accuracy, speed, distance traveled, and energy consumption. Tools like ROS, Gazebo, and RViz can be used to create and display these maps with real-time data.

Things to take in account are to make sure that the way to track the progress works well with the nature of such progress. And inherent properties like update rate should be taken account for complex systems, where usually data is overlapped and might confuse interpreters.