How do you create reliable robot tests?

Getting into test automation is a continuous journey. Once you start, you will see the quickest success by implementing testing for the simple and basic tests before you move on to more complex ones. This also leaves you with more satisfaction as you have quick wins! In addition to the mentioned tests, we conduct in the payment industry also regression testing, stress testing and EMV certification testing.

Implementing comprehensive robot testing across various levels ensures robust system performance. Unit testing verifies the functionality of individual software components, mitigating potential errors at the foundational level. Integration testing assesses the seamless interaction between these components, ensuring cohesive system behavior and data flow. System testing evaluates the overall system performance, guaranteeing hardware-software synergy and resilience in diverse environmental conditions. Acceptance testing validates the system's compliance with end-user requirements, emphasizing usability and safety, thereby fostering stakeholder confidence and satisfaction in the robot's capabilities.

Simulation in robot testing offers a virtual environment, paralleling the structure of traditional testing levels like unit, integration, system, and acceptance testing. It allows for detailed scrutiny from individual components to the entire system and end-user satisfaction, though challenges such as model accuracy and software integration remain. Choosing appropriate tools like Gazebo or CoppeliaSim, and balancing virtual and real-world testing, are crucial for reliable results.

Robot testing is like building a skyscraper—start from the ground up. You’ve got unit tests for individual components, integration tests for how those parts work together, and system tests for the entire robot’s performance. Unit tests are your foundation, making sure each piece, like a sensor or motor controller, works correctly. Integration tests check if these pieces play nicely together, while system tests are the grand finale, ensuring the whole robot operates as expected.

Here is the summary of Testing Levels for Robot Software 1. Unit Testing ? Focuses on individual software components or functions. ? Ensures each unit performs as designed. ? Tools: xUnit frameworks (e.g., PyTest for Python). 2. Integration Testing ? Tests interactions between integrated components. ? Ensures combined parts work together correctly. ? Tools: ROS (Robot Operating System). 3. System Testing ? Examines the entire robot as a complete system. ? Validates end-to-end functionality. ? Tools: Gazebo simulator. 4. Acceptance Testing ? Determines if the robot meets business requirements and is ready for deployment. ? Involves real-world testing scenarios. ? Tools: Custom scripts and real-world test setups.

Having a dedicated test framework will improve your overall quality and reliability of the software. It is worth to implement a framework which meets your specific requirements, even if this means that you need to invest time and resources to transfer your existing testcases from one system to another. If you are just about to begin with test automation and you have everything still in excel, the decision to change might be easier than when you use another framework already. However, utilizing a system which fully complies to your requirements and is adaptable to your needs will help you more on a long run.

I have some experience under my belt with National Instruments tech, and TestStand is also a really nice solution for developing automated tests for hardware, together with LabVIEW and co. Worth a shot, if the scale of application justifies the costs.

Selecting a robust test framework is crucial for efficient robot testing. Frameworks like PyTest and JUnit offer comprehensive features for seamless test automation, organization, and execution. Robot Framework and ROS Test streamline test discovery and reporting, enhancing test management and result analysis. Integration of test debugging tools facilitates efficient error identification and resolution, ensuring the reliability and accuracy of robot testing. Choosing a test framework that aligns with the specific programming language and platform requirements is essential for optimizing test development and maintenance, fostering a streamlined and effective testing process.

Test Framework 1. ROS (Robot Operating System) ? Provides a flexible framework for writing robot software. ? Includes tools and libraries for building, debugging, and testing. 2. Gazebo ? An open-source 3D robotics simulator. ? Allows for testing algorithms, designing robots, and simulating scenarios.

Robot testing encompasses a thorough evaluation process through four main levels: unit, integration, system, and acceptance testing. Unit testing scrutinizes individual components for correctness, while integration testing examines the interactions between these components. System testing assesses the entire robot system, ensuring overall performance and reliability. Acceptance testing verifies that the system fulfills end-user requirements and expectations, guaranteeing usability and safety. Collectively, these testing levels contribute to risk mitigation, foster collaboration across teams, and drive continuous improvement, ensuring a robust, efficient, and user-friendly robot system.

It is beneficial if you have a test set you can follow, which comes from an external institution. This is most often the case with certifications such as the EMV standard in payments. Should there be no standard available, ensure that you outline your requirements and build the test cases around them. Start easy and once you have routine and experience, enhance your testing capabilities. Make sure to always include your previous tests in a new release cycle to ensure that you don't miss out on possible bugs from a legacy software part!

Critical to think about the corner-cases here! That’s why you need someone who can truly craft the test sequence here, who deeply understands how things should work, but also how they can go terribly wrong. And foresee black swans :) With heavy-duty equipment, mishaps can get expensive really quick and ruin your afternoon!

Test Strategies 1. Behavior-Driven Development (BDD) ? Define test cases in plain language using Given-When-Then syntax. ? Ensures clear communication between developers and stakeholders. 2. Test-Driven Development (TDD) ? Write tests before writing the code. ? Ensures that the code is tested thoroughly from the beginning. 3. Continuous Integration (CI) ? Integrate and test code frequently using automated builds and tests. ? Tools: Jenkins, Travis CI, CircleCI.

I usually create hypothetical scenario in which I consider following things - 1. Actuator limitations, its directions 2. Code limitations/ singularities 3. Dependancies - Camera, Real world to Image world transformations, Pixel coordinate system limitations 4. Loop rates for ROS msgs - Sometimes it doesnt match/ needs improvement on time complexity . In this case user may have to use different library or CUDA for calculations. 5. Final testing on robot itself as sometimes, Robot may have opposite orientations - in my internship, Soft robotic arm was there but one system was right oriented but other system was left oriented. In such cases, I developed a test code to check the orientation and then updated main logic. Hope this helps!

Integrating Nav2 in ROS 2 with Gazebo provides a robust framework for simulating and testing autonomous navigation for robots. Nav2, or Navigation 2, is the navigation stack for ROS 2, designed to provide a modular and scalable approach to robot navigation. When paired with Gazebo, a powerful robot simulation tool, it allows developers to create complex environments and accurately simulate sensor data and robot dynamics.This integration enables rigorous testing of navigation algorithms in a virtual environment before deploying them to physical robots, ensuring safety and reducing development time.

Robot testing through simulation stands out for its efficiency, allowing for a virtual representation of the robot system and its interactions in a controlled environment. This method significantly cuts costs, risks, and time, while improving test coverage and consistency. However, challenges such as ensuring model accuracy, software integration, and result validation persist, necessitating a careful selection of simulation tools like Gazebo, CoppeliaSim, or Webots, and a balanced approach between virtual and real-world testing.

It is crucial to configure your test environments in the virtual space as closely as possible to replicate real-world dynamics. This approach not only ensures the safe testing of cutting-edge robot dynamics but also represents a highly cost-effective means of evaluating your systems, sparing you from the expense of utilizing valuable hardware resources before constructing them physically.

I am not a big fan of simulations, if I have the option I prefer to test on a robot and hardware device. A physical device will certainly interact completely different if you execute the same test scenario 1000 times compared to a simulation - and you don't want to miss out on those as this reflect the live scenario which we expect from the hardware in the field.

We implemented test automation to certify our payment software and were able to shorten our release cycle from six months to one month only! This is a huge advantage and would not be possible without robotic test automation. Our developers are now in a continuous testing and improvement flow, where every evening the latest firmware gets loaded on a variety of robots and tested overnight. First thing in the morning, the team comes in and does a potential bugfix, before the programing continues.

In my experience, the implementation of test automation in robotics has proven to be a game-changer, ensuring consistent and reliable test results while significantly minimizing human intervention. However, it’s crucial to acknowledge that this comes with its own set of complexities and the need for a robust system design, which is vital for maintaining the integrity of the automation process.

Test Automation 1. Automated Test Scripts ? Write scripts to automatically execute test cases. ? Use ROS and Gazebo scripts to run tests in simulated environments. 2. CI/CD Pipelines ? Automate the building, testing, and deployment process. ? Tools: GitLab CI, Jenkins, GitHub Actions.

Prepare the necessary test data, which may include input commands, sensor readings, or environmental data. Real-world data should be used wherever possible to make the tests more realistic.

Test Best Practices 1. Modular Testing ? Break down tests into smaller, manageable parts. ? Allows for easier debugging and maintenance. 2. Mocking and Stubbing ? Use mocks and stubs to isolate the component being tested. ? Simulate responses from dependencies to focus on the unit under test. 3. Code Reviews and Pair Programming ? Ensure that tests are reviewed by peers. ? Helps in catching potential issues early. 4. Comprehensive Test Coverage ? Aim for high test coverage to ensure that most of the codebase is tested. ? Use coverage tools to measure and improve coverage.

The ultimate goal in creating reliable robot test is to ensure all measurements and assessments used in the testing processes are consistent and principled. if the integrity of frameworks, strategies, and simulations are unreliable, the analyst will have a difficult time testing and depending on their data. Verification is key!