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Details of Grant 

EPSRC Reference: EP/R025134/2
Title: RoboTest: Systematic Model-Based Testing and Simulation of Mobile Autonomous Robots
Principal Investigator: Hierons, Professor R
Other Investigators:
Dongol, Professor B
Researcher Co-Investigators:
Project Partners:
Adelard LLP Blue Bear Systems Research Ltd Bristol Robotics Laboratory (BRL)
D-RisQ Ltd ESC (Engineering Safety Consultants Ltd) Federal University of Pernambuco
Intel Corporation Ltd Liverpool Data Research Associate LDRA University of Liverpool
Verified Systems International GmbH
Department: Computer Science
Organisation: University of Sheffield
Scheme: Standard Research
Starts: 03 September 2018 Ends: 31 August 2024 Value (£): 575,877
EPSRC Research Topic Classifications:
Robotics & Autonomy Software Engineering
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:  
Summary on Grant Application Form
Mobile and autonomous robots have an increasingly important role in industry and the wider society; from driverless vehicles to home assistance, potential applications are numerous. The UK government identified robotics as a key technology that will lead us to future economic growth (tinyurl.com/q8bhcy7). They have recognised, however, that autonomous robots are complex and typically operate in ever-changing environments (tinyurl.com/o2u2ts7). How can we be confident that they perform useful functions, as required, but are safe?

It is standard practice to use testing to check correctness and safety. The software-development practice for robotics typically includes testing within simulations, before robots are built, and then testing of the actual robots. Simulations have several benefits: we can test early, and test execution is cheaper and faster. For example, simulation does not require a robot to move physically. Testing with the real robots is, however, still needed, since we cannot be sure that a simulation captures all the important aspects of the hardware and environment.

In the current scenario, test generation is typically manual; this makes testing expensive and unreliable, and introduces delays. Manual test generation is error-prone and can lead to tests that produce the wrong verdict. If a test incorrectly states that the robot has a failure, then developers have to investigate, with extra cost and time. If a test incorrectly states that the robot behaves as expected, then a faulty system may be released. Without a systematic approach, tests may also identify infeasible environments; such tests cannot be used with the real robot. To make matters worse, manual test generation limits the number of tests produced.

All this affects the cost and quality of robot software, and is in contrast with current practice in other safety-critical areas, like the transport industry, which is highly regulated. Translation of technology, however, is not trivial. For example, lack of a driver to correct mistakes or respond to unforeseen circumstances leads to a much larger set of working conditions for an autonomous vehicle. Another example is provided by probabilistic algorithms, which make the robot behaviour nondeterministic, and so, difficult to repeat in testing and more difficult to characterise as correct or not.

We will address all these issues with novel automated test-generation techniques for mobile and autonomous robots. To use our techniques, a RoboTest tester constructs a model of the robot using a familiar notation already employed in the design of simulations and implementations. After that, instead of spending time designing simulation scenarios, the RoboTest tester, with the push of a button, generates tests. With RoboTest, testing is cheaper, since it takes less time, and is more effective, because the RoboTest tester can use many more tests, especially when using a simulation.

To execute the tests, the RoboTest tester can choose from a few simulators employing a variety of approaches to programming. Execution of the tests also follows the push of a button. Yet another button translates simulation to deployment tests. So, the RoboTest tester can trace back the results from the deployment tests to the simulation and the original model. So, the RoboTest tester is in a strong position to understand the reality gap between the simulation and the real world.

The RoboTest tester knows that the verdicts for the tests are correct, and understands what the testing achieves; for example, it can be guaranteed to find faults of an identified class. So, the RoboTest tester can answer the very difficult question: have we tested enough?

In conclusion, RoboTest will move the testing of mobile and autonomous robots onto a sound footing. RoboTest will make testing more efficient and effective in terms of person effort, and so, achieve longer term reduced costs.
Key Findings
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Potential use in non-academic contexts
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Organisation Website: http://www.shef.ac.uk