Deploying Constraint Programming for Testing ABB's Painting Robots

By Mossige, Morten; Gotlieb, Arnaud et al. | AI Magazine, Summer 2017 | Go to article overview

Deploying Constraint Programming for Testing ABB's Painting Robots


Mossige, Morten, Gotlieb, Arnaud, Meling, Hein, AI Magazine


* This report explores the use of constraint programming for the validation of ABB Robotics'painting robots.

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Testing software systems is a difficult cognitive task that requires human skills to generate test scenarios and to predict the expected behaviors of a system. Moreover, coming up with possible failure scenarios that may arise during system operation and determining triggers for these cases is a major challenge.

ABB Robotics' painting robots are advanced distributed systems that must be thoroughly tested before they are shipped to customers, mostly from the automotive and avionics domains. Improving the overall quality of painting robots has always been of utmost importance because it is viewed as a competitive advantage in the market for industrial robots--an error in the control system of these robots, uncovered in the field, can have serious economic consequences. Among the many possible failure sources are those related to timing the triggering of different subsystems. For example, an overlap-error scenario can occur when the robot's speed increases and switching between different paint spray patterns occurs too rapidly. In principle, the control system should detect overlap scenarios, send an appropriate error message, and possibly shut down the system. However, it may happen that the robot does not work as expected, for example due to bugs in the design or implementation of the control system. Creating test scenarios targeted at finding such subtle faults is nontrivial.

Faced with this challenge, researchers and engineers from Certus, a Norwegian research-based innovation center, have teamed up to explore the usage of constraint programming (CP) for the validation of ABB's painting robots. Constraint-satisfaction techniques over finite domains were selected because of their versatility in dealing with heterogeneous constraints derived from the design and implementation of ABB's control system for paint robots. We used a combination of constraint propagation with different filtering consistencies and dedicated search heuristics. With these techniques it was possible to propose a cost-effective solution for test case generation for validating the control systems of ABB's painting robots (Mossige, Gotlieb, and Meling 2015; Mossige, Gotliev, and Meling 2014a, 2014b). Currently, these AI techniques are commonly supported by modern CP solvers that ease their adoption in industrial contexts. However, using a constraint solver to generate tests can be time consuming, which is at odds with the need to run both test generation and test execution as part of a continuous integration cycle. Indeed, continuous integration is a software engineering practice, where the result of test execution, following a source code change, should be reported back to the developer quickly.

Despite the strong technical culture of ABB Robotics and the availability of excellent textbooks, deploying CP was not easy, as there are almost no guidelines on how to model with constraints and how to integrate CP in an industrial software production process. However, we managed to develop such a model and to deploy it at ABB Robotics by adding the constraint-solving process to the continuous integration process. The model has now been part of ABB's continuous integration process for more than 24 months of daily operations. After having collected data about its bug-finding capabilities and efficiency, we perform a thorough analysis of its benefits and drawbacks, which led us to draw some lessons learned from this experience.

Bug-Finding Capabilities of ABB's CP Model

For the purpose of validating the CP model itself, known bugs were reintroduced in the paint control systems to examine the capabilities of the model to find them. After some tuning, the model was able to generate test scenarios that could find all the re-introduced bugs. This was considered a significant success and justified the continuation of the research and innovation activities. …

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