Multi-Robot systems in automotive are safety-critical systems that consist of collaborating-aware robots and components that interact with external components, the environment, or humans at run-time. This implies a significant complexity for the system engineer to design, model, validate the system, and optimize the cycle time, including considering unexpected events at run-time. This paper addresses this challenge by describing a model-driven engineering approach that formally designs the system under the consideration of uncertainties and at run-time optimizes the system actions using learning-based approaches. We implemented this approach in an industrial-inspired case study of a spot-welding multi-robot cell. Based on the system requirements, we generate valid system strategies that consider unexpected events such as robot interruptions and failures. Considering movement and interruption time models, we implemented a reinforcement learning method to optimize system actions at run-time. We show that via simulations and learning, our approach can be used to synthesize time-efficient schedules for robot task assignments that improve the overall cycle time.

In automotive production cells, complex processes involving multiple robots must be optimized for cycle time. We investigated using symbolic GR(1) controller synthesis for automating multi-robot task planning. Given a specification of the order of tasks and states to avoid, often multiple valid strategies can be computed; in many states there are multiple choices to satisfy the specification, such as choosing different robots to perform a certain task. To determine the best choices under the consideration of movement times and probabilities that robots may be interrupted for repairs or corrections, we combine the execution of the synthesized controller with Monte Carlo Tree Search (MCTS), a heuristic AI-planning technique. The result is a model-at-run-time approach that we present by the example of a multi-robot spot welding cell. We report on experiments showing that the approach (1) can reduce cycle times by choosing time-efficient movement sequences and (2) can choose executions that react efficiently to interruptions by choosing to delay tasks that, if an interruption of one robot should occur later, can be reallocated to another robot. Most interestingly, we found, however, that (3) in some cases there is a conflict between time-efficient movement sequences and ones that may react efficiently to probable future interruptions—and when interruption probabilities are low, increasing the time allocated for MCTS, i.e., increasing the number of sample simulations made by MCTS, does not improve cycle time.

Software-intensive systems often consist of cooperating reactive components. In mobile and reconfigurable systems, their topology changes at run-time, which influences how the components must cooperate. The Scenario Modeling Language (SML) offers a formal approach for specifying the reactive behavior such systems that aligns with how humans conceive and communicate behavioral requirements. Simulation and formal checks can find specification flaws early. We present a framework for the Scenario-based Programming (SBP) that reflects the concepts of SML in Java and makes the scenario modeling approach available for programming. SBP code can also be generated from SML and extended with platform-specific code, thus streamlining the transition from design to implementation. As an example serves a car-to-x communication system. Demo video and artifact: http://scenariotools.org/esecfse-2017-tool-demo/