Tobias Doernbach

• Autonomous robotics in the real world is based on complex, interacting systems which demand for major integration efforts. In particular, the validation of such systems in order to reach production state raises the stakes in terms of parallelized testing and inter-workgroup interfacing. This thesis proposes to make use of a comprehensive simulation framework in order to increase the validation efficiency of such systems. Seamless integration into the testing pipeline allows for rapid development of components and validation under controlled conditions. This Continuous System Integration paradigm can be utilized to replace missing parts of the system, nevertheless ensuring realistic conditions, during the software development phase. Moreover, it is applicable during deployment in field trials as well where business logic can be replaced by simulated components with no overhead. Additionally, with the foundations of a simulation framework being described and embedded into the context of system integration, this framework can further on be utilized for various knowledge expansion tasks, including benchmarking, optimization and autonomous reasoning needs. To open up these use cases, an abstract Simulation-Aided Knowledge Expansion concept is presented to run specified tasks in a loop within the simulation environment, making use of high-fidelity alignment with sensor data recorded in field trials. This concept allows for generating and expanding knowledge using the simulation in the sense that every iteration yields results which are supportive for use cases like benchmarking algorithms, optimizing parameters or increasing the basis of facts used in autonomous reasoning. For each of these use cases, an implementation of the concept is introduced and evaluated in detail.