Software Fault Injection (SFI) is an established technique for assessing the robustness of a software under test by exposing it to faults in its operational environment. Depending on the complexity of this operational environment, the complexity of the software under test, and the number and type of faults, a thorough SFI assessment can entail (a) numerous experiments and (b) long experiment run times, which both contribute to a considerable execution time for the tests. In order to counteract this increase when dealing with complex systems, recent works propose to exploit parallel hardware to execute multiple experiments at the same time. While Parallel fault Injections (PAIN) yield higher experiment throughput, they are based on an implicit assumption of non-interference among the simultaneously executing experiments. In this paper we investigate the validity of this assumption and determine the trade-off between increased throughput and the accuracy of experimental results obtained from PAIN experiments

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In urban search and rescue scenarios, typical applications of robots include autonomous exploration of possibly dangerous sites, and the recognition of victims and other objects of interest. In complex scenarios, relying on only one type of sensor is often misleading, while using complementary sensors frequently helps improving the performance. To that end, we propose a probabilistic world model that leverages information from heterogeneous sensors and integrates semantic attributes. This method of reasoning about complementary information is shown to be advantageous, yielding increased reliability compared to considering all sensors separately. We report results from several experiments with a wheeled USAR robot in a complex indoor scenario. The robot is able to learn an accurate map, and to detect real persons and signs of hazardous materials based on inertial sensing, odometry, a laser range finder, visual detection, and thermal imaging. The results show that combining heterogeneous sensor information increases the detection performance, and that semantic attributes can be successfully integrated into the world model.

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