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Exploring Gridmap-based Interfaces for the Remote Control of UAVs under Bandwidth Limitations
(2017)
RPSL meets lightning: A model-based approach to design space exploration of robot perception systems
(2017)
We present a universal modular robot architecture. A robot consists of the following intelligent modules: central control unit (CCU), drive, actuators, a vision unit and sensor input unit. Software and hardware of the robot fit into this structure. We define generic interface protocols between these units. If the robot has to solve a new application and is equipped with a different drive, new actuators and different sensors, only the program for the new application has to be loaded into the CCU. The interfaces to the drive, the vision unit and the other sensors are plug-and-play interfaces. The only constraint for the CCU-program is the set of commands for the actuators.
Competitions for Benchmarking: Task and Functionality Scoring Complete Performance Assessment
(2015)
As robots are becoming ubiquitous and more capable, the need for introducing solid robot software development methods is pressing to increase robots' task spectrum. This thesis is concerned with improving software engineering of robot perception systems. The presented research employs a model-based approach to provide the means to represent knowledge about robotics software. The thesis is divided into three parts, namely research on the specification, deployment and adaptation of robot perception systems.
RoCKIn@Work was focused on benchmarks in the domain of industrial robots. Both task and functionality benchmarks were derived from real world applications. All of them were part of a bigger user story painting the picture of a scaled down real world factory scenario. Elements used to build the testbed were chosen from common materials in modern manufacturing environments. Networked devices, machines controllable through a central software component, were also part of the testbed and introduced a dynamic component to the task benchmarks. Strict guidelines on data logging were imposed on participating teams to ensure gathered data could be automatically evaluated. This also had the positive effect that teams were made aware of the importance of data logging, not only during a competition but also during research as useful utility in their own laboratory. Tasks and functionality benchmarks are explained in detail, starting with their use case in industry, further detailing their execution and providing information on scoring and ranking mechanisms for the specific benchmark.
We benchmark the robustness of maximum likelihood based uncertainty estimation methods to outliers in training data for regression tasks. Outliers or noisy labels in training data results in degraded performances as well as incorrect estimation of uncertainty. We propose the use of a heavy-tailed distribution (Laplace distribution) to improve the robustness to outliers. This property is evaluated using standard regression benchmarks and on a high-dimensional regression task of monocular depth estimation, both containing outliers. In particular, heavy-tailed distribution based maximum likelihood provides better uncertainty estimates, better separation in uncertainty for out-of-distribution data, as well as better detection of adversarial attacks in the presence of outliers.