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Titlebook: Software Engineering for Robotics; Ana Cavalcanti,Brijesh Dongol,Jim Woodcock Book 2021 Springer Nature Switzerland AG 2021 Software Engin

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樓主: Optician
11#
發(fā)表于 2025-3-23 13:04:46 | 只看該作者
12#
發(fā)表于 2025-3-23 16:22:46 | 只看該作者
,Verification of Autonomous Robots: A Roboticist’s Bottom-Up Approach,he most critical parts of their software are robust and reliable. Moreover, autonomous robots embed onboard deliberation functions. This is what makes them autonomous but open for new challenges. There are many approaches to consider for the V&V of AR software, e.g. write high-level specifications a
13#
發(fā)表于 2025-3-23 19:00:34 | 只看該作者
,RoboStar Technology: A Roboticist’s Toolbox for Combined Proof, Simulation, and Testing, basis to program tool-specific simulations. The simulation code, written in general or proprietary programming languages, is, however, the only full account of the robotic system. Here, we present the RoboStar technology, a modern approach to design that supports automatic generation of simulation
14#
發(fā)表于 2025-3-24 00:57:16 | 只看該作者
CorteX: A Software Framework for Interoperable, Plug-and-Play, Distributed, Robotic Systems of Syst is resulting in ever more complex robotics installations being deployed, maintained, and extended over long periods of time. Additionally, the unstructured, experimental, or unknown operational conditions frequently result in new or changing system requirements, meaning extension and adaptation is
15#
發(fā)表于 2025-3-24 05:00:23 | 只看該作者
Mutation Testing for RoboChart,leading to a set of mutants of .. Given a mutant . of ., we check whether . is a valid implementation of ., and, if it is not, we find a test case that demonstrates this: a test case that reveals the seeded fault. In order to automate this approach, we used the Wodel tool to seed faults and a combin
16#
發(fā)表于 2025-3-24 10:31:14 | 只看該作者
17#
發(fā)表于 2025-3-24 13:20:56 | 只看該作者
RoboStar Technology: Modelling Uncertainty in RoboChart Using Probability, we discuss first why we need probability by surveying how we use it in designing robots. To illustrate our approach, we focus on the verification of probabilistic robotic algorithms for pose estimation. We verify a model-fitting algorithm: random sample consensus (Ransac). This is a popular algorit
18#
發(fā)表于 2025-3-24 17:55:03 | 只看該作者
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發(fā)表于 2025-3-24 19:54:56 | 只看該作者
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發(fā)表于 2025-3-24 23:49:03 | 只看該作者
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