Cyber-Physical Systems (CPS) such as aircraft, automobiles, industrial robots, medical devices, and Internet-of-Things (IoT) applications, promise significant economic and societal benefits. Advances in the area of Artificial Intelligence are promoting a shift of operational responsibilities from humans to systems that redefines them as autonomous cyber-physical systems in the sense that they operate and achieve goals in complex environments that are not fully specified and constrained at design time. A key aspect of the design challenge is to demonstrate the science and effectiveness of the approaches that are making physical sensing and actuation, perception, situational assessment, decision making, and execution possible at runtime. However, this requires assurance that the system as designed will be able to deal with uncertainty, learn from experience, while reacting at the pace of the physical environment. The ability to handle such intractable high-dimensional spaces often relies on Artificial Intelligence tools, where both academia and industry are rapidly developing innovative solutions in the area of data-driven and model-based techniques, as well as their hardware and software implementation. However, CPS and autonomy challenge these design methodologies, as more freedom is left to both the environment and the control policies that can be adapted and evolved over time through learning.
ACM/IEEE DESTION provides a premier forum for researchers and engineers from academia, industry, and government to present and discuss challenges, promising solutions, and applications in design automation for Autonomous CPS and IoT. The workshop has a broad scope covering tools for modeling, simulation, synthesis, validation and verification of Autonomous CPS and IoT, and their applications in a variety of domains, such as automotive and transportation systems, avionics, robotics, buildings, grid, and medical devices.
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A reference architecture for functional interoperability in robotics
The ability of robotic platforms to be resilient to changes in demand, changes in the environment, changes in the dimensions or weight of the workpiece, replacement of a robot, in other words to be functionally interoperable, is a vexing issue for the ...
Resolute assurance arguments for cyber assured systems engineering
Resolute is a tool and language for embedding an assurance argument in a system architecture model and evaluating the validity of the associated evidence. In this paper we report on a number of extensions to Resolute that support systems engineers in ...
Embedded out-of-distribution detection on an autonomous robot platform
Machine learning (ML) is actively finding its way into modern cyber-physical systems (CPS), many of which are safety-critical real-time systems. It is well known that ML outputs are not reliable when testing data are novel with regards to model training ...
Transactive energy and solarization: assessing the potential for demand curve management and cost savings
Utilities and local power providers throughout the world have recognized the advantages of the "smart grid" to encourage consumers to engage in greater energy efficiency. The digitalization of electricity and the consumer interface enables utilities to ...
Research challenges in the design and composition of surrogate models for robust CPS: position paper
Data-driven Artificial Intelligence (AI)/Machine Learning (ML)-based models of CPS (also called Digital Twins) are becoming important in the design and control of modern CPS. CPS are a unique class of intelligent system, where the governing process ...
MircoRV32: an open source RISC-V cross-level platform for education and research
In this paper we propose μRV32 (MicroRV32) an open source RISC-V platform for education and research. μRV32 integrates several peripherals alongside a 32 bit RISC-V core interconnected with a generic bus system. It supports bare-metal applications as ...
- Proceedings of the Workshop on Design Automation for CPS and IoT