Ac-Ros

Cheng, Betty H. C.; Clark, Robert Jared; Fleck, Jonathon Emil; Langford, Michael Austin; McKinley, Philip K.

doi:10.1145/3365438.3410952

Cited by 21 publications

(7 citation statements)

References 33 publications

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“…Cheng et al [11] focuses on guaranteeing safety requirements during runtime adaptation. Goal Structuring Notation (GSN) models are used as a specification language for a central adaptation node.…”

Section: Model Based Ros Approachesmentioning

confidence: 99%

“…Furthermore, the verification should reflect the influence of other applications and models to check mission-critical reactions to changing environments. Existing approaches building on top of ROS either enable the modeling of infrastructural aspects of ROS, verify parts of ROS applications, or deal with the influence of external applications [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Our approach, Distributed Petri Nets for ROS (DiNeROS), aims to address all three aspects by using Petri nets to realize a model-driven toolchain for ROS applica-tions generating stubs as an integration target for user-defined implementations.…”

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confidence: 99%

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Distributed Petri nets for model-driven verifiable robotic applications in ROS

Ebert,

Mey,

Schöne

et al. 2024

Innovations Syst Softw Eng

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Verifying industrial robotic systems is a complex task because those systems are distributed and solely defined by their implementation instead of models of the system to be verified. Some technologies mitigate parts of this problem, e.g., robotic middleware such as the Robotic Operating System (ROS) or concrete solutions such as automata-based specification of robot behavior. However, they all lack the required modeling depth to describe the structure, behavior, and communication of the system. We introduce an improved version of our previous model-driven approach based on Petri nets, integrating these three aspects of ROS-based systems. Using a formal modeling language enables verification of the described system and the generation of complete system parts in the form of ROS nodes. This reduces testing effort because the specification of component workflows and interfaces remains formally proven, while only changed implementations have to be revalidated. We extended our previous approach with novel model transformations, which considerably improved our approach’s performance and memory requirements. We evaluate our approach in a case study involving multiple industrial robotic arms and show that the structure of and communication between ROS nodes can be described and verified.

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Section: Model Based Ros Approachesmentioning

confidence: 99%

mentioning

confidence: 99%

Distributed Petri nets for model-driven verifiable robotic applications in ROS

Ebert,

Mey,

Schöne

et al. 2024

Innovations Syst Softw Eng

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“…We describe their adaptations loops and approaches to introduce self-adaptation to a robotic system. Cheng et al authored a paper about the AC-ROS robotic system [12]. This system adapts to meet safety and energy non-functional requirements while it patrols.…”

Section: B Self-adaptation Approachesmentioning

confidence: 99%

Development and Integration of Self-Adaptation Strategies for Robotics Software

Alberts

2023

2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C)

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Robots are becoming more prevalent in industry and society as a whole. Alongside this growth their application domain is also broadening. Each application brings with it a host of potential uncertainties that the robots should be able to handle at runtime. To tackle this, the doctoral thesis outlined in this paper proposes to address three main problems. First, the current ad-hoc state of robotics software which impedes its evolution. Second, the inability to imagine every possible uncertainty at design time leading to unexpected scenarios at runtime. Third, unexpected scenarios resulting from the reality gap between the simulated environments in which robots are developed versus the real world. These unexpected scenarios may cause a system to violate its requirements, especially in our case non-functional requirements. In an attempt to solve these problems, we plan to implement a variety of self-adaptation strategies. These strategies allow systems to change their composition to handle the aforementioned unexpected events during operation autonomously. To accomplish this we will need to reason about how best to integrate these strategies into the software of existing robots, as well as how existing information available to designers regarding the robots can best be utilized to improve the strategies. Lastly, these strategies and the process through which they are integrated will be assessed in their impact across different robotic case studies. Preliminary results from the work towards the thesis are also presented, alongside a consideration of its potential industrial impact.

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“…ENTRUST [101] supports the development of an SAS driven by GSN assurance cases and verified by probabilistic models at run time. Similarly, AC-ROS [102] is a GSN model-driven self-adaptive framework for ROS-based applications, which includes self-assessment through utility functions as assurance evidence. In contrast, MoDALAS uses KAOS goal models to assess the satisfaction of system requirements of an LES at run time.…”

Section: Run-time Adaptationmentioning

confidence: 99%

MoDALAS: addressing assurance for learning-enabled autonomous systems in the face of uncertainty

et al. 2023

Self Cite

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Increasingly, safety-critical systems include artificial intelligence and machine learning components (i.e., learning-enabled components (LECs)). However, when behavior is learned in a training environment that fails to fully capture real-world phenomena, the response of an LEC to untrained phenomena is uncertain and therefore cannot be assured as safe. Automated methods are needed for self-assessment and adaptation to decide when learned behavior can be trusted. This work introduces a model-driven approach to manage self-adaptation of a learning-enabled system (LES) to account for run-time contexts for which the learned behavior of LECs cannot be trusted. The resulting framework enables an LES to monitor and evaluate goal models at run time to determine whether or not LECs can be expected to meet functional objectives and enables system adaptation accordingly. Using this framework enables stakeholders to have more confidence that LECs are used only in contexts comparable to those validated at design time.

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Ac-Ros

Cited by 21 publications

References 33 publications

Distributed Petri nets for model-driven verifiable robotic applications in ROS

Distributed Petri nets for model-driven verifiable robotic applications in ROS

Development and Integration of Self-Adaptation Strategies for Robotics Software

MoDALAS: addressing assurance for learning-enabled autonomous systems in the face of uncertainty

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