E.G. Alberts
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3 records found
1
Journal article
(2025)
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Elvin Alberts, Ilias Gerostathopoulos, Ivano Malavolta, Carlos Hernández Corbato, Patricia Lago
Context:
Robotics software architecture-based self-adaptive systems (RSASSs) are robotics systems made robust to runtime uncertainty by adapting their software architectures. The research landscape of RSASS approaches is multidisciplinary and fragmented, with many aspects still unexplored or ineffectively shared among communities involved.
Objective:
We aim at identifying, classifying, and analyzing the state of the art of existing approaches for RSASSs from the following perspectives: (i) the key characteristics of approaches and (ii) the evaluation strategies applied by researchers.
Method:
We apply the systematic mapping research method. We selected
primary studies via automatic, manual, and snowballing-based search and selection procedures. We rigorously defined and applied a classification framework composed of 32 parameters and synthesize the obtained data to produce a comprehensive overview of the state of the art.
Results:
This work contributes (i) a rigorously defined classification framework for studies on RSASSs, (ii) a systematic map of the research efforts on RSASSs, (iii) a discussion of emerging findings and implications for future research, and (iv) a publicly available replication package.
Conclusion:
This study provides a solid evidence-based overview of the state of the art in RSASS approaches. Its results can benefit RSASS researchers at different levels of seniority and involvement in RSASS research. ...
Robotics software architecture-based self-adaptive systems (RSASSs) are robotics systems made robust to runtime uncertainty by adapting their software architectures. The research landscape of RSASS approaches is multidisciplinary and fragmented, with many aspects still unexplored or ineffectively shared among communities involved.
Objective:
We aim at identifying, classifying, and analyzing the state of the art of existing approaches for RSASSs from the following perspectives: (i) the key characteristics of approaches and (ii) the evaluation strategies applied by researchers.
Method:
We apply the systematic mapping research method. We selected
primary studies via automatic, manual, and snowballing-based search and selection procedures. We rigorously defined and applied a classification framework composed of 32 parameters and synthesize the obtained data to produce a comprehensive overview of the state of the art.
Results:
This work contributes (i) a rigorously defined classification framework for studies on RSASSs, (ii) a systematic map of the research efforts on RSASSs, (iii) a discussion of emerging findings and implications for future research, and (iv) a publicly available replication package.
Conclusion:
This study provides a solid evidence-based overview of the state of the art in RSASS approaches. Its results can benefit RSASS researchers at different levels of seniority and involvement in RSASS research. ...
Context:
Robotics software architecture-based self-adaptive systems (RSASSs) are robotics systems made robust to runtime uncertainty by adapting their software architectures. The research landscape of RSASS approaches is multidisciplinary and fragmented, with many aspects still unexplored or ineffectively shared among communities involved.
Objective:
We aim at identifying, classifying, and analyzing the state of the art of existing approaches for RSASSs from the following perspectives: (i) the key characteristics of approaches and (ii) the evaluation strategies applied by researchers.
Method:
We apply the systematic mapping research method. We selected
primary studies via automatic, manual, and snowballing-based search and selection procedures. We rigorously defined and applied a classification framework composed of 32 parameters and synthesize the obtained data to produce a comprehensive overview of the state of the art.
Results:
This work contributes (i) a rigorously defined classification framework for studies on RSASSs, (ii) a systematic map of the research efforts on RSASSs, (iii) a discussion of emerging findings and implications for future research, and (iv) a publicly available replication package.
Conclusion:
This study provides a solid evidence-based overview of the state of the art in RSASS approaches. Its results can benefit RSASS researchers at different levels of seniority and involvement in RSASS research.
Robotics software architecture-based self-adaptive systems (RSASSs) are robotics systems made robust to runtime uncertainty by adapting their software architectures. The research landscape of RSASS approaches is multidisciplinary and fragmented, with many aspects still unexplored or ineffectively shared among communities involved.
Objective:
We aim at identifying, classifying, and analyzing the state of the art of existing approaches for RSASSs from the following perspectives: (i) the key characteristics of approaches and (ii) the evaluation strategies applied by researchers.
Method:
We apply the systematic mapping research method. We selected
primary studies via automatic, manual, and snowballing-based search and selection procedures. We rigorously defined and applied a classification framework composed of 32 parameters and synthesize the obtained data to produce a comprehensive overview of the state of the art.
Results:
This work contributes (i) a rigorously defined classification framework for studies on RSASSs, (ii) a systematic map of the research efforts on RSASSs, (iii) a discussion of emerging findings and implications for future research, and (iv) a publicly available replication package.
Conclusion:
This study provides a solid evidence-based overview of the state of the art in RSASS approaches. Its results can benefit RSASS researchers at different levels of seniority and involvement in RSASS research.
SUAVE
An Exemplar for Self-Adaptive Underwater Vehicles
Conference paper
(2023)
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Gustavo Rezende Silva, Juliane Pasler, Jeroen Zwanepol, Elvin Alberts, S. Lizeth Tapia Tarifa, Ilias Gerostathopoulos, Einar Broch Johnsen, Carlos Hernandez Corbato
Once deployed in the real world, autonomous underwater vehicles (AUVs) are out of reach for human supervision yet need to take decisions to adapt to unstable and unpredictable environments. To facilitate research on self-adaptive AUVs, this paper presents SUAVE, an exemplar for two-layered system-level adaptation of AUVs, which clearly separates the application and self-adaptation concerns. The exemplar focuses on a mission for underwater pipeline inspection by a single AUV, implemented as a ROS 2-based system. This mission must be completed while simultaneously accounting for uncertainties such as thruster failures and unfavorable environmental conditions. The paper discusses how SUAVE can be used with different self-adaptation frameworks, illustrated by an experiment using the Metacontrol framework to compare AUV behavior with and without self-adaptation. The experiment shows that the use of Metacontrol to adapt the AUV during its mission improves its performance when measured by the overall time taken to complete the mission or the length of the inspected pipeline.
...
Once deployed in the real world, autonomous underwater vehicles (AUVs) are out of reach for human supervision yet need to take decisions to adapt to unstable and unpredictable environments. To facilitate research on self-adaptive AUVs, this paper presents SUAVE, an exemplar for two-layered system-level adaptation of AUVs, which clearly separates the application and self-adaptation concerns. The exemplar focuses on a mission for underwater pipeline inspection by a single AUV, implemented as a ROS 2-based system. This mission must be completed while simultaneously accounting for uncertainties such as thruster failures and unfavorable environmental conditions. The paper discusses how SUAVE can be used with different self-adaptation frameworks, illustrated by an experiment using the Metacontrol framework to compare AUV behavior with and without self-adaptation. The experiment shows that the use of Metacontrol to adapt the AUV during its mission improves its performance when measured by the overall time taken to complete the mission or the length of the inspected pipeline.
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 afore-mentioned 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.
...
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 afore-mentioned 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.