Software Engineering for Self-Adaptive Robotics: A Research Agenda
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911650652618752 |
|---|---|
| author | Sartaj, Hassan Ali, Shaukat Cavalcanti, Ana Esterle, Lukas Gomes, Cláudio Larsen, Peter Gorm Tefas, Anastasios Woodcock, Jim Zhang, Houxiang |
| author_facet | Sartaj, Hassan Ali, Shaukat Cavalcanti, Ana Esterle, Lukas Gomes, Cláudio Larsen, Peter Gorm Tefas, Anastasios Woodcock, Jim Zhang, Houxiang |
| contents | Self-adaptive robotic systems operate autonomously in dynamic and uncertain environments, requiring robust real-time monitoring and adaptive behaviour. Unlike traditional robotic software with predefined logic, self-adaptive robots exploit artificial intelligence (AI), machine learning, and model-driven engineering to adapt continuously to changing conditions, thereby ensuring reliability, safety, and optimal performance. This paper presents a research agenda for software engineering in self-adaptive robotics, structured along two dimensions. The first concerns the software engineering lifecycle, requirements, design, development, testing, and operations, tailored to the challenges of self-adaptive robotics. The second focuses on enabling technologies such as digital twins and AI-driven adaptation, which support runtime monitoring, fault detection, and automated decision-making. We identify open challenges, including verifying adaptive behaviours under uncertainty, balancing trade-offs between adaptability, performance, and safety, and integrating self-adaptation frameworks like MAPE K/MAPLE-K. By consolidating these challenges into a roadmap toward 2030, this work contributes to the foundations of trustworthy and efficient self-adaptive robotic systems capable of meeting the complexities of real-world deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19629 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Software Engineering for Self-Adaptive Robotics: A Research Agenda Sartaj, Hassan Ali, Shaukat Cavalcanti, Ana Esterle, Lukas Gomes, Cláudio Larsen, Peter Gorm Tefas, Anastasios Woodcock, Jim Zhang, Houxiang Software Engineering Robotics Self-adaptive robotic systems operate autonomously in dynamic and uncertain environments, requiring robust real-time monitoring and adaptive behaviour. Unlike traditional robotic software with predefined logic, self-adaptive robots exploit artificial intelligence (AI), machine learning, and model-driven engineering to adapt continuously to changing conditions, thereby ensuring reliability, safety, and optimal performance. This paper presents a research agenda for software engineering in self-adaptive robotics, structured along two dimensions. The first concerns the software engineering lifecycle, requirements, design, development, testing, and operations, tailored to the challenges of self-adaptive robotics. The second focuses on enabling technologies such as digital twins and AI-driven adaptation, which support runtime monitoring, fault detection, and automated decision-making. We identify open challenges, including verifying adaptive behaviours under uncertainty, balancing trade-offs between adaptability, performance, and safety, and integrating self-adaptation frameworks like MAPE K/MAPLE-K. By consolidating these challenges into a roadmap toward 2030, this work contributes to the foundations of trustworthy and efficient self-adaptive robotic systems capable of meeting the complexities of real-world deployment. |
| title | Software Engineering for Self-Adaptive Robotics: A Research Agenda |
| topic | Software Engineering Robotics |
| url | https://arxiv.org/abs/2505.19629 |