Software Engineering for Self-Adaptive Robotics: A Research Agenda

• URL: http://arxiv.org/abs/2505.19629v2
• Date: Thu, 02 Oct 2025 11:52:44 GMT
• Title: Software Engineering for Self-Adaptive Robotics: A Research Agenda
• Authors: Hassan Sartaj, Shaukat Ali, Ana Cavalcanti, Lukas Esterle, Cláudio Gomes, Peter Gorm Larsen, Anastasios Tefas, Jim Woodcock, Houxiang Zhang,
• Abstract summary: Self-adaptive robots exploit artificial intelligence (AI), machine learning, and model-driven engineering to adapt continuously to changing conditions.<n>This paper presents a research agenda for software engineering in self-adaptive robotics, structured along two dimensions.<n>The first concerns the software engineering lifecycle, requirements, design, development, testing, and operations, tailored to the challenges of self-adaptive robotics.<n>The second focuses on enabling technologies such as digital twins, AI-driven adaptation, and quantum computing, which support runtime monitoring, fault detection, and automated decision-making.
• Score: 12.231810723415789
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, AI-driven adaptation, and quantum computing, 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.

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