为自适应机器人设计可信软件体系，解决动态环境下的安全与性能难题。
Software Engineering for Self-Adaptive Robotics: A Research Agenda
- 构建面向自适应机器人的全生命周期软件工程框架
- 融合数字孪生与AI实现运行时监控与自动决策
- 适合研究机器人可靠性、AI系统集成的开发者与学者
自适应机器人在动态不确定环境中自主运行，需具备实时监控与持续适应能力。与传统预设逻辑的机器人软件不同，这类系统依赖人工智能、机器学习和模型驱动工程，在变化中不断调整行为，以保障可靠性、安全性和性能最优。本文提出自适应机器人软件工程的研究路线图，涵盖两个维度：一是针对需求、设计、开发、测试与运维等全生命周期的挑战；二是支撑技术如数字孪生与AI驱动自适应，支持运行时监控、故障检测与自动化决策。文中识别了若干开放性问题，包括在不确定性下验证自适应行为、权衡适应性、性能与安全性之间的矛盾，以及集成如MAPE-K/MAPLE-K等自适应框架。通过将这些挑战整合为2030年发展路线图，本工作为构建可信赖且高效的自适应机器人系统奠定基础，以应对真实世界部署的复杂性。
原文摘要 · Abstract (English)
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.
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