Robotic systems increasingly operate in dynamic, unpredictable environments, where tightly coupled sensors and software modules increase the probability of a single fault cascading across components and admitting multiple plausible strategies to resolve the underlying uncertainty. Most existing self-adaptive approaches that have been applied to robotics assume predefined one-to-one uncertainty-to-adaptation mappings. We present a ROS2-based self-adaptive approach building upon the MAPE-K feedback loop that addresses (1) multiple simultaneous uncertainties with differing criticality, (2) cascading uncertainties across components, and (3) multiple plausible resolving strategies per detected symptom. Central to our approach is an adaptation rule set which lets designers specify uncertainty patterns, assign criticality levels, and enumerate multiple plausible adaptation strategies. This rule set, combined with an automatically extracted live ROS2 dependency graph, enables lightweight root-cause analysis and strategy ranking to prioritize minimal and effective adaptations. Evaluations on an underwater robot scenario and a perception use case show that our approach can identify root causes among concurrent uncertainties, favours inexpensive adaptations, reduces unnecessary adaptations, and achieves performance comparable to existing baselines designed for sequential uncertainties. The code is publicly available.

翻译：机器人系统日益在动态、不可预测的环境中运行，其中紧密耦合的传感器与软件模块增加了单一故障在组件间级联传播的可能性，并允许存在多种解决底层不确定性的可行策略。大多数应用于机器人领域的现有自适应方法均预设了一对一的不确定性到适应行为的映射关系。本文提出一种基于ROS2的自适应方法，该方法建立在MAPE-K反馈循环之上，旨在解决：(1) 具有不同关键性的多重并发不确定性；(2) 跨组件的级联不确定性；(3) 每个检测到的症状对应多种可行的解决策略。我们方法的核心是一个适应规则集，允许设计者指定不确定性模式、分配关键性等级并枚举多种可行的适应策略。该规则集与自动提取的实时ROS2依赖图相结合，能够实现轻量级的根因分析和策略排序，从而优先选择最小化且有效的适应行为。在水下机器人场景和感知用例上的评估表明，我们的方法能够在并发不确定性中识别根本原因，倾向于低成本的适应，减少不必要的适应，并达到与为顺序不确定性设计的现有基线方法相当的性能。代码已公开。