谁该负责？在复杂ROS系统中多重同时不确定性的自我适应
Who Is Responsible? Self-Adaptation Under Multiple Concurrent Failures With Unknown Faults in Complex Robotic Systems
AI总结：
本文提出了一种基于ROS2的自我适应方法，能够处理复杂系统中多重同时不确定性的根本原因分析和策略选择，通过规则集和依赖图实现轻量级适应，提升适应效率与性能。
摘要：
机器人系统越来越多地在动态、不可预测的环境中运行，其中紧密耦合的传感器和软件模块增加了单个故障在组件之间级联传播的可能性，并允许多个可能的策略来解决基本的不确定性。大多数现有应用于机器人学的自我适应方法都假设预定义的一对一不确定性到适应的映射。我们提出了一种基于ROS2的自我适应方法，建立在MAPE-K反馈循环之上，解决了（1）多个同时存在的不确定性，具有不同的严重性；（2）跨组件的级联不确定性；以及（3）每个检测到的症状的多个可能的解决策略。我们方法的核心是一个适应规则集，使设计人员可以指定不确定性模式，分配严重性级别，并列举多个可能的适应策略。该规则集结合自动提取的实时ROS2依赖图，使轻量级的根本原因分析和策略排名能够优先考虑最小和有效的适应。在水下机器人场景和感知用例的评估中，我们的方法能够识别并发不确定性的根本原因，倾向于低成本的适应，减少不必要的适应，并实现与现有针对顺序不确定性的基线设计相当的性能。代码已公开可用。
英文摘要：
Robotic systems increasingly operate in dynamic, unpredictable environments, where tightly coupled sensors and software modules increase the probability of a single failure cascading across components. Therefore, multiple strategies can be plausible to resolve the underlying fault. Most existing selfadaptive approaches that have been applied to robotics assume predefined one-to-one failure-to-adaptation mappings. We present a ROS2-based self-adaptation approach building upon MAPE-K that addresses (1) multiple simultaneous failures with differing criticality, (2) cascading failures across components, and (3) multiple plausible resolving strategies per detected failure. Central to our approach is an adaptation rule set which lets designers specify failure patterns, assign criticality levels, and enumerate multiple plausible adaptation strategies. This rule set, combined with an automatically extracted live dependency graph, enables lightweight root-cause analysis and strategy ranking to prioritize minimal and effective adaptations. Our approach implements a lightweight self-optimizing component which learns estimated success probabilities of different strategies for each known failure. Experiments on an underwater robot scenario and a perception use case show that our approach can identify root causes among concurrent failures, favors inexpensive adaptations, reduces unnecessary adaptations, and achieves performance comparable to existing baselines designed for sequential failures. The code is publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
