Who Is Responsible? Self-Adaptation Under Multiple Concurrent Failures With Unknown Faults in Complex Robotic Systems
This paper presents a ROS2-based self-adaptation approach building upon the MAPE-K framework to handle multiple simultaneous failures, cascading failures, and multiple resolving strategies in complex robotic systems. The authors introduce a domain-specific language (DSL) that allows designers to specify failure patterns, assign criticality levels, and enumerate adaptation strategies. Through a live dependency graph and a probabilistic planning algorithm, the approach performs root-cause analysis and strategy ranking to prioritize minimal and effective system adaptations.
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Who issued it
Who it governs
Robotics researchers and software developers seeking to implement autonomous fault-handling and self-adaptation in ROS-based robotic systems.
What the document contains
The document addresses runtime self-adaptation in robotic systems running on ROS2, specifically focusing on the detection and resolution of multiple simultaneous failures, cascading component failures, and unknown fault sources using a MAPE-K loop, domain-specific language, and dependency-based root-cause analysis.
The text does not provide legal frameworks, enforcement mechanisms, or commercial product terms of use.
- Modern robotic systems require self-adaptation mechanisms that can handle multiple concurrent and cascading failures rather than sequential ones.
- A domain-specific language (DSL) allows system designers to specify failure patterns, criticality levels, and alternative recovery strategies.
- Combining a live dependency graph with criticality levels and system impact enables lightweight root-cause analysis and strategy ranking.
- Empirical evaluations on benchmark robotics scenarios show that the approach identifies root causes and favors inexpensive, effective adaptations.
· I. Introduction · II. Related Work · III. Problem · IV. Approach · V. Evaluation · VI. Discussion · VII. Conclusion & Future Work
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
4/5Clear responsibility for AI outcomes
Central to our approach is an adaptation rule set which lets designers specify failure patterns, assign criticality levels, and enumerate multiple plausible adaptation strategies.
Abstract
screening score · 2026-10-23
Fairness
0/5Non-discrimination and equitable treatment
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Human Dignity
0/5Respect for the inherent worth of persons
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Human Oversight
0/5Humans remain in control of consequential decisions
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Privacy
0/5Protection of personal data and private life
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Enforcement
0/5Consequences and compliance mechanisms with real force
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