Enhancing Resilience in Robotic Systems Through Self-Awareness and Adaptive Recovery

Robotic systems are becoming increasingly complex in both structure and behavior, integrating multiple components to provide advanced services.As this complexity grows, so does the likelihood of faults, making resilience essential for maintaining dependable functionality.This paper proposes a novel framework to enhance the resilience of robotic systems by enabling self-awareness and self-recovery in response to errors.To manage this complexity, we introduce the concept of a "Skill Chain, a set of components that collaboratively deliver specific services required for the system to transition through its states and achieve its mission goals.By continuously monitoring its internal state, the system detects errors before they propagate beyond the skill chain level.Upon detection, the system evaluates its current state and available resources.If spare components or other fault tolerance mechanisms exist, it reconfigures itself by forming a new skill chain capable of maintaining service delivery.Recovery strategies, such as backward recovery to return to a previously successful state or forward recovery to adapt service delivery, are dynamically applied to ensure minimal disruption.The paper introduces the approach concept and demonstrates its applicability on exemplar robotic systems.