ROSA: A Knowledge-based Solution for Robot Self-Adaptation
This work addresses the problem of designing self-adaptive robotic systems for researchers and developers, presenting an incremental improvement through a novel framework.
The paper tackles the challenge of enabling autonomous robots to adapt their task execution and software architecture in diverse environments by introducing ROSA, a knowledge-based framework for robot self-adaptation, which demonstrated advantages in reusability and development effort in an underwater robotics application.
Autonomous robots must operate in diverse environments and handle multiple tasks despite uncertainties. This creates challenges in designing software architectures and task decision-making algorithms, as different contexts may require distinct task logic and architectural configurations. To address this, robotic systems can be designed as self-adaptive systems capable of adapting their task execution and software architecture at runtime based on their context.This paper introduces ROSA, a novel knowledge-based framework for RObot Self-Adaptation, which enables task-and-architecture co-adaptation (TACA) in robotic systems. ROSA achieves this by providing a knowledge model that captures all application-specific knowledge required for adaptation and by reasoning over this knowledge at runtime to determine when and how adaptation should occur. In addition to a conceptual framework, this work provides an open-source ROS 2-based reference implementation of ROSA and evaluates its feasibility and performance in an underwater robotics application. Experimental results highlight ROSA's advantages in reusability and development effort for designing self-adaptive robotic systems.