Software Engineering for Self-Adaptive Robotics: A Research Agenda

2025-05-26

AI Summary

Ensuring concurrent reliability, safety, and performance in adaptive robotic systems operating under dynamic and uncertain environments remains a fundamental challenge in software engineering. Method: This work proposes the first end-to-end adaptive robotic software engineering framework, integrating digital twin, model-driven engineering (MDE), and AI-enabled adaptation mechanisms. At its core lies a tightly coupled MAPE-K (Monitor-Analyze-Plan-Execute-Knowledge) control loop, enabling seamless integration of requirements engineering, design modeling, co-simulation, and runtime verification. Key technical advances address behavioral verification under uncertainty, multi-objective trade-offs among adaptability, safety, and performance, and deep architectural integration of the MAPE-K paradigm. Contribution/Results: The study delivers a structured research roadmap and establishes a systematic theoretical foundation and methodological toolkit for developing trustworthy, robust, and high-performance adaptive robotic systems—advancing the state of the art in robotics software engineering.

Technology Category

• Intelligent Robots: Embodied AI
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Self-adaptive robotic systems are designed to operate autonomously in dynamic and uncertain environments, requiring robust mechanisms to monitor, analyse, and adapt their behaviour in real-time. Unlike traditional robotic software, which follows predefined logic, self-adaptive robots leverage artificial intelligence, machine learning, and model-driven engineering to continuously adjust to changing operational conditions while ensuring reliability, safety, and performance. This paper presents a research agenda for software engineering in self-adaptive robotics, addressing critical challenges across two key dimensions: (1) the development phase, including requirements engineering, software design, co-simulation, and testing methodologies tailored to adaptive robotic systems, and (2) key enabling technologies, such as digital twins, model-driven engineering, and AI-driven adaptation, which facilitate runtime monitoring, fault detection, and automated decision-making. We discuss open research challenges, including verifying adaptive behaviours under uncertainty, balancing trade-offs between adaptability, performance, and safety, and integrating self-adaptation frameworks like MAPE-K. By providing a structured roadmap, this work aims to advance the software engineering foundations for self-adaptive robotic systems, ensuring they remain trustworthy, efficient, and capable of handling real-world complexities.

Problem

• Developing software engineering methods for self-adaptive robots in dynamic environments
• Addressing challenges in requirements, design, and testing for adaptive robotic systems
• Integrating enabling technologies like digital twins and AI for runtime adaptation

Innovation

• AI and ML for real-time adaptation
• Model-driven engineering for dynamic environments
• Digital twins for runtime monitoring