Software Engineering for Self-Adaptive Robotics: A Research Agenda

TL;DR

This paper articulates a research agenda for software engineering in self-adaptive robotics (SAR), framing the challenge as a two-dimensional problem: the software engineering lifecycle for SAR and the enabling technologies that support runtime adaptation, such as digital twins, AI, and quantum computing. It leverages the MAPLE-K loop (an extension of MAPE-K with legitimacy checks) to structure adaptation across requirements, design, development, testing, and operations, while exploring the integration of model-driven engineering, simulation, and verification to manage uncertainty. The authors identify open challenges—ranging from dynamic requirements and safe runtime evolution to AI trustworthiness and secure, scalable simulations—and map them to a 2030 roadmap with concrete opportunities in requirements engineering, design, testing, and deployments. The work aims to establish foundations for trustworthy, efficient SAR capable of operating safely in real-world, heterogeneous environments, informing both researchers and practitioners about where to focus efforts for future SAR systems.

Abstract

Self-adaptive robotic systems operate autonomously in dynamic and uncertain environments, requiring robust real-time monitoring and adaptive behaviour. Unlike traditional robotic software with predefined logic, self-adaptive robots exploit artificial intelligence (AI), machine learning, and model-driven engineering to adapt continuously to changing conditions, thereby ensuring reliability, safety, and optimal performance. This paper presents a research agenda for software engineering in self-adaptive robotics, structured along two dimensions. The first concerns the software engineering lifecycle, requirements, design, development, testing, and operations, tailored to the challenges of self-adaptive robotics. The second focuses on enabling technologies such as digital twins, AI-driven adaptation, and quantum computing, which support runtime monitoring, fault detection, and automated decision-making. We identify open challenges, including verifying adaptive behaviours under uncertainty, balancing trade-offs between adaptability, performance, and safety, and integrating self-adaptation frameworks like MAPE-K/MAPLE-K. By consolidating these challenges into a roadmap toward 2030, this work contributes to the foundations of trustworthy and efficient self-adaptive robotic systems capable of meeting the complexities of real-world deployment.

Figures (2)

• Figure 1: A conceptual view of the MAPLE-K loop for SAR and the phases of the software engineering lifecycle.
• Figure 2: An overview of the roadmap, illustrating its structure and the aspects covered in the software engineering lifecycle and enabling technologies.