🤖 AI Summary
A significant gap exists between academic research and industrial practice in robot runtime software reconfiguration: while the literature predominantly focuses on structural reconfiguration (e.g., component loading/unloading), industry widely adopts lightweight, low-intrusion parameter-based reconfiguration.
Method: We conduct a mixed-methods study comprising a systematic literature review of 78 papers, source-code and documentation analysis of four mainstream robotic frameworks, and empirical evaluation across 48 subsystems plus real-world case studies.
Contribution/Results: This work is the first to systematically expose this practice-research divide and establishes the first comprehensive design space for robot reconfiguration. It confirms parameter-level reconfiguration as the sole widely adopted paradigm in practice; identifies critical research gaps and emerging technical trends; and proposes actionable, engineering-oriented improvement pathways. Our findings provide empirically grounded guidance for practitioners in tool selection, framework design, and industry–academia collaboration.
📝 Abstract
Robots often need to be reconfigurable—to customize, calibrate, or optimize robots operating in varying environments with different hardware. A particular challenge in robotics is the automated and dynamic reconfiguration to load and unload software components, as well as parameterizing them. Over the last decades, a large variety of software reconfiguration techniques has been presented in the literature, many specifically for robotics systems. Also many robotics frameworks support reconfiguration. Unfortunately, there is a lack of empirical data on the actual use of reconfiguration techniques in real robotics projects and on their realization in robotics frameworks. To advance reconfiguration techniques and support their adoption, we need to improve our empirical understanding of them in practice. We present a study of automated reconfiguration at runtime in the robotics domain. We determine the state-of-the art by reviewing 78 relevant publications on reconfiguration. We determine the state-of-practice by analyzing how four major robotics frameworks support reconfiguration, and how reconfiguration is realized in 48 robotics (sub-)systems. We contribute a detailed analysis of the design space of reconfiguration techniques. We identify trends and research gaps. Our results show a significant discrepancy between the state-of-the-art and the state-of-practice. While the scientific community focuses on complex structural reconfiguration, only parameter reconfiguration is widely used in practice. Our results support practitioners to realize reconfiguration in robotics systems, as well as they support researchers and tool builders to create more effective reconfiguration techniques that are adopted in practice.
