robotics

This study addresses the persistent gap between theoretical control performance and its practical realization in real-world robotic systems, often caused by inadequate discretization, insufficient real-time guarantees, and weak error handling in control software. For the first time from a software engineering perspective, the authors systematically analyze 184 open-source robotic controllers through code review, empirical analysis, and test evaluation, uncovering common deficiencies in application scenarios, implementation details, and verification practices. The findings reveal that most implementations fail to properly account for critical system constraints, and their testing strategies inadequately validate the theoretical assurances they claim. This work highlights a significant disconnect between implementation quality and theoretical promises, offering concrete directions and practical guidelines for developing reliable, verifiable robotic control software.

This study addresses the limitations of current physical human–robot interaction safety standards, such as ISO/TS 15066, which rely on simplified assumptions without a systematic analysis of their theoretical foundations, underlying premises, or impact on system performance. By modeling safety constraints, conducting energy-based safety analyses, and performing numerical simulations, this work uncovers the implicit assumptions embedded in widely used safety criteria and elucidates their practical consequences, highlighting the central role of energy in safety assessment. The research quantifies the performance degradation induced by oversimplified design choices and introduces tunable parameters to optimize safety-critical control strategies. Furthermore, it offers novel insights into energy-driven safety methodologies, significantly advancing the balance between safety and performance in human–robot collaborative systems.

Small- and medium-sized enterprises (SMEs) and research institutions face significant challenges in deploying robotic systems due to limited robotics expertise, heavy reliance on external system integrators, complex hardware interfaces, and poor cross-platform reusability. Method: This paper proposes a general-purpose robot software framework based on object-oriented design. Its core innovation is a unified abstraction model—“robot skill”—achieved through layered abstraction and modular encapsulation, which decouples hardware-specific implementations and standardizes control logic across heterogeneous platforms (e.g., Yaskawa Motoman GP4). Implemented in Python, the framework enables rapid integration into smart manufacturing systems. Contribution/Results: Experimental evaluation demonstrates that the framework substantially reduces interface complexity, shortens prototyping and deployment cycles, decreases dependency on external integrators, and improves both development efficiency and skill reusability across robotic platforms.

Traditional robot design and control are typically decoupled, leading to morphologies poorly aligned with task requirements. This paper proposes a simulation-driven co-optimization framework for morphology and control, breaking the conventional “design-then-control” paradigm to enable task-oriented, end-to-end joint search. Our method employs gradient-free optimization to simultaneously evolve structural parameters and controller policies within a URDF-based multi-task reinforcement learning simulation environment. Key contributions include: (1) demonstrating that controller retraining significantly improves performance, yielding an average gain of 37%; and (2) revealing an inverse correlation between morphological complexity and controller training budget—providing theoretical justification for structural simplification under resource constraints. We validate the framework across four public simulation benchmarks, showing that co-optimization consistently yields more compact, robust, and task-adapted robot morphologies compared to sequential approaches.

This study addresses the significant challenges in testing robotic software, which arise from its tight coupling with hardware, high environmental uncertainty, and strong autonomy—factors that make it considerably more difficult to test than traditional software and hinder comprehensive coverage of potential failure scenarios. Through a systematic literature mapping of 247 relevant studies, this work constructs the first conceptual framework that maps robotic software testing onto established general software testing theory. It systematically categorizes, compares, and relates existing approaches, offering a structured overview of the current research landscape, core challenges, and representative practices. By identifying key open problems, the study provides a theoretical foundation and strategic guidance for advancing robotic software testing, thereby fostering interdisciplinary integration between software engineering and robotics and supporting the development of reliable, verifiable autonomous systems.

This work addresses the reliability challenges inherent in deploying autonomous mobile robots from simulation to real-world environments by proposing and implementing an end-to-end development and validation framework. Building upon an existing mechatronic platform, the system integrates onboard sensing and computing units to achieve self-localization and autonomous navigation. The complete architecture is first developed and rigorously validated in a high-fidelity simulation environment before being seamlessly transferred to a physical robot. Experimental results demonstrate that the real-world system successfully replicates the performance observed in simulation, thereby confirming the effectiveness and robustness of the proposed approach. These findings substantiate the use of high-fidelity simulation as a trustworthy foundation for robotic development, significantly enhancing deployment efficiency and system credibility.

This work addresses the multidimensional safety challenges—spanning action, decision-making, and human-centered considerations—that foundation model–driven robots encounter in open, dynamic, and long-tailed real-world environments, which existing approaches struggle to handle effectively. The paper presents the first systematic three-dimensional framework for robotic safety and introduces a modular safety guardrail architecture comprising monitoring and intervention layers. This architecture enables cross-layer coordination through mechanisms such as representation alignment and conservatism allocation, thereby delivering full-stack, scalable, and composable safety guarantees. Crucially, the design supports dynamic adaptation to evolving tasks and environments, offering a flexible and efficient safety deployment paradigm for physical AI systems.

Autonomous agents operating without continuous human oversight are prone to safety violations and behavioral instability. This work proposes a discrete-time control system that, for the first time, integrates a five-tier execution mechanism—comprising Gobs, Gsug, Gplan, Gexec, and Gint—with the SMART governance lifecycle to decouple action control from autonomous governance while providing formal safety guarantees. The framework incorporates utility-gated scheduling, event-triggered fallback, consensus gating, and collective Lyapunov analysis to ensure runtime safety in both single- and multi-agent cyber-physical systems. Experimental validation on a UR5 triple-arm robotic platform demonstrates a 99.6% anomaly detection rate (versus 2.1% for the baseline), a 3.5× reduction in detection latency, and the generation of verifiable safety certificates over the physical workspace.

This study addresses the limitations of traditional computer science curricula, which often lack sufficient hands-on experience to cultivate the integrated hardware-software and engineering competencies required for tackling industrial robotics challenges. To bridge this gap, the authors propose a hybrid pedagogical approach that combines agile development, project-based learning (PBL), and interactive instruction, centered on the Robot Operating System (ROS). Students engage in a semester-long development of a robotic software ecosystem grounded in real-world scenarios—such as automated disassembly of building blocks—to foster deep, applied understanding. Implemented successfully in a digital technologies program, this model significantly enhanced students’ comprehensive engineering practice, collaborative teamwork, and ability to apply theoretical knowledge, offering a replicable teaching paradigm for robotics education.