Jul 2026· International Conference on Artificial Intelligence Testing· pp. 211-221· 0 citations· 70 references
Abstract
Robots are being deployed for an increasingly diverse set of purposes, from industrial manufacturing to delivery, inspection, and surgical assistance, and the systems entering these roles are markedly more capable than earlier generations, utilizing learned perception, language-based planning, and multi-sensor input. This increase in the number of deployments is reflected in industry forecasts that report rapid, sustained growth in industrial robot installations and in the worldwide operational stock [18]. As robots take on broader and more complex missions in human-centered environments, their quality assurance and physical safety become increasingly relevant. However, despite this growth and advancements, few existing works offer a comprehensive review of robotic test automation with its conventional, AI-powered, and agentic landscape and overall trends. This paper addresses this gap by summarizing, classifying, and visualizing conventional and AI-based robot testing. Extending this, a reading of the commercial landscape suggests that conventional and AI methods act as complements rather than substitutes. Lastly, this work portrays many problems, challenges, and needs to aid in future research.
Flexible manufacturing, characterized by high-mix, low-volume, and highly variable production, demands robotic systems with strong adaptability, dexterity, and intelligence that conventional offline-programmed industrial robots cannot provide. This paper presents a systematic review of key technologies for robot embodied intelligence oriented toward flexible manufacturing, organized around the closed loop of perception, decision-making, and execution. The purpose is to clarify the current research landscape, identify core technical bottlenecks, and outline future directions for embodied-intelligent manufacturing. Adopting a literature-analysis and comparative-review method, the study examines representative advances at three levels: multimodal environmental perception and real-time modeling, flexible adaptive precision manipulation, and intelligent decision-making for process planning and scheduling. The review finds that multimodal fusion and semantic SLAM are overcoming perception bottlenecks, that deep learning and force/position hybrid control are balancing flexible adaptability with high-precision operation, and that deep reinforcement learning and large models are advancing intelligent process planning. It concludes that data scarcity, model reliability, software-hardware integration, and ethical-legal standards remain the principal challenges to large-scale industrial deployment.
Zheng-Yang Chen· Advances in Engineering Inno...· 0 citations
Robotic systems increasingly operate in dynamic, uncertain, and open-ended environments, where design-time assumptions may no longer hold, and adaptation becomes necessary to maintain effective and safe operation. Behavior Trees (BTs) are widely used in robotic control architectures due to their modularity, readability, and reactivity. This raises a central question: are BTs sufficient to meet the adaptation needs of modern robotic systems? This paper investigates this question through a literature-driven study complemented by empirical validation. First, we derive a classification of robotic adaptation needs from the literature, organizing them into six categories: Knowledge, Perception, Actuation, System, Mission, and Environment. Then, we analyze the capabilities and limitations of classical BTs with respect to these needs. Then, we characterize BT-based approaches for adaptation from the existing literature and organize them into four primary families, i.e., generation, extension, evolution, and refinement, including approaches that combine multiple families. Our analysis shows that the modularity, flexibility, and reactivity of classical BTs are insufficient for adaptation needs involving runtime restructuring, reasoning under uncertainty, mission reinterpretation, learning, or integration with external knowledge and planning mechanisms. Enhanced BT approaches address several of these limitations, but to different extents and often with limitations of their own. Our findings relate adaptation needs to both the capabilities and limitations of classical and enhanced BTs, providing guidance on when classical BTs are sufficient, when enhanced mechanisms are needed, and which challenges remain or emerge for adaptive robotic control architectures.
Currently, most commercial robots rely on fixed programs to perform repetitive tasks. The visual modules equipped on these devices have limited anti-interference capabilities, making it difficult to adapt to complex structures and variable environments, which in turn limits the practical effectiveness of robotic intelligence deployment. Drawing from hands-on experience in robot debugging and project implementation, this paper explores the integration of computer vision and embodied intelligence. Based on common application scenarios such as industrial sorting, power inspection, and intelligent services, it analyzes specific methods by which visual technologies assist robots in environmental perception, intelligent decision-making, motion adjustment, and model updating. The study identifies several common challenges encountered during industry deployment, including insufficient stability in recognizing complex scenes, difficulty balancing computational power with recognition accuracy, significant gaps between simulation training and real-world conditions, and a lack of unified application standards across industries. Addressing these practical issues, the paper proposes feasible improvement strategies from four perspectives: algorithm enhancement, computational power upgrade, simulation optimization, and the establishment of industry standards. The conclusions drawn from this research offer practical insights for advancing robot vision technology, expanding application scenarios, and promoting standardized development within the industry.
Zi-Chen Wang· Journal of Computer Science...· 0 citations
The quantified requirements of industrial robots enabled by EAI4I are analyzed and recent research progress is reviewed, covering core technologies for single- and multi-robot systems, dedicated hardware platforms, high-fidelity simulators, task-specific datasets, representative industrial application scenarios, and critical deployment challenges.
Hai-Bin Yu, Chunhe Song, Yinlong Zhang et al.· National Science Review· 0 citations
Research and current innovations required more open-source tools to enhance the multidisciplinary kind of research growth. Thus, open-source tools are very fruitful specially in case of robotics and automation systems. Because of this any user can design and test virtually before wasting the time and money to implement a physical model. In this research paper major focus is to develop a prototype model using open-source resources. It’s required since robotics and automation have significantly transformed human society, beginning with the industrial revolution when machinery revolutionized labor set the foundation for modern industry. The primary reason of the fast growth of automation is evolving rapidly through advancements in artificial intelligence (AI), which allows machines to do complex tasks. In the coming years, this new wave of automation is expected to drive substantial change, improving productivity and accuracy across industry. So, through this research work an attempt has been tried to explores the role of robotics and automation in both historical and modern context, examining their potential to shape a more efficient future while considering the societal impact using different open-source tools and latest technologies. It also addresses future challenges, including the need for ethical framework, cybersecurity safeguard, and workforce reskilling initiatives. The major tools are used here is Arduino IDE, Tinker cad, coppelia-Sim, Circuit verse etc. Addressing these issues proactively will be crucial to ensuring that robotic and automation continue to benefit society equitably.
Priyanka Mishra, Upkar Singh Kandhari, Anchana B S et al.· 2026 4th International Confe...· 0 citations
The emergence of autonomous laboratories is accelerating discovery in chemistry, drug discovery, materials science, and related fields by enabling high-throughput, data-driven experimentation. However, the integration of heterogeneous robotic systems, ranging from fixed manipulators to mobile platforms, introduces safety challenges that are not systematically addressed in newly established laboratories. In this context, this work aims to raise awareness of robotic safety among chemists and biologists leading laboratory automation projects who may have limited access to industrial robotics expertise. To support a preliminary evaluation of existing or newly developed automated laboratory systems, we explain and demonstrate the use of a simple, structured safety assessment methodology based on ISO standards and tailored to laboratory environments. The framework combines established robotics safety standards with laboratory-specific considerations, including chemical hazards, human-robot interaction, and dynamic workflows. To facilitate its adoption by scientists, the methodology is illustrated through a case study conducted at the Swiss CAT+ West Hub autonomous laboratory, focusing on a multi-instrument analytical platform integrating collaborative robotic arms and mobile robotic systems. The proposed framework follows a six-step iterative process encompassing system definition, hazard identification, risk estimation, risk reduction, and validation. Its applicability was evaluated through the case study, in which sixteen hazards were identified, with robot-human collisions and chemical exposure representing the most critical risks. Experimental force and pressure measurements further demonstrated that widely used collaborative robots may exceed accepted safety thresholds under realistic operating conditions, particularly as a consequence of end-effector design and task-dependent motion characteristics. Risk mitigation strategies based on dynamic safety zoning, sensor-based human detection, and operational mode control were implemented to ensure compliance with safety requirements. The results highlight the need for systematic, context-specific safety assessments in autonomous laboratories and demonstrate that collaborative robots are not inherently safe without rigorous validation. This work provides a practical framework for the safe deployment of robotic systems in autonomous and digital laboratory environments.
