Traditional manual crack inspection methods often face limitations in terms of efficiency, safety, and consistency. To overcome these issues, a new approach based on autonomous robotic systems has gained attention, combining robotics, artificial intelligence, and advanced sensing technologies. However, most existing reviews focus on individual components in isolation and fail to present a complete picture of how these systems work together. This study focuses on robotic crack detection and proposes a structured framework that connects three core modules: the physical platform (robots and sensors), the cognitive core (crack detection algorithms), and autonomous action (navigation and planning). We analyze key technologies, their interactions, and the challenges involved in real-world implementation. The aim is to provide a clear roadmap of current progress and future directions, helping researchers and engineers better understand the field and develop smart, deployable systems for infrastructure crack inspection.

This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI), AI 3.0 (Physical AI), and a speculative AI 4.0 (Conscious AI). Each AI generation is driven by shifting priorities among algorithms, computing power, and data. AI 1.0 accompanied breakthroughs in pattern recognition and information processing, fueling advances in computer vision, natural language processing, and recommendation systems. AI 2.0 is built on these foundations through real-time decision-making in digital environments, leveraging reinforcement learning and adaptive planning for agentic AI applications. AI 3.0 extended intelligence into physical contexts, integrating robotics, autonomous vehicles, and sensor-fused control systems to act in uncertain real-world settings. Building on these developments, the proposed AI 4.0 puts forward the bold vision of self-directed AI capable of setting its own goals, orchestrating complex training regimens, and possibly exhibiting elements of machine consciousness. This paper traces the historical foundations of AI across roughly 70 years, mapping how changes in technological bottlenecks from algorithmic innovation to high-performance computing to specialized data have stimulated each generational leap. It further highlights the ongoing synergies among AI 1.0, 2.0, 3.0, and 4.0, and explores the ethical, regulatory, and philosophical challenges that arise when artificial systems approach (or aspire to) human-like autonomy. Ultimately, understanding these evolutions and their interdependencies is pivotal for guiding future research, crafting responsible governance, and ensuring that AI's transformative potential benefits society.

AI has been successful with decoding human speech, playing games (like chess and Go), self-driving cars, and understanding complex data. AI and machine learning technology is used in applications including: search engines, recommendation systems, virtual assistants, autonomous vehicles, automatic language translation, facial recognition, image labeling, advertising, and driving internet traffic. Domains There are different domains of artificial intelligence. - Pattern recognition like speech, writing, and handwriting - Knowledge engineering like logic programming and inference engines - Expert systems for question answering and Chatbots - Machine learning - Artificial neural networks and deep learning - Computer vision - Robotics - General game playing - Artificial life Types and classes Researchers Kaplan and Haenlein say there are three types of AI system: analytical, human-inspired, and humanized artificial intelligence.[4] - Analytical A.I. has similarities with cognitive intelligence which tries to understand the world and make decisions based on that. - Human-inspired A.I. which tries to be more "human" with cognitive intelligence with emotional intelligence.

This paper presents a biomimetic approach involving cognitive process modeling, for use in intelligent robot decisionmaking. The principle of inner rehearsal, a process believed to occur in human and animal cognition, involves internal rehearsing of actions prior to deciding on and executing an overt action, such as a motor action. The inner-rehearsal algorithmic approach we developed is posed and investigated in the context of a relatively complex cognitive task, an under-rubble search and rescue. The paper presents the approach developed, a synthetic environment which was also developed to enable its studies, and the results to date. The work reported here is part of a Cognitive Robotics effort in which we are currently engaged, focused on exploring techniques inspired by cognitive science and neuroscience insights, towards artificial cognition for robotics and autonomous systems.

applications in artificial intelligence and robotics. Artificial and human cognition have different strengths and weaknesses. For example, artificial cognition excels Cognition encompasses mental processes that deal with knowledge. It includes psychological activities that acquire, store, retrieve, transform, or apply information. Cognitive processes are a fundamental part of mental life, helping individuals understand and interact with the world. Cognitive processes are typically categorized by their function. Perception organizes and interprets sensory inform Cognition encompasses mental processes that deal with knowledge. It includes psychological activities that acquire, store, retrieve, transform, or apply information. Cognitive processes are a fundamental part of mental life, helping individuals understand and interact with the world. Cognitive processes are typically categorized by their function. Perception organizes and interprets sensory information, such as light and sound, to construct a coherent experience of objects and events. Attention prioritizes specific aspects while filtering out irrelevant information. Memory is the ability to retain, store, and retrieve information, including working memory and long-term memory. Thinking encompasses psychological activities in which concepts, ideas, and mental representations are considered and manipulated. It includes reasoning, concept formation, problem solving, and decision-making. Many cognitive activities deal with language, including language acquisition, comprehension, and production. Metacognitive processes deal with information about other mental processes, such as knowing that one can recall a specific memory. Classifications also distinguish between conscious and unconscious processes and between controlled and automatic ones. There are many theories of the nature of cognition. Classical computationalism posits that cognitive processes manipulate symbols according to formal rules, similar to how computers execute algorithms. Connectionism models the mind as a complex network of nodes where information flows as they communicate with each other. Representationalism and anti-representationalism disagree about whether cognitive processes operate on internal representations of the world. Many disciplines explore cognition, including psychology, neuroscience, and cognitive science. They examine different levels of abstraction and employ distinct methods of inquiry. Some scientists study cognitive development, investigating how mental abilities grow from infancy through adulthood. While cognitive research mostly focuses on humans, it also explores how other animals acquire knowledge and how artificial systems can emulate cognitive processes. The study of…

A growing body of research within the field of intelligent robotics argues for a view of intelligence drastically different from classical artificial intelligence and cognitive science. The holistic and embodied ideas expressed by this research promote the view that intelligence is an emergent phenomenon. Similar perspectives, where numerous interactions within the system lead to emergent properties and cognitive abilities beyond that of the individual parts, can be found within many scientific fields. With the goal of understanding how behavior may be represented in robots, the present review

Challenges and Opportunities of Evolutionary Robotics Robotic hardware designs are becoming more complex as the variety and number of on-board sensors increase and as greater computational power is provided in ever-smaller packages on-board robots. These advances in hardware, however, do not automatically translate into better software for controlling complex robots. Evolutionary techniques hold the potential to solve many difficult problems in robotics which defy simple conventional approaches, but present many challenges as well. Numerous disciplines including artificial life, cognitive science and neural networks, rule-based systems, behavior-based control, genetic algorithms and other forms of evolutionary computation have contributed to shaping the current state of evolutionary robotics. This paper provides an overview of developments in the emerging field of evolutionary robotics, and discusses some of the opportunities and challenges which currently face practitioners in the field. Published as: D.A. Sofge, M.A. Potter, M.D. Bugajska, and A.C. Schultz, "Challenges and Opportunities of Evolutionary Robotics." In Proc. 2nd Int'l Conf.

Current AI benchmarks often equate mimicry with genuine intelligence, emphasizing task performance over the underlying cognitive processes that enable human-like understanding. The Machine Perturbational Complexity & Agency Battery (mPCAB) introduces a new, substrate-independent framework that applies neurophysiological methods used initially to assess consciousness in artificial systems. Unlike existing evaluations, it features four key components-perturbational complexity, global workspace assessment, norm internalization, and agency-that link mechanisms with functions. This enables systematic comparisons across digital, neuromorphic, and biological substrates, addressing three research gaps: long-term reasoning with coherent behavior, norm internalization amid distribution shifts, and transformational creativity involving meta-cognitive rule modification. By analyzing theories of consciousness (GNW, IIT, PP, HOT), we identify targets for AI implementation. Our cognitive architecture analysis maps human functions-such as working memory and executive control-to their computational counterparts, providing guiding principles for design. The creativity taxonomy progresses from combinational to transformational, with measurable criteria like changes in conceptual space and the depth of meta-level reasoning. Ethical considerations are integrated into frameworks for monitoring organoid intelligence, reducing bias in creativity, and addressing rights issues. Pilot studies demonstrate mPCAB's feasibility across different substrates and show that its metrics are comparable. This framework moves evaluation away from superficial benchmarks toward mechanism-based assessment, supporting the development of mind-like machines and responsible AI advancements.

Neural Architectures for Robot Intelligence We argue that the direct experimental approaches to elucidate the architecture of higher brains may benefit from insights gained from exploring the possibilities and limits of artificial control architectures for robot systems. We present some of our recent work that has been motivated by that view and that is centered around the study of various aspects of hand actions since these are intimately linked with many higher cognitive abilities. As examples, we report on the development of a modular system for the recognition of continuous hand postures based on neural nets, the use of vision and tactile sensing for guiding prehensile movements of a multifingered hand, and the recognition and use of hand gestures for robot teaching. Regarding the issue of learning, we propose to view real-world learning from the perspective of data mining and to focus more strongly on the imitation of observed actions instead of purely reinforcement-based exploration.

Brain-machine interfaces (BMI) allows individuals to control an external device by controlling their own brain activity, without requiring bodily or muscle movements. Performing voluntary movements is associated with the experience of agency (“sense of agency”) over those movements and their outcomes. When people voluntarily control a BMI, they should likewise experience a sense of agency. However, using a BMI to act presents several differences compared to normal movements. In particular, BMIs lack sensorimotor feedback, afford lower controllability and are associated with increased cognitive

The growing demand for high-fidelity, multi-parameter, distributed sensing in critical domains such as structural health monitoring, oil and gas exploration, and secure perimeter surveillance is pushing traditional optical fiber sensors (OFS) to their performance limits. Although conventional multiplexing techniques such as time-division and wavelength-division multiplexing (TDM, WDM) have been commercially successful, they are rapidly approaching fundamental bottlenecks in sensor density, spatial resolution, and data capacity. This review argues that the synergistic convergence of space-division multiplexing (SDM) and artificial intelligence (AI) represents a paradigm shift, enabling a new generation of intelligent, high-dimensional sensing networks. We comprehensively survey the state of the art in SDM-based OFS, detailing the operating principles and applications of multi-core fibers (MCFs) for ultra-dense sensor arrays and 3D shape sensing, as well as few-mode fibers (FMFs) for mode-division multiplexing and enhanced multi-parameter discrimination. However, the unprecedented spatial parallelism provided by SDM introduces significant challenges, including inter-channel crosstalk, complex signal demultiplexing, and massive data volumes. This paper systematically explores how AI, particularly machine learning (ML) and deep learning (DL), is being leveraged not merely as a tool but as an indispensable core technology to mitigate these impairments. We critically analyze AI's role in digital crosstalk suppression, intelligent mode demultiplexing, signal denoising, and solving complex inverse problems for parameter estimation. Furthermore, we highlight how this AI-SDM synergy enables capabilities beyond the reach of either technology alone, such as super-resolution sensing and predictive analytics. The discussion is extended to include the critical supporting pillars of this ecosystem, such as advanced interrogation techniques and the associated data management challenges. Finally, we provide a forward-looking perspective on the trajectory of the field, outlining a path toward cognitive sensing networks that are self-calibrating, adaptive, and capable of autonomous decision-making. This review is intended to serve as a foundational reference for researchers and engineers at the intersection of photonics and intelligent systems, illuminating the pathway toward tomorrow's intelligent sensing infrastructure.