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Summary

Next-Gen Robotics: An In-Depth Analysis of Current AI Trends and Future Prospects explores the dynamic interplay between robotics and artificial intelligence (AI), highlighting the historical evolution, current trends, and future implications of these intertwined technologies. The history of robotics spans from the early days of industrial automation to contemporary advancements, marked by milestones such as the introduction of the first industrial robot in 1956 and the pivotal role robotics plays in modern manufacturing and Industry 4.0 initiatives[1]. These advancements underscore the critical role robots have played in enhancing productivity, safety, and efficiency across various industries. In the current landscape, AI-powered robots have emerged as key players in advancing technological frontiers. Robotics serves as a platform for refining machine learning algorithms, leading to innovations in sectors ranging from healthcare to space exploration[2]. Examples include AI-enhanced quality control in automotive manufacturing and the deployment of collaborative robots (cobots) designed to work alongside human workers safely and efficiently[1][2]. These developments have not only optimized industrial processes but have also introduced AI-driven decision-making capabilities that extend the potential of robotic applications. The integration of AI in robotics has paved the way for groundbreaking innovations, such as deep reinforcement learning algorithms that enhance robotic dexterity and autonomous mobile robots (AMRs) equipped with advanced sensors for real-time data processing[3]. This symbiotic relationship between AI and robotics has catalyzed significant advancements in public safety, agriculture, and personalized healthcare, illustrating the broad impact of these technologies on everyday life[4][3]. However, these advancements are not without challenges, including ethical considerations related to data privacy, job displacement, and the need for robust legal frameworks to ensure responsible AI development[5]. Looking ahead, the future of robotics is characterized by the continuous convergence of AI, advanced materials, and manufacturing techniques. By 2030, it is anticipated that robots will become ubiquitous in daily life, fundamentally altering social, economic, and cultural landscapes[4]. This integration will necessitate global collaboration to address ethical dilemmas and harness the full potential of these technologies for societal benefit. As AI continues to enhance the capabilities of robotics, the focus will remain on developing sustainable, resilient, and ethically aligned systems that support human agency and contribute positively to society and the environment[4][5].

Historical Background

The history of robotics dates back to the early days of industrialization when simple automation machines were developed to perform routine and repetitive tasks[1]. These early robotic inventions were primarily conceptual and rudimentary in function, laying the foundational principles of automation and mechanization[4]. In the following decades, advancements in control engineering and computing enabled the development of more sophisticated robots capable of performing a wider range of tasks[1]. A significant milestone in the field of robotics was achieved in 1956 with the introduction of the first industrial robot by George Devol and Joseph Engelberger[1]. By the 1970s, robots had become a standard tool in the manufacturing industry, particularly in automotive production[1]. Over time, further advances in electronic and software engineering, as well as miniaturization, enabled the development of more compact and versatile robots that could be used in various industries[1]. Each phase of this evolution was characterized by an increasing sophistication in design, functionality, and application, reflecting a growing understanding of the potential roles robots could play in various aspects of human life[4]. By automating tasks that used to be done by humans and minimizing errors from human input, robots began proving vital in improving precision and speed within many manufacturing settings[2]. In recent years, robotics has emerged as a crucial technology in implementing Industry 4.0 objectives, allowing for the automation of even complex and repetitive tasks, thereby increasing productivity and efficiency in manufacturing processes[6]. The integration of industrial, collaborative, and mobile robots into manufacturing processes has become pivotal, enhancing safety, reducing costs, and improving product quality to meet customer demands more efficiently[6].

Current AI Trends in Robotics

Robotics is a major factor in advancing AI technologies, serving as an environment for trialing and refining machine learning algorithms, and allowing access to real-world scenarios for this purpose[2]. Through collaboration with Intel and Nebbiolo Technologies, Audi has created quality control processes involving robotic arms equipped with Intel’s technology alongside predictive analytics and machine learning methods, assisting the inspection of welds[2]. AI-powered robots leverage data collection abilities to conduct tasks otherwise hazardous to humans, making safety-focused operations within industries more efficient and preserving human lives[2].

Types of AI-Powered Robots

Robotics, as demonstrated by the World Economic Forum, is being used to handle complicated tasks that cannot be efficiently managed with programmed automation[2]. AI is employed in turnkey robotics systems to enable robots to understand their environment and make decisions like humans. For instance, machine learning algorithms aid production output while simultaneously reducing errors on the assembly line for more effective manufacturing processes[2]. However, some robotic functions involving repetitive movements don’t require complex AI systems; they may use pre-programmed instructions without human intelligence models or other artificial components[2]. To achieve higher levels of capability, AI must still play an essential role, enabling machines to reach their full potential in communicating with our world through autonomous actions and reactions[2].

The Role of Robotics in AI

AI and robotics are two integral disciplines working hand in hand to create advanced robotic systems. Robotics involves machines designed and programmed to complete tasks autonomously without human intervention, while AI seeks to imitate the actions of the human brain when solving complex challenges[2]. The combination of these technologies allows robots not only to execute repetitive assignments quicker than humans but also to handle complexities previously beyond reach[2]. Applications range from healthcare to space exploration, demonstrating the substantial impact of AI-augmented robotics on numerous industries globally[2].

The Role of AI in Robotics

Specifically in robotics, AI is optimizing robots’ autonomy in planning tasks and interacting with the world. AI robots offer a greater advantage over conventional robots that can only apply pre-defined reflex actions[7]. These AI-enabled robots can learn from experience, adapt to environments, and make reasonable decisions based on their sensing capabilities[7]. For example, AI learning algorithms are leveraged to make robots learn the best path to take in different scenarios, utilize natural language processing (NLP) for intuitive human-robot interaction, and employ deep neural networks to understand emotional intents in human-robot interactions (HRI)[7]. Computer vision has also significantly enhanced the perception and awareness of robots[7].

AI in Public Safety Robotics

In urban public safety, robotics offers innovative solutions for surveillance and crowd management[4]. Drones, for instance, are extensively used for aerial surveillance, providing a comprehensive view of large public spaces to monitor and manage safety effectively[4]. These aerial robots, equipped with cameras and sensors, can identify potential security threats or emergencies, enabling swift response from authorities[4]. Ground robots assist in traffic control, incident reporting, and interacting with the public to provide information or assistance[4]. The use of robotics in public safety not only enhances security but also helps allocate human resources more effectively, ensuring that personnel are available for tasks requiring human judgment and interaction[4].

Advanced Learning Algorithms in Robotics

The combination of deep learning and reinforcement learning, known as deep reinforcement learning (DRL), addresses more complicated problems involving perception and decision-making[3].

Key Technologies and Innovations

Collaborative Robots (Cobots)

Collaborative robots, commonly known as cobots, represent a paradigm shift in the field of robotics, emphasizing interaction and cooperation with human workers. Cobots are designed to work alongside humans in a shared workspace, differing significantly from traditional industrial robots in their operational approach and design philosophy[1]. Cobots are characterized by their safety features, user-friendliness, and flexibility. Unlike traditional robots that require safety barriers, cobots are equipped with sensors and safety mechanisms that allow them to detect and respond to the presence of humans, reducing the risk of injury[1]. For example, in automotive assembly lines, cobots can assist workers by handling and positioning heavy parts, thereby reducing physical strain on human workers.

Edge Computing and Artificial Intelligence (AI)

The fusion of edge computing and AI involves processing AI algorithms on users’ devices, offering benefits like reduced latency, energy efficiency, and real-time applications[8]. This integration allows for real-time data processing and decision-making at the source, significantly reducing latency and bandwidth use[8]. By leveraging edge AI, organizations can achieve greater efficiency, enhanced privacy, and faster insights, driving innovation across various sectors[8]. Edge AI refers to integrating AI capabilities at the network edge, enabling distributed intelligence with edge devices. It aims to improve network connectivity, enable the deployment of AI pipelines with defined quality targets, and allow adaptation for data-driven applications[8]. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge computing have unlocked the enormous scope of Edge AI[8]. The goal of Edge AI is to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity[8]. Despite being a relatively new field of research, spanning from 2014 to the present, it has shown significant and rapid development over the last five years[8].

Cloud, Fog, and Edge Computing

AI models and applications can be deployed in different infrastructures based on their target scenarios. The three most common infrastructures widely used are Cloud, Fog, and Edge[8]. Cloud computing provides powerful computing and storage resources, fog computing emphasizes real-time and low latency at the edge of the network, and edge computing enables real-time data processing and decision-making closest to the end device[8]. These three together build a complete edge intelligence ecosystem, providing comprehensive support for the development of AI at the edge[8].

Industry 4.0 and Digital Transformation

Two critical developments have raised the bar for manufacturers as they pursue a factory of the future strategy. Game-changing digital and Industry 4.0 technologies have redefined what’s possible for next-generation production systems[9]. When properly deployed, these new capabilities can raise productivity by more than 10% and create a host of additional advantages regarding flexibility, quality, consumption of natural resources, capex allocation, labor savings, and more[9]. The rapid convergence of exponential technologies, such as the Internet of Things, Artificial Intelligence, Blockchain, Robotics, and Augmented Reality, is forcing many companies to rethink their business models[10]. These technology shifts and their convergence are shaping the next industrial revolution[10]. In the automotive industry, for example, technologies like autonomous driving are turning from a moonshot a few years back to a reality that will soon hit our streets in most cities[10].

Power Sources for Next-Gen Robotics

The development of energy innovations is crucial for powering the next generation of robots. As robots become more advanced and autonomous, the demand for efficient and sustainable energy sources will increase. The creation of high-capacity, fast-charging batteries represents a significant milestone in this area. These batteries will enable robots to operate for extended periods without the need for frequent recharging, enhancing their efficiency and usability in various applications[4]. Additionally, the advancement of alternative power sources, such as solar power or wireless charging technologies, is pivotal. Solar-powered robots could operate independently in outdoor environments, harnessing the sun’s energy for continuous operation. Wireless charging, conversely, would allow for more seamless and efficient energy replenishment, particularly for robots that need to be constantly on the move, such as those used in logistics or surveillance[4]. In the realm of public services, the future of robotics by 2030 is poised to transform safety and emergency response strategies dramatically. Robotics will play a pivotal role in augmenting the capabilities of first responders, and enhancing the efficiency and effectiveness of emergency operations. Robots, with their advanced sensors, AI, and unmanned capabilities, can access hazardous or inaccessible areas, providing real-time data and assistance, thereby reducing risks to human responders[4]. Today’s robots are designed to consume less energy, leading to lower operating costs. To meet sustainability targets for their production, companies utilize industrial robots equipped with energy-saving technology. For instance, robot controls can convert kinetic energy into electricity and feed it back into the power grid, significantly reducing the energy required to run a robot. Another feature is the smart power-saving mode that controls the robot’s energy supply on-demand throughout the workday. As industrial facilities need to monitor their energy consumption, such connected power sensors are likely to become an industry standard for robotic solutions[11]. Robots require a range of technologies to reach their full potential, including more efficient power sources like batteries, faster processors, and algorithms for improved decision-making ability, along with accurate sensors. To effectively interact with the environment, they need better sensors that can perceive data reliably. Achieving this level of efficiency requires greater processing capabilities. Additionally, reducing power consumption is essential, making energy-saving a critical challenge to overcome if these machines are to be useful in various applications worldwide[2]. The advancement of smaller, mobile robots that can manipulate objects with their entire arms or bodies, rather than large robotic arms that only grasp using fingertips, could help reduce energy consumption and drive down costs. This approach could be especially useful in exploration missions to Mars or other solar system bodies, where robots need to quickly adapt to their environments using only onboard computers[12]. Battery and power management are crucial for ensuring uninterrupted and prolonged operations. Autonomous Mobile Robots (AMRs), for instance, are often equipped with advanced lithium-ion batteries due to their high energy density, longer life cycles, and faster charging capabilities. Efficient battery management systems monitor battery health, optimize power consumption, and ensure that the AMR utilizes its energy resources effectively. This not only extends the battery lifespan but also ensures that the AMR can perform its tasks reliably over extended periods, significantly enhancing the return on investment and reliability of the technology[13]. The integration of green energy solutions in robotics is also being explored, including solar, wind, hydropower, geothermal, biomass, and biofuel energy.

Industry Applications

Manufacturing

In manufacturing, Industry 4.0 has widespread repercussions. It’s used to leverage operational efficiency, refine demand forecasting, break down data silos, engage in predictive maintenance, offer workers boosts to safety and virtual training, and more[14][15]. Industry 4.0, as part of a wider concept termed digital transformation, spans manufacturing from planning to delivery, with solutions for deep analytics, shop floor data sensors, smart warehouses, simulated changes, plus product and asset tracking[14]. For manufacturers, Industry 4.0 technologies help to bridge the gap between what were once separate processes to a more transparent, visible view across the entire organization, with plenty of actionable insights[15].

Zero Downtime

One of the primary aims of Industry 4.0 technologies in manufacturing is to achieve zero downtime. Advanced tools and systems, such as ABB Ability, FANUC FIELD, ZDT, and KUKA Connect, are employed to make robots more productive and minimize downtime[16]. One of the pioneering applications of this technology was a collaboration between General Motors, Cisco, and FANUC in the auto industry, which launched a zero downtime program called ZDT[16].

Cybersecurity in Manufacturing

With the integration of digital and smart technologies, the manufacturing industry faces significant cybersecurity risks, including the theft of intellectual property, manipulation of manufacturing equipment, identity theft, and ransomware[15]. Modern cybersecurity measures, such as blockchain and artificial intelligence, are essential to protect industrial IoT devices and other systems from both internal and external threats[15]. Companies can mitigate these risks by preparing comprehensive security plans and implementing robust cybersecurity protocols to safeguard their equipment and data[15].

Automation and Interoperability

Cheaper, smarter, and more adaptable automation systems are revolutionizing manufacturing. To capture the full value of these new systems, companies must align their automation strategies with business and operational goals[17]. Automation should improve worker safety, reduce costs, enhance quality, and increase flexibility[17]. Furthermore, the integration of automation systems with other organizational systems, including computer-aided design and enterprise-resource-planning systems, facilitates a seamless manufacturing process and allows real-time responsiveness to changes in demand or material availability[17].

The Cloud

The transition from manual labor to an interconnected, digitized manufacturing ecosystem marks the emergence of Industry 4.0. This transformation aims to create smart factories where machines, devices, and products communicate to increase efficiency and productivity[6]. Achieving these objectives necessitates the integration of advanced technologies such as artificial intelligence, big data analytics, and robotics[6].

Interoperability

Industry 4.0 relies on interconnectivity and interoperability, where machines, devices, and systems communicate seamlessly using standard communication protocols like OPC UA and MQTT[18]. This interoperability is crucial for integrating legacy systems with new technologies, facilitating a smooth transition to smart manufacturing, and enhancing collaboration among suppliers, manufacturers, and customers[18].

Agriculture and Food Production

Industry 4.0 technologies are also transforming agriculture and food production. Precision farming, smart irrigation, and optimized supply chains are made possible through IoT sensors and drones that collect real-time data on soil conditions, weather patterns, and crop health[18]. AI-powered algorithms optimize irrigation, fertilization, and pest control, improving crop yields and reducing environmental impact[18]. Additionally, blockchain technology enhances traceability and transparency in food supply chains, ensuring food safety and quality[18].

AI-Enhanced Capabilities

Artificial Intelligence (AI) has significantly transformed the capabilities of robotics by enabling machines to perform tasks that generally require human intelligence, such as learning, reasoning, and self-correction[8]. The integration of AI spans various domains, including healthcare, finance, and transportation, where it is utilized to analyze large datasets, automate tasks, and provide predictive insights[8]. This revolution has enhanced efficiency, improved decision-making, and created new opportunities for innovation across these sectors.

AI in Industrial Robotics

AI-first approaches are being adopted in the design of industrial robots, emphasizing the integration of intelligence at the core of machine operations rather than merely augmenting existing functionalities[19]. This methodology involves creating solutions that seamlessly blend hardware into software-defined infrastructures, thereby enhancing the performance, reliability, security, and safety of the task at hand[19]. Organizations leveraging AI-integrated robotics are predicted to achieve substantial operational cost savings and improved efficiencies across business operations[19].

AI-Enhanced Sensors and Data Processing

Significant advancements have been made in AI-enhanced sensors, which are critical in monitoring vital biomedical and physiological data[20]. Flexible sensors, energy harvesters, and self-powered sensors are paving the way for self-sustaining sensing systems characterized by energy autonomy and extended operational lifespans[20]. Moreover, combining multiple sensors and microprocessors allows for abundant sensing and the incorporation of AI functionalities, such as information processing, logical discrimination, and cognitive processes[21]. These innovations are particularly beneficial in applications like search and rescue, where comprehensive environmental sensing is crucial[22].

Intelligent Robot Structures

Inspired by biological organisms, researchers are developing intelligent robot structures for various scenarios, such as humanoid robots for household use and four-legged robots for navigating complicated terrains[21]. These robots are equipped with sensors that mimic human senses—vision, audition, tactile, olfactory, and taste—thus providing a natural bio-inspired approach to intelligent sensing[21]. Critical sensors like lidar, cameras, ultrasonic sensors, and inertial measurement units (IMUs) enable precise navigation, obstacle detection, and context understanding[23].

AI in Advanced Robotics Applications

AI is crucial in the development of sophisticated robotic systems, such as autonomous mobile robots (AMRs) and robotic hands. These systems employ advanced AI-focused cameras and software algorithms to analyze and process data in real time, ensuring high autonomy and low power consumption[24]. In the realm of dexterous manipulation, AI approaches particularly deep reinforcement learning, have achieved notable success in enabling robots to handle various objects with precision and dexterity[3]. However, challenges remain in improving the dexterity and control of robotic hands, especially in unstructured environments and media like water or oil[3].

Future Prospects

As we look ahead to 2024, the future of deep learning is characterized by innovation, diversity, and transformative potential. Trends such as federated learning, quantum computing, ethical AI, and sustainability are shaping the landscape and hold the promise of advancing AI capabilities to address societal challenges[25]. By fostering collaboration across disciplines, these advancements can help harness the full potential of deep learning, creating a brighter, more inclusive future for all.

AI and Robotics Integration

The landscape of the future of robotics is a vibrant tapestry of innovation and transformation. As we edge closer to 2030, robotics is not merely an isolated field of technology but an integral part of our evolving society. This integration will manifest in various aspects of life, from automated transportation and smart homes to personalized healthcare and education, fundamentally reshaping how we interact with technology and blurring the lines between human and machine capabilities[4]. By 2030, the ubiquity of robots in daily life will mark a significant shift, affecting social structures, economic models, and cultural norms[4].

Ethical and Diplomatic Considerations

As robotics becomes an integral part of the global landscape, international ethics and diplomacy will play increasingly important roles. The development and deployment of robotic technologies raise ethical questions that transcend national boundaries, such as data privacy, autonomous decision-making, and the impact of robotics on employment. Addressing these issues will require a global context and collaborative efforts[4].

Advanced Materials and Manufacturing

Another critical milestone in the future of robotics is the development of new materials and manufacturing techniques. These advancements will not only make robots more durable and resilient but also lighter and more flexible. This combination of strength and agility will be essential for robots designed for intricate tasks or to operate in challenging environments, such as deep-sea exploration or delicate surgical procedures. Breakthroughs in energy storage and efficiency will further enhance the sustainability and autonomy of future robots[4].

Personalized Experiences

One of the most exciting aspects of future robotics is the personalized experiences enabled by integrated technologies. Robots equipped with advanced sensors and AI will be able to understand and adapt to individual user preferences, creating more intuitive and helpful interactions. This personalization will extend to various applications, from healthcare and education to customized manufacturing processes, making these technologies more relevant and valuable to end users[4].

Industry 4.0 and Digital Integration

A fundamentally new approach to production systems, combining strategic principles with methods and tools that bring those principles to life, is pivotal for future manufacturing. Integrated systems based on a clear digital strategy are key to translating sustainability goals into tangible shop-floor actions and decisions. Industry 4.0 technologies, such as advanced robotics and 3D printing, promise to raise productivity by more than 10%, offering additional advantages in flexibility, quality, and resource consumption[9][26]. The journey to this future state will vary by company, plant, and specific value stream, depending on their maturity levels and ambitions. A careful cost-benefit analysis will be essential to tailor approaches that meet strategic needs and accelerate progress towards the factory of the future[9].

Ethical and Societal Implications

As robotics and AI technologies continue to advance, it is essential to consider their ethical and societal implications. The integration of AI in robotic systems raises several ethical considerations, including the need for technical robustness and safety, privacy and data governance, transparency, diversity, non-discrimination, fairness, societal and environmental well-being, and human agency and oversight.

Technical Robustness and Safety

AI systems should be reliable and stable, even in situations of uncertainty, and resilient against external manipulations. Ensuring technical robustness and safety is fundamental to maintaining trust in AI-driven robots[5].

Privacy and Data Governance

Throughout their lifecycle, AI systems must guarantee privacy and data governance. This includes controlling and managing data access and ensuring data quality[5]. Robust data governance frameworks are necessary to protect user information and maintain trust.

Transparency

Transparency in AI systems involves thorough documentation of data and processes to trace the causes of errors. These systems should be explainable to users at an appropriate level to understand the decisions being made[5]. This transparency helps in building trust and accountability in AI-driven robotics.

Diversity, Non-Discrimination, and Fairness

AI systems must be designed to control for biases that could lead to discriminatory results. Ensuring access to AI for all people is crucial to promoting fairness and inclusivity in society[5]. Identifying and mitigating biases in AI algorithms are essential steps in this process.

Societal and Environmental Well-Being

The use of AI should benefit society and the natural environment. It is essential to prevent violations of democratic processes and ensure that AI technologies contribute positively to societal and environmental well-being[5]. Ethical deliberations must consider the broader impact of AI on society and the environment.

Human Agency and Oversight

AI systems should support human autonomy and decision-making and be subject to human oversight. This requirement emphasizes the importance of human involvement in AI-driven decision processes to ensure ethical outcomes[5]. The notion of ethical deliberation cannot be reduced to mere computational tasks; human judgment remains essential[5].

Job Displacement and Workforce Adaptation

The potential for AI-powered robots to displace human workers is a significant societal concern. To address this challenge, investments in education and training programs are necessary to help individuals develop new skills required in an AI-driven world[27]. Collaboration between businesses, educational institutions, and governments will be crucial in preparing the workforce for future demands[4].

Legal and Ethical Frameworks

Establishing legal and ethical frameworks is vital to ensuring responsible AI development and deployment. Organizations like the IEEE and the European High-Level Expert Group on AI (HLEG-AI) have issued guidelines and standards for ethically aligned AI systems[7]. These frameworks help guide the ethical use of AI and robotics in civilian and military contexts.

Public Discourse and Cultural Influence

Public engagement in discussions about the ethical implications of robotics is essential for aligning technological advancements with societal values and ethical standards. Additionally, art and culture play a significant role in shaping public perceptions and understanding of robotics, provoking thought, and inspiring creativity[27]. By addressing these ethical and societal considerations, we can ensure that the future of AI-enhanced robotics is both beneficial and sustainable for all, creating a world where technological advancements are aligned with ethical principles and societal values.

References 

[1]: Electronics | Free Full-Text | Intelligent Robotics—A Systematic …
[2]: The Future of Robotics – Trends and Predictions for the Next Decade …
[3]: The Future of Robotics and Artificial Intelligence: Exploring AI and …
[4]: The Role of Robotics in Achieving Industry 4.0 Objectives
[5]: Augmented Reality Meets Artificial Intelligence in Robotics: A …
[6]: Dexterous Manipulation for Multi-Fingered Robotic Hands With …
[7]: Edge AI: A Taxonomy, Systematic Review and Future Directions – arXiv.org
[8]: Factory of the Future: How Industry 4.0 and AI Can Transform …
[9]: An industry 4.0 Case Study – The Digital Transformation People
[10]: Top 5 Robot Trends 2023 – International Federation of Robotics
[11]: AI helps robots manipulate objects with their whole bodies
[12]: Autonomous Mobile Robots: Your Guide to AMRs – boxlogix.com
[13]: Emerging Industry 4.0 Technologies With Real-World Examples
[14]: Industry 4.0 technologies with real-world examples
[15]: Robotics in the Age of Industry 4.0 | 2020-05-15 | ASSEMBLY
[16]: Automation, robotics, and the factory of the future | McKinsey
[17]: Industry 4.0 Explained: The 4th Industrial Revolution
[18]: The AI Impact for Next-Gen Industrial Robots | RoboticsTomorrow
[19]: Artificial intelligence enhanced sensors – enabling technologies to …
[20]: Bioinspired sensors and applications in intelligent robots: a review …
[21]: Sensor Fusion: The Ultimate Guide to Combining Data for … | Wevolver
[22]: How AI technology is set to enhance autonomous mobile robots – ZME Science
[23]: The Growing Intelligence of Autonomous Mobile Robots
[24]: Future of Deep Learning: 10 Trends and Innovations for 2024
[25]: Robotics and Industry 4.0: Reshaping the way things are made – ZDNET
[26]: Human-centered AI and robotics – AI Perspectives & Advances
[27]: Exploring How AI Can Improve Robotics in the Future
[undefined]: A guide to navigating the next-generation robotics wave | TNW