Assistive robotics: An overview

The introduction of Intelligent Robot Service is the direction of library development. The application of intelligent robot technology will greatly improve the library service efficiency and service level. At present, there are still some problems in library service of intelligent robot, such as the core technology needs to be mastered skillfully, the level of intelligent service is not enough, and the construction of Corpus needs to be improved. In the era of library intelligent service, intelligent robot should pay more attention to the application of multi-purpose intelligent robot system in library, the fusion of Intelligent Library and robot technology under the background of Internet of things, and the development trend of intelligent robot providing various care services for different readers. The introduction of intelligent robot technology will greatly improve the efficiency of books on and off shelves, and expand the space-time limit of reference service. In the process of building the intelligent library, the infrastructure and service of the traditional library are reconstructed by using the automatic book access robot and the Consulting robot system, it can be concluded that Book Auto-access Technology and IM consultation system are most suitable for library management system, and Book auto-access robot technology can solve the bottleneck problem of library intelligent robot technology, promoting the transformation and upgrading of libraries to smart libraries.

Summary form only given. A new program of intelligent service robot entitled as ubiquitous robotic companion (URC) project is introduced, where the project is funded by Ministry of Information and Communication, Republic of Korea from 2004 for 4 years. In the project, a new paradigm of intelligent robot, URC is newly defined by fusion of robot, network and information technologies, in which software robot and embedded robot are also introduced as well as the conventional robot. All three actors are connected by high speed network infra and provide high quality service to human users at any time and any place. That is why the project is entitled as ubiquitous robotic companion. In the presentation, some more details about URC together with consisting subprojects were introduced.

The contemporary robotics technology is broadening its applications from factory to more general-purpose applications in domestic and public use, e.g., partner to the elderly, rehabilitations, search and rescue, etc. If robotics technology is to be successful in such complex, unstructured, dynamic environments with high level of uncertainties, it will need to meet new levels of robustness, physical dexterity and cognitive capability. This presentation discusses an emerging field called cognitive robotics. The one solution for building cognitive robots in order to cope with imprecise, incomplete, and inconsistent information that arises in complex technical systems, is computational intelligence that uses biologically inspired soft-computing techniques, like artificial neural networks, evolutionary approaches, and swarm intelligence. Research topics, features and challenges of cognitive robotics will be introduced. Key challenges in constructing these robots include the systematic treatment of uncertainties, the modeling of the environmental state, the coordination of teams of cooperating robots in dynamic environments, the interaction with humans, development, and learning. It is important to notice that in order to realize cognitive robots many overlapping disciplines are needed, e.g. robotics, artificial intelligence, cognitive science, neuroscience, biology, psychology, and cybernetics. Some important research topics from this area will be specially analyzed: Advanced perception (vision, tactile sensing, haptic sensing, multi-sensor fusion), Advanced locomotion and manipulation, SLAM, Learning including imitation learning, reinforcement learning, supervised learning, Human-robot interaction, Reasoning and Making Decisions, Intelligent planning and navigation, Swarm intelligence, etc. A case study of cognitive methods applied for humanoid and service mobile robots will be introduced.

As the importance of the role of the state in the development of modern science and technology does not require additional discussion, so the development of our country's robot industry will ask for continuous review to see whether the current robot industry promotion legislation has sufficient content for robot industry promotion or it can be improved. Such review work is also an essential part for the realization of the principle of the rule of law that state intervention should always be carried out under the support of appropriate laws. For the effective conceptual regulation of “robot”, the target of policy, should be preceded to secure industrial competitiveness related to robot technology and promote policies for continuous development of robot industry, the study examines the appropriateness of the definition of “intelligent robot” of the current 「Intelligent Robots Development and Distribution Promotion Act」 - “The term "intelligent robot" means a mechanical device(including software required for operating the mechanical device) that perceives the external environment for itself, discerns circumstances, and moves voluntarily”(Article 2, 1.) - from the point of view of the analysis of systemicity(; Systematik) and understandability(Verständlichkeit) among the examination creteria of the legislative evaluation(Gesetzesevaluation). In conclusion, the concept of “intelligent robot” in the current law does not mean much in the legal system and it is not easy to understand either. And there is also a risk of unnecessarily reducing the applicable scope of the law in practice. So there is a need of deleting the modifier “intelligent” and it is proposed to use a semi-exemplary definition of an exemplary arrangement of targets for the robot industry promotion and support policy based on the concept of “robot” from a more technical point of view,

Industry 5.0 represents a significant shift in manufacturing, where human labor and modern robotic technologies are seamlessly integrated. Cognitive robots make proactive decisions and independently reconfigure their functions according to the situation. Cognitive robots can potentially transform many industries, from healthcare to manufacturing. They are intelligent machines equipped with the ability to perceive, reason, and learn, thereby enhancing productivity, flexibility, and safety in industrial settings. Through real-time data analysis and predictive maintenance, cognitive robots optimize production schedules, minimize downtime, and ensure quality control. The foundational concepts, key components of cognitive capabilities of robots, mechanisms of sensor fusion, machine learning (ML), and decision-making algorithms, etc. are discussed in detail in this work. In addition, factors such as safety, reliability, human-robot interaction (HRI), and ethical considerations are the challenges summarized in this chapter.

Robot technology at small- and medium-sized enterprises has become a crucial part of current business operations. Beginning with the manufacturing industry, more industries than ever before have recently begun making use of robot technology to increase operational efficiency and productivity. However, prior studies regarding innovation related to intelligent robot use have been limited to developing strategies for describing robot technologies in general. Therefore, we developed a research model for investigating process innovation as it relates to intelligent robots. Based on the literature, two variables of technology benefits (direct usefulness and indirect usefulness) and two constructs of environmental pressure (industry and government) were incorporated into the research model as key determinants of a firm’s process innovation. Furthermore, organizational supports as moderating variables were added to the relationship between technology benefits and process innovation. We collected 257 responses in managerial position at various firms in order to test the proposed hypotheses using structural equation modeling in the statistical software (AMOS 22.0). The results revealed that all variables have a significant impact on process innovation, as well as the moderator. The findings of this study provide theoretical and practical implications for process innovation based on intelligent robot technology.

A soft robot is a kind of robot designed to simulate mollusks. It has the characteristics of degrees of freedom, strong adaptability, and high flexibility and safety. The main purpose of this paper is to study the intelligent education assistance of soft robots and then combine the application of improved genetic algorithm and the Internet of Things technology in soft robots to improve its performance and effect. Therefore, this paper designs the optimal guidance strategy through the NSGA genetic algorithm and then combines the improved genetic algorithm and the application of the Internet of Things technology in the flexible actuator and FEA actuator of the soft robot. In order to optimize the performance of the IoT-assisted intelligent education software robot based on the improved genetic algorithm, the genetic algorithm simulation test experiment, the rolling motion simulation experiment of the bionic software robot, and the inflating and exhausting experiment of the base section of the software robot are designed and analyzed. Through the analysis of the data obtained from the experiment, this paper finally designs a set of controlled experiments to verify its teaching ability. The experimental results show that the students’ evaluation of the IoT intelligent education software robot education method based on the improved genetic algorithm designed in this paper is 16.31 points higher than that of the traditional education method. Compared with the traditional teaching, the scores of the students after the IOT intelligent education software robot teaching based on the improved genetic algorithm is 11.16 points higher.

This research constructs a disruptive technology identification framework based on attributes and functions and selects intelligent logistics robot technology for empirical analysis in the artificial intelligence & Internet of Things (AIoT) industry. We take the three attributes of technological novelty, breakthrough, and influence as measurement indicators to identify disruptive technologies and perform functional analysis of the technology to find potential markets for disruptive technologies. The research demonstrates that two‐dimensional code technology, sensing technology, intelligent control technology, artificial intelligence technology, and wireless communication technology are disruptive technologies in intelligent logistics robots. The future development directions of intelligent logistics robots include efficient, intelligent control operations, good human‐computer interaction, accurate safety obstacle avoidance, and accurate and efficient position detection. This research has put forward the future development direction of disruptive technology in AIoT and provided a reference for the R&D and manufacture of intelligent logistics robots.

Artificial intelligence (AI) is playing an increasingly vital role in transforming the medical field, particularly in areas like medical imaging, clinical decision-making, pathology, and minimally invasive surgery. The rapid growth of medical data and the continuous refinement of machine learning algorithms have propelled AI's integration into healthcare. This study explores the advancements and applications of AI, specifically machine learning algorithms and intelligent medical robots, in enhancing diagnostics, treatment, and healthcare delivery. A comprehensive review of current AI applications in healthcare, including its use in medical imaging, pathology, clinical decision-making, and robotic-assisted surgery, was conducted. AI technologies such as the Da Vinci Surgical Robot and machine learning-based diagnostic tools have significantly improved diagnostic accuracy and the precision of minimally invasive surgeries. AI-driven systems also contributed to better clinical decision support, faster recovery times for patients, and more accurate treatment plans. Overall, AI, through machine learning algorithms and intelligent medical robots, is revolutionizing healthcare by offering promising improvements in diagnostics, surgical precision, and patient care.

The Robot Grasping and Manipulation Competition, held during the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) in Daejeon, South Korea was sponsored by the IEEE Robotic and Automation Society (RAS) Technical Committee (TC) on Robotic Hands Grasping and Manipulation (RHGM) [1]. This competition was the first of a planned series of grasping and manipulation-themed events of increasing difficulty that are intended to spur technological developments and advance test methods and benchmarks so that they can be formalized for use by the community. The coupling of standardized performance testing with robot competitions will promote the use of unbiased evaluation methods to assess how well a robot system performs in a particular application space. A strategy is presented for a series of grasping and manipulation competitions that facilitate objective performance benchmarking of robotic assembly solutions. This strategy is based on test methods that can be used for more rigorous assessments and comparison of systems and components outside of the competition regime. While competitions have proven to be useful mechanisms for assessing the relative performance of robotic systems with measures of success, they often lack a methodical measurement science foundation. Consequently, scientifically sound and statistically significant metrics, measurement, and evaluation methods to quantify performance are missing. Using performance measurement methods in a condensed format will accommodate competition time limits while introducing the methods to the community as tools for benchmarking performance in the developmental and deployment phases of a robot system. The particular evaluation methods presented here are focused on the mechanical assembly process, an application space that is expected to accelerate with the new robot technologies coming to market.

Digitalisation and intelligent robotics in value chain of circular economy oriented waste management – A review

The fluidic muscle cylinder consists of an air bellows tube, flanges and lock nuts. Its features are softness of material and motion, simplicity of structure, low production cost and high power efficiency. Recently, unlikely the pneumatic cylinder, the fluidic muscle cylinder without air leakage, stick slip, friction, and seal was developed as a new concept actuator. It has the characteristics such as light weight, low price, high response, durable design, long life, high power, high contraction, which is innovative product fulfilling RT(Robot Technology) which is one of the nation-leading next generation strategy technologies 6T as well as cleanness technology. The application fields of the fluidic muscle cylinder are so various like fatigue tester, brake, accelerator, high technology testing device such as driving simulator, precise position, velocity, intelligent servo actuator under special environment such as load controlling system, and intelligent robot. In this study, we carried out the finite element modeling and analysis about the main design variables such as contraction ration and force, diameter increment of fluidic muscle cylinder. On the basis of finite element analysis, the prototype of fluidic muscle cylinder was fabricated and tested. Finally, we compared the results between the test and the finite element analysis.

The Robot World Cup Initiative (RoboCup) is an international joint project to promote science and technology in intelligent robotics, AI and related fields with the specific goal of building a fully humanoid robot that can be the human world cup champion by 2050, and to apply technologies developed in this activity to socially significant issues such as rescue, and for next generation industries. This idea inspired the imagination of people and it has grown to be one of the largest research areas in multi-agent robotics. The paper discusses extension of RoboCup activity for education and edutainment. Specifically, RoboCup has initiated a new project called RoboCup Jr. that aims at promotion of engineering education and popularization of robotics using RoboCup. A RoboCup Jr. league will be created to meet diverse needs of the education field, and a series of organizational frameworks are proposed to develop a network of social, personal, educational, and technical infrastructures to assist those who are interested in RoboCup Jr. activity.

With the rapid advancement of robot technology, indoor positioning and navigation for robots has become a crucial area of research in intelligent robotics. This paper presents a positioning method that fuses data from an inertial measurement unit (IMU) and an acoustic sensor. Additionally, it incorporates an omnidirectional travel strategy to enhance path tracking. The comparative experiment examines three path-tracking methods: pure sound tracking, IMU-integrated sound tracking, and a combination of IMU sound tracking and omnidirectional travel. The experimental results show that the tracking way that only relies on sound signals is greatly affected by environmental noise and reflection, resulting in path deviation. In contrast, sound tracking with IMU can effectively reduce the deviation of the path and improve navigation accuracy. Furthermore, after introducing the omnidirectional travel mode, the robot can intelligently choose whether to move forward or backward, optimizing the displacement offset during turns. It enhances the flexibility and efficiency of the overall path tracking. Experiments verify the effectiveness and practicability of this method in low-cost indoor positioning systems and offer new solutions for the application of smart homes, industrial automation, and unmanned distribution.

Path planning is one of the key technologies for intelligent robots to achieve autonomous navigation. The A star algorithm, as a classic heuristic search algorithm, has been widely applied in robot path planning. However, the traditional A star algorithm faces issues such as low search efficiency and non-smooth paths when dealing with complex environments. This paper proposes a path planning method for intelligent robots based on an improved A star algorithm. By optimizing the heuristic function, search neighborhood, and key node extraction strategies, the efficiency of path planning and the quality of the planned path are significantly enhanced. Experimental results demonstrate that the improved A star algorithm outperforms the traditional A star algorithm in terms of path length, planning time, and path smoothness, providing effective support for the efficient navigation of intelligent robots in complex environment.