Development Of Robotic System Research Articles

Intelligent Robotic Arm-Assisted Electrical Furnace for Efficient Metal Casting

Abstract: This project proposes the development of an intelligent robotic arm-assisted electrical furnace system for efficient and precise metal casting. Electric furnace is used for heating purpose in various industrial production processes. Electric furnaces are used where more accurate temperature control is required. By combining the strengths of robotic automation with the clean and controllable environment of an electric furnace, the system aims to achieve Enhanced quality and reduced waste, Improved safety and reduced hazards, increased efficiency and productivity, Greater flexibility, and versatility, Reduced environmental impact. The project will explore the integration of sensors enhancing efficiency and quality control. The integration of these technologies aims to enhance productivity, precision, and safety while reducing operational costs. The robotic arm is designed to perform loading and unloading tasks, handling raw materials and moulds with exceptional accuracy and repeatability. The robotic arm can be attached to the electrical furnace. With the help of sensors, robots can manage to avoid obstacles on their paths and achieve their goals with or without prior planning, depending on the complexity of their implemented program.

Development of an In-Pipe Inspection Robot for Large-Diameter Water Pipes.

This paper describes the development of an in-pipe inspection robot system designed for large-diameter water pipes. The robot is equipped with a Magnetic Flux Leakage (MFL) sensor module. The robot system is intended for pipes with diameters ranging from 900 mm to 1200 mm. The structure of the in-pipe inspection robot consists of the front and rear driving parts, with the inspection module located centrally. The robot is powered by 22 motors, including eight wheels with motors positioned at both the bottom and the top for propulsion. To ensure that the robot's center aligns with that of the pipeline during operation, lifting units have been incorporated. The robot is equipped with cameras and LiDAR sensors at the front and rear to monitor the internal environment of the pipeline. Pipeline inspection is conducted using the MFL inspection modules, and the robot's driving mechanism is designed to execute spiral maneuvers while maintaining contact with the pipeline surface during rotation. The in-pipe inspection robot is configured with wireless communication modules and batteries, allowing for wireless operation. Following its development, the inspection robot underwent driving experiments in actual pipelines to validate its performance. The field test bed used for these experiments is approximately 1 km in length. Results from the driving experiments on the field test bed confirmed the robot's ability to navigate various curvatures and obstacles within the pipeline. It is posited that the use of the developed in-pipe inspection robot can reduce economic costs and enhance the safety of inspectors when examining aging pipes.

Robot base placement and tool mounting optimization based on capability map for robot-assistant camera holder

Abstract In the field of laparoscopic surgery, research is currently focusing on the development of new robotic systems to assist practitioners in complex operations, improving the precision of their medical gestures. In this context, the performance of these robotic platforms can be conditioned by various factors, such as the robot’s accessibility and dexterity in the task workspace. In this paper, we present a new strategy for improving the kinematic and dynamic performance of a 7-degrees of freedom robot-assisted camera-holder system for laparoscopic surgery. This approach involves the simultaneous optimization of the robot base placement and the laparoscope mounting orientation. To do so, a general robot capability representation approach is implemented in an innovative multiobjective optimization algorithm. The obtained results are first evaluated in simulation and then validated experimentally by comparing the robot’s performances implementing both the existing and the optimized solution. The optimization result led to a 2% improvement in the accessibility index and a 14% enhancement in manipulability. Furthermore, the dynamic performance criteria resulted in a substantial 43% reduction in power consumption.

Vision Assisted Robotic Arm Using Arduino

Abstract: In the era of automation, the fusion of robotics and computer vision offers promising solutions for sustainable industrial practices. This paper presents the development and implementation of a vision-assisted robotic arm system designed for efficient object sorting by color. Leveraging the integration of robotics and computer vision, the system demonstrates its potential to optimize industrial processes while promoting environmental sustainability. The paper discusses the construction, experimental results, insights gained, and future implications of the project, highlighting its contributions to sustainable development goals.

Numerical Simulation of Motion Test of Bionic Amphibious Quadruped Robot

Bionic amphibious quadruped robots are a huge leap in robotics, with the ability to navigate various terrains with agility and efficiency. In this study, the researchers utilized numerical simulations of motion tests to assess the locomotion skills of these extraordinary robotic systems. They learned a lot about their functioning and problems by rigorously analyzing performance parameters across various terrains and conditions, such as terrain adaptability, traversal speed, energy efficiency, stability, and manoeuvrability. The results they obtained highlighted the importance of adaptive control algorithms and mechanical designs in maximizing traversal speed and energy economy, especially while negotiating difficult terrains. In addition, the study demonstrated the interdisciplinary character of research in bionic amphibious quadruped robots, drawing on insights from biomechanics, control theory, robotics, and computational modelling. By combining previous research and utilizing computational simulation approaches, they opened the road for the creation of versatile and durable robotic systems capable of navigating difficult terrains and surroundings with precision and efficiency. Future research efforts could focus on improving design and control, investigating novel propulsion mechanisms, adaptive locomotion tactics, and biomimetic control algorithms to improve performance and adaptability. Addressing the challenges identified in the research and seizing opportunities for innovation will drive the development of robotic systems that push the limits of locomotion capabilities and open up new possibilities for applications in exploration, search and rescue, environmental monitoring, and beyond.

COMPUTER NAVIGATION AND ROBOTIC SURGERY DURING TOTAL KNEE ARTHROPLASTY

Total knee arthroplasty (TKA) is a successful treatment for knee osteoarthritis. The emphasis on optimal sizing and alignment of the components has led to an increase in the use of tools that allow for preoperative planning and verification of intraoperative steps. Computer navigation and robotic surgery have emerged as valuable tools for planning and performing surgery with greater precision and consistency. Objective. The aim of this paper is to organise information on the use of robotic systems in total knee arthroplasty based on own personal experience and analysis of contemporary literature sources. Methods. This study analysed professional articles that discussed the advantages and disadvantages of using robotic systems during knee arthroplasty. The information was obtained from electronic databases including PubMed, Scopus, Web of Science and Google Scholar, with a search span of over 20 years. Computerised or navigation devices allow the surgeon to enter anatomical data via an interface and receive feedback on the alignment of the implant and the knee as a whole, but cannot be programmed to perform additional tasks. Currently, several patented systems are available, and rapid technological advances in computer processing power have allowed for the rapid development of robotic surgical systems. Robotic systems usually provide feedback similar to navigation systems, but they can also be programmed to assist in specific surgical tasks. It is expected that these systems will become more reliable and accurate in the future, potentially leading to a reduced role for physicians in certain aspects of the surgical process, limiting their involvement to supervision, and thus improving the workflow of the operating room. The integration of new technologies, such as mixed reality, which overlays simulated images on real-life images, is expected to further expand the range of capabilities of these devices. But for now, it is crucial to establish the long-term outcomes of robotic-assisted total knee arthroplasty as a process to determine the viability of widespread adoption of these devices.

Mathematical Model Development of an In-Line Robotic Complex Mechanical System

Mathematical Model Development of an In-Line Robotic Complex Mechanical System

High Performance Proprioceptive Fiber Actuators Based on Ag Nanoparticles-Incorporated Hybrid Twisted and Coiled System.

Thermally driven fiber actuators are emerging as promising tools for a range of robotic applications, encompassing soft and wearable robots, muscle function restoration, assistive systems, and physical augmentation. Yet, to realize their full potential in practical applications, several challenges, such as a high operational temperature, incorporation of intrinsic self-sensing capabilities for closed-loop feedback control, and reliance on bulky, intricate actuation systems, must be addressed. Here, an Ag nanoparticles-based twisted and coiled fiber actuator that achieves a high contractile actuation of ≈36% is reported at a considerably low operational temperature of ≈83°C based on a synergistic effect of constituent fiber elements with low glass transition temperatures. The fiber actuator can monitor its contractile actuation in real-time based on the piezoresistive properties inherent to its Ag-based conductive region, demonstrating its proprioceptive sensing capability. By exploiting this capability, the proprioceptive fiber actuator adeptly maintains its intended contractile behavior, even when faced with unplanned external disturbances. To demonstrate the capabilities of the fiber actuator, this study integrates it into a closed-loop feedback-controlled bionic arm as an artificial muscle, offering fresh perspectives on the future development of intelligent wearable devices and soft robotic systems.

Development of a Novel Robotic Rehabilitation System Using Electrical Stimulation for Upper Limb Motor Recovery

To improve the quality of life for people with neurological disorders or accidents, it is important to create therapy tools for upper limb movement recovery that work and are easy for everyone to use. This study suggests a new artificial therapy device that uses electrical stimulation to help people recover their upper limb movement skills. The technology uses both artificial help and electrical treatment at the same time to help the brain change and learn new skills. The robotic therapy system is made up of a suit that moves with the upper limbs and supports them mechanically. It has sensors that can figure out what the user wants and change the amount of help based on that. In addition, the system has electrical activation probes that are put to target specific muscles or groups of muscles that help move the upper limbs. A closed-loop control system makes the system work. The user's moves are constantly tracked, and the robotic help and electrical stimulation are changed in real time. This flexible method lets recovery plans be made just for each person, based on their wants and results. A pilot study with people who have trouble moving their upper limbs was used to test how well the planned method would work. When the method was used for six weeks, the data showed big gains in motor function, muscle strength, and range of motion. Participants were also very happy with how easy the method was to use and how well it helped them improve their movement skills.

Investigation of Artificial Intelligence Algorithms in Robot Object Recognition Systems Under the Background of Big Data

In the long history of human beings, with the continuous exploration and research of natural phenomena and social life, many scientific fields have emerged, and robots are the product of this technological development to a certain stage. At present, there are hundreds of different types of robots applied in production and daily life in the world, which have achieved significant economic benefits. However, its technical issues have gradually emerged. For example, the shortcomings in visual perception and other aspects cannot be effectively addressed. Object recognition is not precise enough, and information resources cannot be effectively utilized to achieve control functions. These are the main factors that constrain the further progress and improvement of robots. The emergence of big data and Artificial Intelligence (AI) has brought unprecedented opportunities to robots. Especially, the application of big data analysis in intelligent manufacturing and smart city construction is becoming increasingly widespread, thus providing new solutions for robot services. They not only enable people to quickly and accurately grasp a large amount of valuable knowledge, but also better tap into the enormous potential contained in human intelligence, which largely drives the robot industry towards intelligence. By summarizing the existing research results, this paper explored the development trend of robot object recognition systems, and focused on its key technologies, the feature matching-based pattern recognition and acceleration strategy-based detection efficiency improvement. In response to the current problems, corresponding solutions were proposed and comparative experiments were designed. This proved that the anti-interference detection accuracy of the robot object recognition system based on big data and AI algorithm improved by about 12.48%, thus hoping to provide reference for future robot system development.

Conceptual directions for the development of driverless agricultural mobile power units

BACKGROUND: Currently, major global developers and manufacturers in the field of mobile agricultural machinery are working on the development of agricultural robotic systems. Particular attention is paid to the development of universal driverless mobile power units (MPU) capable of performing various technological operations autonomously, without human intervention. In the future, this makes it possible to exclude the operator from the MPU control process and to reconsider approaches to the issue of increasing the efficiency of technological operations. The existing trend of productivity improvement by increasing the main parameters of the unit, such as operating width, operating velocities, load capacity, etc., may change to an alternative path consisting in the use of numerous autonomous small-sized units comparable in performance (a swarm of agricultural robots). Thus, the use of driverless control systems makes it possible to use conceptually new approaches to the development of agricultural MPUs. In this regard, it becomes relevant to conduct the study aimed at identifying promising conceptual directions for the development of driverless MPUs and evaluating the efficiency of their application. AIM: Identification of conceptual directions for the development of driverless driverless MPUs and a theoretical assessment of the efficiency of their application. METHODS: The study object was the MPU transformation in the context of the development of driverless control systems. The study was based on scientific publications on the development of robotic agricultural tools, informational data of manufacturers of agricultural tractors and control systems for agricultural machinery. In the course of the study, such methods as information analysis, synthesis, methods of performance analysis of agricultural units and analysis of present cost of performing technological operations, adapted for driverless MPUs by the VIM, were used. RESULTS: The prospects for the introduction of driverless MPUs, the existing digital and intelligent control systems of MPUs and the main factors hindering their development are analyzed. A classification of agricultural MPUs according to automation levels is proposed. The main directions of development are identified and conceptual models of driverless MPUs are proposed: universal driverless MPUs (driverless tractors) with keeping the existing traction class and power classification, universal (multifunctional) low-power driverless MPUs of the only traction class, separate power modules capable of being combined into a single driverless unit based on the coupled agricultural machine. The method is proposed and the equivalent number of driverless MPUs of each conceptual model for each traction class is calculated. An assessment of the impact of the use of the proposed conceptual models of driverless MPUs on the arable unit performance and the present cost of arable operations has been carried out. CONCLUSIONS: Conceptual models for the advancing of driverless MPUs have been developed and comparative calculations of the efficiency of their application as part of arable units, helping to assess the possible prospects for their use, have been made.

Pentafecta evaluation in robot-assisted radical prostatectomy by risk group.

Radical prostatectomy is a therapeutic option in organ-confined prostate cancer. As the development of robotic systems progresses, the approach with this technology has begun to impact the functional and oncological outcomes of urological patients. The objective is to report the rate of pentafecta in patients undergoing robot-assisted radical prostatectomy (RARP) stratified by risk groups. Retrospective, observational, descriptive study from 2013 to 2020 that included 112 patients undergoing RARP. A rate of pentafecta at 12 months of follow-up of 35.7% (n = 40) was obtained. In the subanalysis by risk groups, at 1-year follow-up, was obtained an index of 43% (n = 26), 26% (n = 9) and 22% (n = 4) in low-, intermediate-, and high-risk patients, respectively. Prostatectomy showed functional and oncological results similar to those reported in the literature with robotic approach, regardless of the risk group for prostate cancer.

Implementation of singularity-free inverse kinematics for humanoid robotic arm using Bayesian optimized deep neural network

This study presents the efficacy of deep learning techniques in controlling the arm of a humanoid robot without resorting to inverse kinematic analysis. Emphasizing real-time applicability, a straightforward deep neural network (DNN) structure is developed and optimized using hyperparameter Bayesian optimization. The Keras Bayesian Optimization Tuner with Gaussian process fine-tunes the DNN architecture. Utilizing a simulation environment and generating around 10 billion datasets, the optimized DNN is evaluated on both simulated and real robots. Impressively, the trained DNN exhibits a notable ability to predict robot control within a 25 ms timeframe, achieving a total Mean Absolute Error (MAE) of 0.02 for the xyz-axes. These results underscore the potential of deep learning-based approaches in humanoid robot arm control, eliminating the need for inverse kinematic equations and providing valuable insights for future robotic system development.

AI and human-robot interaction: A review of recent advances and challenges

The integration of artificial intelligence (AI) into human-robot interaction (HRI) has witnessed significant advancements in recent years, revolutionizing the way humans and robots collaborate and coexist. This review provides a comprehensive overview of the latest breakthroughs in AI-driven HRI and identifies the challenges that lie ahead. Recent years have seen a surge in AI-driven capabilities that enhance human-robot interaction. Machine learning algorithms enable robots to adapt to user preferences and behaviors, creating personalized and intuitive interactions. Natural language processing (NLP) facilitates seamless communication between humans and robots, enabling voice commands and context-aware responses. Computer vision advancements empower robots with enhanced perception, enabling them to recognize and interpret human gestures, emotions, and facial expressions. Reinforcement learning has played a pivotal role in enabling robots to learn from human feedback and optimize their actions in real-time. Socially assistive robots leverage AI to provide emotional support and companionship, particularly in healthcare and elderly care settings. Despite these advancements, challenges persist in the field of AI-driven HRI. Ethical considerations, including privacy concerns and the responsible use of AI in influencing human behavior, demand careful attention. Ensuring the safety and security of AI-driven robotic systems remains paramount, requiring robust measures against malicious attacks and unintended consequences. Human-robot trust remains a critical challenge, necessitating transparent AI algorithms and effective communication strategies. Interdisciplinary collaboration between AI researchers, roboticists, psychologists, and ethicists is essential to address the complex socio-technical aspects of HRI. The fusion of AI and human-robot interaction holds immense potential to redefine various facets of our daily lives. This review highlights recent strides in AI-driven HRI, emphasizing the need for interdisciplinary efforts to address challenges and ensure the responsible development and deployment of AI-powered robotic systems. As researchers continue to innovate, the dynamic landscape of AI and human-robot interaction promises a future where seamless collaboration and coexistence between humans and robots become an integral part of our societal fabric.

Design of a robotic system to assist in the treatment of severe COVID‐19 patients

AbstractThe article presents the conceptualization, development, and implementation of a sophisticated mobile robotic system with the purpose of providing aid in the medical care of those afflicted with severe cases of COVID‐19. The robotic system will engage in verbal communication with the patient and provide updates regarding the external environment. Additionally, it will facilitate the delivery of food, beverages, and other consumable items to the isolation room. The robot has the capability to navigate to its intended location using two distinct modes: autonomous mode and online control mode. The hardware is constructed within a mobile robot system that is interconnected to the Internet over a 4G mobile network. The system employs a client–server software architecture wherein the transmission of data between the client and the server occurs through various transport protocols. Experimental simulations were conducted in an active treatment room to evaluate the performance of autonomous operating mechanisms, including obstacle avoidance positioning, safe destination navigation, and feedback display for remote robot operation.

Design and development of robotic system for precise vascular access and autonomous venipuncture

AbstractThe medical robot systems have the effect of reducing the work load and preventing accidents by supporting task that are difficult for inexperienced medical practitioners. Recently, research on automated blood collection and injection robotic system has been increasing, but work is still necessary such as replacing needle tips, setting tourniquet, and fixing the patient's arm. This letter proposes an intelligent intravenous injection robot without having to see the medical workers face‐to‐face. A revolving system is designed for automatic needle replacement and an arm holder for vessel fixation. Finally, positioning the needle at the target point is experimented by position control based on mathematical modelling and velocity kinematics.

Advancements in robotics engineering: Transforming industries and society

Robotics engineering is a dynamic and multidisciplinary field that has undergone rapid evolution in recent years, transforming various aspects of our lives. This paper serves as a comprehensive exploration of robotics engineering, delving into its historical development, contemporary applications, and the exciting prospects that lie ahead. The history of robotics engineering is a fascinating journey, from the early automata and industrial robots to the cutting-edge autonomous systems and humanoid robots of today. It traces the evolution of technology and human ingenuity, showcasing how robotics has become an integral part of our modern world. Current applications of robotics engineering span a wide range of industries, from the manufacturing sector, where robots enhance efficiency and precision, to healthcare, where they assist in surgery and patient care. Furthermore, robots are venturing into hazardous environments, disaster response, and space exploration, expanding their utility. However, as robotics technology continues to advance, it raises ethical and societal questions. This paper will explore these concerns, such as the impact on employment, privacy, and human-robot interaction, emphasizing the importance of responsible development and use of robotic systems. In conclusion, robotics engineering is a transformative force, shaping industries and influencing society in profound ways. This paper seeks to shed light on its pivotal role in our world, inspiring discussions and responsible practices that ensure its positive impact on the future.

Research of microprocessor device and software for remote control of a robotic system

The modern stage of the development of intelligent robotic systems is characterized by the expansion of fields of application, which is due to autonomous work and decision-making in conditions of uncertainty. The object of research is the system of remote control of robotic systems. During the remote control of robotic systems, problems arise that are associated with the use of wireless communication in real time. The article analyzes software and hardware implementations of various remote control systems suitable for use as part of autonomous robotic systems and analyzes promising microcontroller platforms for implementing a remote control device for a robotic system. A brief review of existing protocols for transmitting control signals using radio communication equipment and microprocessor platforms for the development of embedded systems is performed, among which a solution is selected for research. Several approaches to the control of a robotic system are highlighted – control using a wired connection and corresponding protocols, control via wireless communication or via the Internet, control via general-purpose network protocols. The target platform is chosen and justified, and the S.BUS protocol is analyzed with the provision of an algorithm for obtaining the values of the control channels from the S.BUS package. The structure and algorithm of functioning of the microprocessor remote control system based on the ESP32 microcontroller and the FreeRTOS OS are given. A study of the operation process of the proposed remote control system is carried out, for which it is placed on the chassis of a ground autonomous robotic system with four-wheel drive, and the delay time of the control signal from the receiver to the engine control modules is determined. According to the conducted analysis, the expediency of using specialized radio communication equipment with the S.BUS protocol for controlling executive devices as part of a robotic system, for precise movement control in real time, is shown.

A self-organizing robotic aggregate using solid and liquid-like collective states.

Designing robotic systems that can change their physical form factor as well as their compliance to adapt to environmental constraints remains a major conceptual and technical challenge. To address this, we introduce the Granulobot, a modular system that blurs the distinction between soft, modular, and swarm robotics. The system consists of gear-like units that each contain a single actuator such that units can self-assemble into larger, granular aggregates using magnetic coupling. These aggregates can reconfigure dynamically and also split into subsystems that might later recombine. Aggregates can self-organize into collective states with solid- and liquid-like properties, thus displaying widely differing compliance. These states can be perturbed locally via actuators or externally via mechanical feedback from the environment to produce adaptive shape-shifting in a decentralized manner. This, in turn, can generate locomotion strategies adapted to different conditions. Aggregates can move over obstacles without using external sensors or coordinates to maintain a steady gait over different surfaces without electronic communication among units. The modular design highlights a physical, morphological form of control that advances the development of resilient robotic systems with the ability to morph and adapt to different functions and conditions.

Petri Nets for the Iterative Development of Interactive Robotic Systems

We argue for the use of Petri nets as a modeling language for the iterative development process of interactive robotic systems. Petri nets, particularly Timed Colored Petri nets (TCPNs), have the potential to unify various phases of the development process-design, specification, simulation, validation, implementation, and deployment. We additionally discuss future directions for creating a domain-specific variant of TCPNs tailored specifically for HRI systems development.