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Industrial mobile robots easily experience actuator loss of some effectiveness and additive bias faults due to the working scenarios, resulting in unexpected performance degradation. This article proposes a novel adaptive fault-tolerant control (FTC) strategy for nonholonomic mobile robot systems subject to simultaneous actuator lock-in-place (LIP) and partial loss-of-effectiveness (LOE) faults. First, a nominal fractional-order sliding mode controller based on the designed exponential super-twisting reaching law is investigated to reduce the reaching phase time and eliminate the chattering. To address the time-varying LIP faults and uncertainties, a novel barrier function (BF)-based gain is explored to assist the super-twisting law. An estimator is designed to estimate the lower bound of the time-varying partial LOE fault coefficients, thus without requiring the boundary information of faults that is commonly requested in traditional FTC schemes. Combined with the nominal controller clubbed with BF and estimator-based LOE fault compensation term, the fault-tolerant controller is finally constructed. The proposed FTC scheme achieves fast convergence and the sliding variables can be confined in a predetermined neighborhood of the sliding manifold under actuator faults. The results show that the proposed controller has superior tracking performance under faulty conditions compared with other state-of-the-art adaptive FTC approaches. [ABSTRACT FROM AUTHOR]

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Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use. [ABSTRACT FROM AUTHOR]

Soft robots are increasingly gaining attention not only within the scientific community, but also in various real-world applications, due to their inherent compliance, ease of manufacture and favourable mechanical properties. These robots can manipulate a wide range of objects without the need for precise feedback on position, orientation, force, and torque. Their low elastic moduli make them inherently safe. However, although the field of soft robotics has been well established now, there is still a significantly smaller number of successfully realized applications in the domain of mobile soft robotics. This article presents a detailed development process of a tethered mobile caterpillar-like soft robot. A single part mould is designed and used to cast the silicone body of the robot with integrated four Shape Memory Alloy tendons, allowing generation of the inching motion pattern. The detailed analysis of the manufacturing process for robot fabrication, with the necessary hardware and software used for robot control is presented. Experimental verification shows usability of presented approach, but also reveals limitations mainly due to insufficient cooling of the Shape Memory Alloy tendons which limits the number of cycles per time. [ABSTRACT FROM AUTHOR]

In order to improve the trajectory control effect of multi‐degree‐of‐freedom industrial robots, this paper combines visual image technology to conduct research on trajectory control of multi‐degree‐of‐freedom industrial robots. Aiming at the problem of video segmentation under sudden illumination changes, this paper uses a Gaussian mixture model based on the global illumination function to adopt a variety of illumination invariant features, and proposes a scene segmentation algorithm suitable for sudden illumination changes. Moreover, this paper compares and verifies the algorithm from the subjective and objective perspectives through experiments, which shows that the algorithm in this paper can segment the scene more accurately even in the environment of sudden changes in illumination. In addition, the results of the accuracy test and the trajectory control test show that the research method of the multi‐degree‐of‐freedom industrial robot trajectory control based on the visual image proposed in this paper can effectively improve the trajectory control effect of the robot. [ABSTRACT FROM AUTHOR]

When a robot performs tasks such as assembly or human–robot interaction, it is inevitable for it to collide with the unknown environment, resulting in potential safety hazards. In order to improve the compliance of robots to cope with unknown environments and enhance their intelligence in contact force-sensitive tasks, this paper proposes an improved admittance force control method, which combines classical adaptive control and machine learning methods to make them use their respective advantages in different stages of training and, ultimately, achieve better performance. In addition, this paper proposes an improved Deep Deterministic Policy Gradient (DDPG)-based optimizer, which is combined with the Gaussian process (GP) model to optimize the admittance parameters. In order to verify the feasibility of the algorithm, simulations and experiments are carried out in MATLAB and on a UR10e robot, respectively. The experimental results show that the algorithm improves the convergence speed by 33% in comparison to the general model-free learning method, and has better control performance and robustness. Finally, the adjustment time required by the algorithm is 44% shorter than that of classical adaptive admittance control. [ABSTRACT FROM AUTHOR]

Robotic surgery has been steadily growing, with many new platforms entering the field. Research platforms, however, are limited in number, require a sizable capital expenditure or are difficult to access. This paper presents the analysis and development of a novel surgical manipulator based on parallel kinematics, utilizing the Delta robot as a foundational element. We investigate various aspects including kinematics, statics, workspace and constraints of the manipulator. Additionally, a physics-based model is constructed to validate the analysis and facilitate the creation of a control algorithm aimed at input tracking, particularly for teleoperation purposes. Two experiments are conducted to evaluate the manipulator's performance: one focusing on circle tracking and a second one employing real kinematic data from a suturing task. The results indicate a maximum tracking error under 1 mm and an RMS error below 0.6 mm for the first trial and 0.3 mm by 2 mm for the suturing tracking task, respectively. Furthermore, through non-linear Bode analysis we demonstrate that the closed-loop system effectively decouples input–output cross-gain terms while maintaining minimal amplification in the diagonal terms. This suggests that the system is well-suited for the intricate and precise motions required in surgical procedures. [ABSTRACT FROM AUTHOR]

This article presents the design and implementation of an innovative human–machine interface (HMI) in mixed reality for a robot model operating within Robot Operating System 2 (ROS 2). The interface is specifically developed for compatibility with Microsoft HoloLens 2 hardware and leverages the Unity game engine alongside the Mixed Reality Toolkit (MRTK) to create an immersive mixed reality application. The project uses the Turtlebot 3 Burger model robot, simulated within the Gazebo virtual environment, as a representative mechatronic system for demonstration purposes. Communication between the mixed reality application and ROS 2 is facilitated through a publish–subscribe mechanism, utilizing ROS TCP Connector for message serialization between nodes. This interface not only enhances the user experience by allowing for the real-time monitoring and control of the robotic system but also aligns with the principles of Industry 5.0, emphasizing human-centric and inclusive technological advancements. The practical outcomes of this research include a fully functional mixed reality application that integrates seamlessly with ROS 2, showcasing the potential of mixed reality technologies in advancing the field of industrial automation and human–machine interaction. [ABSTRACT FROM AUTHOR]

Capsule medical robots with unique application advantages have broad prospects for gastrointestinal detection, targeted drug delivery, medical assistance, and other fields. However, due to the complexity of the gastrointestinal environment and the limitations of the space magnetic field, robust motion control of capsule robots is still a challenge. Using a permanent magnet (NdFeB) as a space magnetic source, a capsule robot manipulation instrument with an integrated motion intelligent control approach is proposed in this work. First of all, a steering fixture controlling a permanent magnet is designed, and a manipulation instrument capable of five degrees of motion freedom is built with low cost and high accuracy. Furthermore, an integrated motion control approach, consisting of a primary motion subsystem and an auxiliary motion subsystem, is presented, where the motion route of the capsule robot is planned. A parallel optimization strategy is adopted to improve the traditional Gray Wolf Optimization Algorithm, the improved version of which is utilized to tune the parameters of controllers. Finally, some experiments of the designed capsule robot are carried out in different planes and slopes, as well as simulated stomach and real pig stomach, respectively. The results show that the motion characteristics are continuous and stable, and the average position error is 4.2 mm, which meets the application requirements. [ABSTRACT FROM AUTHOR]

Robots with flexible joints are gaining importance in areas such as collaborative robots (cobots), exoskeletons, and prostheses. They are meant to directly interact with humans, and the emphasis in their construction is not on precision but rather on weight reduction and soft interaction with humans. Well-known rigid robot control strategies are not valid in this area, so new control methods have been proposed to deal with the complexity introduced by elasticity. Some of these methods are seldom used and are unknown to most of the academic community. After selecting the methods, we carried out a comprehensive comparative study of algorithms: simple gravity compensation (Sgc), the singular perturbation method (Spm), the passivity-based approach (Pba), backstepping control design (Bcd), and exact gravity cancellation (Egc). We modeled these algorithms using MATLAB and simulated them for different stiffness levels. Furthermore, their practical implementation was analyzed from the perspective of the magnitudes to be measured and the computational costs of their implementation. In conclusion, the Sgc method is a fast and affordable solution if joint stiffness is relatively high. If good performance is necessary, the Pba is the best option. [ABSTRACT FROM AUTHOR]

Climbing robots, with their expansive workspace and flexible deployment modes, have the potential to revolutionize the manufacturing processes of large and complex components. Given that the surfaces to be machined typically exhibit variable curvature, good surface adaptability, load capacity, and motion accuracy are essential prerequisites for climbing robots in manufacturing tasks. This paper addresses the manufacturing requirements of climbing robots by proposing an asymmetric independently steerable wheel (AISW) for climbing robots, along with the motion control method. Firstly, for the adaptability issue of the locomotion mechanism on curved surfaces under heavy load, an asymmetric independently steerable wheel motion module is proposed, which improves the steering difficulty of the traditional independently steerable wheel (ISW) based on the principle of steering assisted by wheels. Secondly, a kinematic model of the AISW chassis is established and, on this basis, a trajectory tracking method based on feedforward and proportional–integral feedback is proposed. Comparative experimental results on large, curved surface components show that the asymmetric independently steerable wheel has lower steering resistance and higher motion accuracy, significantly enhancing the reachability of climbing robots and facilitating their application in the manufacturing of large and complex components. [ABSTRACT FROM AUTHOR]

In this study, a cooperative robotic system comprising two Selective Compliance Assembly Robot Arm (SCARA) robots was developed and simulated. An algorithm was proposed for the coordination of robots in cooperative tasks, along with a Rapidly exploring Random Tree (RRT) path planner for obstacle avoidance. The proposed system proved effective in transferring objects between robots and in handling various scenarios of variable complexity without collisions. The implementation of advanced trajectory planning and coordination algorithms significantly improves the adaptability and autonomy of robotic systems, allowing robots to predict and react to the movements of their counterparts and changes in the environment in real time. This capability is crucial for maintaining a safe and efficient work environment. The importance of synchronization and effective communication between robots is highlighted to avoid collisions and optimize trajectories and cycle times. All tests were conducted in virtual environments, allowing for the evaluation and refinement of the performance of the robots' performance under controlled conditions. The positive results obtained in the simulations suggest that the system is well suited for future practical implementation in industrial and manufacturing applications, such as chemical handling, collaborative welding, quality inspection, among others. These findings underscore the potential of the cooperative SCARA system to improve the efficiency and safety in industrial applications using advanced algorithms and control techniques, establishing a solid foundation for future research and development in the field of cooperative robotics. [ABSTRACT FROM AUTHOR]

A coaxial helicopter with a maximum take-off weight of 600 kg was converted to an unmanned aerial vehicle. A minimally invasive robotic actuator system was developed, which can be retrofitted onto the copilot seat of the rotorcraft in a short period of time to enable automatic flight. The automatic flight control robot includes electromechanical actuators, which are connected to the cockpit inceptors and control the helicopter. Most of the sensors and avionic components were integrated into the modular robotic system for faster integration into the rotorcraft. The mechanical design of the control system, the development of the robot control software, and the control system architecture are described in this paper. Furthermore, the multi-body simulation of the robotic system and the estimation of the linear low-order actuator models from hover-frame flight test data are discussed. The developed technologies in this study are not specific to a coaxial helicopter and can be applied to the conversion of any crewed flight vehicle with mechanical controls to unmanned or fly-by-wire. This agile development of a full-size flying test-bed can accelerate the testing of advanced flight control laws, as well as advanced air mobility-related functions. [ABSTRACT FROM AUTHOR]

The utilization of the Delta parallel robot in high-speed and high-precision applications has been extensive, with motion stability being a critical performance measure. To address the inherent inaccuracies of the model and minimize the impact of external disturbances on motion stability, we propose an adaptive backstepping fractional-order non-singular terminal sliding mode control (ABF-NTSMC). Initially, by employing a backstepping algorithm, we select the virtual control for subsystems as the state variable function in joint space while incorporating a calculus operator to enhance fractional-order sliding mode control (SMC). Subsequently, we describe factors such as model uncertainty and external disturbance using a lumped uncertainty function and estimate its upper bound through an adaptive control law. Ultimately, we demonstrate system stability for our proposed control approach and provide an analysis of finite convergence time. The effectiveness of this presented scheme is demonstrated through simulation and experimental research. [ABSTRACT FROM AUTHOR]

Robots in hazardous environments demand precise and advanced motion control, making extensive simulations crucial for verifying the safety of motion planning. This paper presents a simulation system that enables interactive path editing, allowing for motion planning in a simulated collaborative robot environment and its real-world application. The system includes a simulation host, a control board, and a robot. Unity 3D on a Windows platform provides the simulation environment, while a virtual Linux environment runs ROS2 for execution. Unity sends edited motion paths to ROS2 using the Unity ROS TCP Connector package. The ROS2 MoveIt framework generates trajectories, which are synchronized back to Unity for simulation and real-world validation. To control the six-axis Indy7 collaborative robot, we used the MIO5272 embedded board as an EtherCAT master. Verified trajectories are sent to the target board, synchronizing the robot with the simulation in position and speed. Data are relayed from the host to the MIO5272 using ROS2 and the Data Distribution Service (DDS) to control the robot via EtherCAT communication. The system enables direct simulation and control of various trajectories for robots in hazardous environments. It represents a major advancement by providing safe and optimized trajectories through efficient motion planning and repeated simulations, offering a clear improvement over traditional time-consuming and error-prone teach pendant methods. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of dynamic modeling of a hybrid robotic arm, a dynamic model system of industrial robotic arm based on Simscape Multibody was established with the MG400 robotic arm as the research object, which combines the motion control and data acquisition modules. The model is dynamically visualized and provides a convenient platform for studying the control algorithm of the robot arm. In response to the issues of sluggish speed and substantial position error in robot trajectory tracking control of a traditional controller, a variable gain iterative learning control methodology was designed. The robot arm control system model was employed to corroborate the trajectory tracking control under the stipulated target trajectory. The empirical outcomes indicate that in comparison to the traditional controller and the fixed-gain iterative learning controller, the variable-gain iterative learning controller can regulate the robot end trajectory more precisely, with swift tracking speed and accurate tracking posture, demonstrating commendable feasibility and portability. It offers an open-source research and development platform for the dynamic modeling of robot arm and a potent solution for the control strategy of robot arm. [ABSTRACT FROM AUTHOR]

Assigning repetitive and physically demanding construction tasks to robots can alleviate human workers' exposure to occupational injuries, which often result in significant downtime or premature retirement. However, the successful delegation of construction tasks and the achievement of high-quality robot-constructed work requires transferring necessary dexterous and adaptive construction craft skills from workers to robots. Predefined motion planning scripts tend to generate rigid and collision-prone robotic behaviors in unstructured construction site environments. In contrast, imitation learning (IL) offers a more robust and flexible skill transfer scheme. However, the majority of IL algorithms rely on human workers repeatedly demonstrating task performance at full scale, which can be counterproductive and infeasible in the case of construction work. To address this concern, in this paper, we propose an immersive and Cloud Robotics-based virtual demonstration framework that serves two primary purposes. First, it digitalizes the demonstration process, eliminating the need for repetitive physical manipulation of heavy construction objects. Second, it employs a federated collection of reusable demonstrations that are transferable for similar tasks in the future and can, consequently, reduce the requirement for repetitive illustration of tasks by human agents. In addition, to enhance the trustworthiness, explainability, and ethical soundness of the robot training, this framework utilizes a hierarchical imitation learning (HIL) model to decompose human manipulation skills into sequential and reactive subskills. These two layers of skills are represented by deep generative models; these models enable adaptive control of robot action. The proposed framework has the potential to mitigate technical adoption barriers and facilitate the practical deployment of full-scale construction robots to perform a variety of tasks with human supervision. By delegating the physical strains of construction work to human-trained robots, this framework promotes the inclusion of workers with diverse physical capabilities and educational backgrounds within the construction industry. [ABSTRACT FROM AUTHOR]

The ability of robots to tackle complex non-repetitive tasks will be key in bringing a new level of automation in agricultural applications still involving labor-intensive, menial, and physically demanding activities due to high cognitive requirements. Harvesting is one such example as it requires a combination of motions which can generally be broken down into a visual servoing and a manipulation phase, with the latter often being straightforward to pre-program. In this work, we focus on the task of fresh mushroom harvesting which is still conducted manually by human pickers due to its high complexity. A key challenge is to enable harvesting with low-cost hardware and mechanical systems, such as soft grippers which present additional challenges compared to their rigid counterparts. We devise an Imitation Learning model pipeline utilizing Vector Quantization to learn quantized embeddings directly from visual inputs. We test this approach in a realistic environment designed based on recordings of human experts harvesting real mushrooms. Our models can control a cartesian robot with a soft, pneumatically actuated gripper to successfully replicate the mushroom outrooting sequence. We achieve 100% success in picking mushrooms among distractors with less than 20 min of data collection comprising a single expert demonstration and auxiliary, non-expert, trajectories. The entire model pipeline requires less than 40 min of training on a single A4000 GPU and approx. 20 ms for inference on a standard laptop GPU. [ABSTRACT FROM AUTHOR]

In robotics, the ability of quadruped robots to perform tasks in industrial, mining, and disaster environments has already been demonstrated. To ensure the safe execution of tasks by the robot, meticulous planning of its foot placements and precise leg control are crucial. Traditional motion planning and control methods for quadruped robots often rely on complex models of both the robot itself and its surrounding environment. Establishing these models can be challenging due to their nonlinear nature, often entailing significant computational resources. However, a more simplified approach exists that focuses on the kinematic model of the robot's floating base for motion planning. This streamlined method is easier to implement but also adaptable to simpler hardware configurations. Moreover, integrating impedance control into the leg movements proves advantageous, particularly when traversing uneven terrain. This article presents a novel approach in which a quadruped robot employs impedance control for each leg. It utilizes sixth-degree Bézier curves to generate reference trajectories derived from leg velocities within a planar kinematic model for body control. This scheme effectively guides the robot along predefined paths. The proposed control strategy is implemented using the Robot Operating System (ROS) and is validated through simulations and physical experiments on the Go1 robot. The results of these tests demonstrate the effectiveness of the control strategy, enabling the robot to track reference trajectories while showing stable walking and trotting gaits. [ABSTRACT FROM AUTHOR]

This research focuses on developing an artificial vision system for a flexible delta robot manipulator and integrating it with machine-to-machine (M2M) communication to optimize real-time device interaction. This integration aims to increase the speed of the robotic system and improve its overall performance. The proposed combination of an artificial vision system with M2M communication can detect and recognize targets with high accuracy in real time within the limited space considered for positioning, further localization, and carrying out manufacturing processes such as assembly or sorting of parts. In this study, RGB images are used as input data for the MASK-R-CNN algorithm, and the results are processed according to the features of the delta robot arm prototype. The data obtained from MASK-R-CNN are adapted for use in the delta robot control system, considering its unique characteristics and positioning requirements. M2M technology enables the robot arm to react quickly to changes, such as moving objects or changes in their position, which is crucial for sorting and packing tasks. The system was tested under near real-world conditions to evaluate its performance and reliability. [ABSTRACT FROM AUTHOR]

This paper presents a model-free adaptive sliding mode control for the Delta robot with a novel reaching law for achieving a less conservative sign-function gain and protecting the Delta robot against overloads. A desired closed-loop system with asymptotic stability based on the Lyapunov theorem is proposed to derive the control law. The proposed control system can overcome uncertainties without prior knowledge of the bounding functions. The reaching time is adjusted adaptively to achieve a smooth convergence to the sliding surface, resulting in chattering attenuation. The gradient descent algorithm is utilized for the first time with a novel adaptation rule to estimate the Delta robot inverse Jacobian matrix. Instead of employing a numerical dynamic model, an analytical model is used for the proposed control law, stability analysis, and simulations. A simple, straightforward inverse-kinematics solution based on a geometrical approach is presented. Simulation results demonstrate a superior performance of the proposed reaching law through comparisons. • Proposed control system is based on an Eular–Lagrange dynamics of the Delta robot. • A simple straight forward estimation of the inverse Jacobian matrix is presented. • Proposed control system is model-free. • A less conservative gain is acquired for the switching function. • Control effort regulation and chattering attenuation is established. [ABSTRACT FROM AUTHOR]

Robotic manipulators are critical for industrial automation, boosting productivity, quality, and safety in various production applications. Key factors like the payload, speed, accuracy, and reach define robot performance. Optimizing these factors is crucial for future robot applications across diverse fields. While 6-Degrees-of-Freedom (DoF)-articulated robots are popular due to their diverse applications, this research proposes a novel 5-DoF robot design for industrial automation, featuring a combination of three prismatic and two revolute (2R) joints, and analyzes its workspace. The proposed techno-economically efficient design offers control over the robot manipulator to achieve any reachable position and orientation within its workspace, replacing traditional 6-DoF robots. The kinematic model integrates both parallel and serial manipulator principles, combining a Cartesian mechanism with rotational mechanisms. Simulations demonstrate the end effector's flexibility for tasks like welding, additive manufacturing, and material inspections, achieving the desired position and orientation. The research encompasses the design of linear and rotational actuators, kinematic modeling, Human–Machine Interface (HMI) development, and welding application integration. The developed robot demonstrates a superior performance and user-friendliness in welding. The experimental work validates the design's optimized joint trajectories, efficient power usage, singularity avoidance, easy access in application areas, and reduced costs due to fewer actuators. [ABSTRACT FROM AUTHOR]

With their unique point‐contact ability with the ground and exceptional adaptability to complex terrains, quadruped robots have become a focal point in the fields of automation and robotic engineering. Significant research progress has been made in aspects such as structural design, motion planning, and balance control of these robots. However, a primary challenge in current research lies in further enhancing the dynamic performance, environmental adaptability, and payload capacity. In this article, research achievements in key technical areas of quadruped robots, encompassing structural design, gait planning, traditional control strategies, intelligent control strategies, and autonomous movement, are comprehensively discussed. The focus of the article is on analyzing trends in intelligence and technological innovation within the aforementioned areas, aiming to provide robust theoretical support and forward‐looking technical guidance for quadruped robots research. Additionally, it offers valuable references for scholars engaged in this field. [ABSTRACT FROM AUTHOR]

An ARM Cortex simulation system for collaborative welding robots is presented in this paper. The components of the ARM Cortex SoC for embedded robot control, an OpenGL ES with image rendering, and a 3D geometry engine OpenCasCade for modeling are integrated for the purposes of simulating system self-controllability and cost effectiveness. This simulation of a collaborative welding robot achieved convenience while meeting the performance requirements; meanwhile, the auxiliary design was able to mark the trajectory of the robot's end effector and reveal the collaborative robot's inverse kinematic parameters, namely the position and Euler angle. An ARM Linux X11 Window environment that was set to create a 3D simulation rendering algorithm was built simultaneously. Then, the STEP model of the robot was loaded by using the OpenCasCade functionality. After that, the robot model and complex spline surface could be visualized by using the Qt QGLWidget. Finally, the correctness of the kinematic algorithm was verified by conducting simulations and analyzing the robot's kinematics through the simulation results, which could verify the expected design and provide a set of fundamental samples for the robot trajectory industry regarding welding applications. [ABSTRACT FROM AUTHOR]

Hand dysfunction is a common symptom in stroke patients. This paper presents a robotic device which assists the rehabilitation process in order to reduce the need of physical therapy, i.e., a 3UPS/S parallel robotic device is employed for repetitive robot-assisted rehabilitation. Euler angle representation was used to solve the robot's inverse kinematics. The robot's joint space and rotational workspace were determined for two scenarios. In the first scenario, the workspace was obtained considering the actuator's stroke limitations, while in the second scenario, the workspace was determined by adding a second condition, i.e., the range of motion of the spherical joints. Singularity analysis was performed using the geometric algebra approach. The robot was manufactured using additive manufacturing technology. The solution of the inverse kinematic problem was employed to control the robot. The robot can perform a full range of motion during wrist ulnar deviation and radial deviation motions, with the exception of limited wrist flexion and extension motions. The robot has singular configurations within its workspace. Although the spherical joints have roles in reducing the workspace, the primary causes are actuator selection, radii of the base and moving platforms, and the length of the central leg. These factors can be considered to improve the workspace. Singularity can be avoided by carefully selecting the rotation of the moving platform about the Z-axis and avoiding same leg lengths. [ABSTRACT FROM AUTHOR]

In the pursuit of enhancing the efficiency of the inverse kinematics of SCARA robots with four degrees of freedom (4-DoF), this research delves into an approach centered on the application of Artificial Neural Networks (ANNs) to optimise and, hence, solve the inverse kinematics problem. While analytical methods hold considerable importance, tackling the inverse kinematics for manipulator robots, like the SCARA robots, can pose challenges due to their inherent complexity and computational intensity. The main goal of the present paper is to develop efficient ANN-based solutions of the inverse kinematics that minimise the Mean Squared Error (MSE) in the 4-DoF SCARA robot inverse kinematics. Employing three distinct training algorithms – Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) – and three generated datasets, we fine-tune the ANN performance. Utilising diverse datasets featuring fixed step size, random step size, and sinusoidal trajectories allows for a comprehensive evaluation of the ANN adaptability to various operational scenarios during the training process. The utilisation of ANNs to optimise inverse kinematics offers notable advantages, such as heightened computational efficiency and precision, rendering them a compelling choice for real-time control and planning tasks. Through a comparative analysis of different training algorithms and datasets, our study yields valuable insights into the selection of the most effective training configurations for the optimisation of the inverse kinematics of the SCARA robot. Our research outcomes underscore the potential of ANNs as a viable means to enhance the efficiency of SCARA robot control systems, particularly when conventional analytical methods encounter limitations. [ABSTRACT FROM AUTHOR]

The technology used for the 3D printing of buildings from concrete is currently a very relevant and developing topic and appears to be especially advantageous in terms of sustainable production. An important aspect of the sustainability assessment is the energy efficiency of the printing robots. Printing robots consume a significant amount of energy when printing. It is important to analyse this energy thoroughly and to be able to predict it in order to optimise the movement and control of printing robots to reduce energy consumption. In that paper, we analyse in detail the energy consumption of printing robots, which has not yet been thoroughly investigated in the context of 3D printing building applications. We present a methodology to develop an energy consumption model for a printing robot, specifically developed and optimized for this technology. Our methodology incorporates an innovative approach to determine reduced-efficiency maps, allowing for the inclusion of difficult-to-measure drive efficiency parameters in the model. This results in a comprehensive model of the energy consumption of the printing robot, reflecting its operating characteristics in a real-world environment. An open control system of the printing robot is used for the measurement of energy quantities, and specially developed software tools are introduced. We also present the first direct comparison of the energy consumption of different printing robots when following a uniform printing trajectory. The comparison is made based on the presented methodology to obtain and compare actual energy data from workplaces with printing robots. The methodology combines measured data with energy simulations from ABB RobotStudio, enabling energy comparisons between industrially articulated robots and real printing robots, including the ABB IRB4600, the gantry printing robot, and the printing robot. The experiments clearly demonstrate that the kinematic structure of printing robots significantly affects their energy consumption in 3D printing concrete. Based on the conducted methodologies and analyses, we identify key aspects of energy consumption of printing robots in 3D Construction Printing or 3D Concrete Printing (3DCP) technology. In doing so, we bring a new perspective and provide a basis for further research and development in this previously understudied area. [ABSTRACT FROM AUTHOR]

The rapid progression of technology and robotics has brought about a transformative revolution in various fields. From industrial automation to healthcare and beyond, robots have become integral parts of our society, such as using them to move laparoscopic cameras. Eye-gaze-based control in robotics is a cutting-edge innovation, providing enhanced human-robot interaction and control. However, current research is in the underexplored area of gaze-based control for robotics. This paper presents a systematic bibliometric analysis review of controlling robots using gaze estimation. The aim is to provide a research map overview of the use of eye gaze to control robots by clustering application areas based on ISIC-UN and several data acquisition technologies. Over the past 10 years, the number of publications in this field has been relatively stable, averaging 21.5 papers per year, with minimal fluctuations in annual article counts (σ = 4.9). This differs from research on robotics, which grows by an average of 1376 papers per year. Research on using eye gaze for robot control in the last 10 years in the field of human health and social work has only resulted in 17 articles; transportation and storage resulted in 12 articles; professional, scientific, and technical activities resulted in eight articles; information and communication resulted in five articles; and education and art resulted in two articles. Data acquisition technology for eye gaze research, primarily using a commercial eye tracker. Thus, there is significant potential for future research through the utilization of gaze estimation in various fields, as mentioned above. [ABSTRACT FROM AUTHOR]

Robots can help people complete repetitive and high-risk tasks, such as industrial production, medical care, environmental monitoring, etc. The control system of robots is the key to their ability to complete tasks, and studying robot control systems is of great significance. This article used Convolutional Neural Network (CNN) and Robotic Process Automation (RPA) technologies to optimize and train the robot control system and constructed a robot control system. This article conducts perception and decision-making experiments and execution experiments in the experimental section. According to the experimental results, it can be concluded that the average image recognition accuracy of the robot control system in perception and decision-making experiments was 94.62%. The average decision accuracy was 87.5%, and the average time efficiency was 176 seconds. During the execution of the experiment, the deviation of the motion trajectory shall not exceed 5 cm, and the oscillation amplitude shall not exceed 6°; the distance from the obstacle shall not exceed 20 cm, and the movement speed shall not exceed 0.6 m/s; the operating time shall not exceed 25 hours, and the number of faults shall not exceed 0.2 times per hour, all within the normal range. The robot control system based on Deep Learning (DL) and RPA has broad application prospects and research value, which would bring new opportunities and challenges to the development and application of robot technology. [ABSTRACT FROM AUTHOR]

This paper proposes an admittance control scheme for robots equipped with joint-level position controllers involving deadtime. Its main feature is an elaborate discrete-time jerk limiter, which limits the third derivative of the position command sent to the controller. The jerk limiter is designed to suppress undesirable oscillation especially when the robot is in contact with a stiff environment. The controller is designed as a differential inclusion involving normal cones in the continuous-time domain, and its discrete-time algorithm is derived by the implicit Euler discretization. The presented controller was validated with experiments using a collaborative robot UR3e of Universal Robots, which has a deadtime of 6 ms in the velocity-command mode. [ABSTRACT FROM AUTHOR]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The facial expressions of humanoid robots play a crucial role in human–computer information interactions. However, there is a lack of quantitative evaluation methods for the anthropomorphism of robot facial expressions. In this study, we designed and manufactured a humanoid robot head that was capable of successfully realizing six basic facial expressions. The driving force behind the mechanism was efficiently transmitted to the silicone skin through a rigid linkage drive and snap button connection, which improves both the driving efficiency and the lifespan of the silicone skin. We used human facial expressions as a basis for simulating and acquiring the movement parameters. Subsequently, we designed a control system for the humanoid robot head in order to achieve these facial expressions. Moreover, we used a flexible vertical graphene sensor to measure strain on both the human face and the silicone skin of the humanoid robot head. We then proposed a method to evaluate the anthropomorphic degree of the robot's facial expressions by using the difference rate of strain. The feasibility of this method was confirmed through experiments in facial expression recognition. The evaluation results indicated a high degree of anthropomorphism for the six basic facial expressions which were achieved by the humanoid robot head. Moreover, this study also investigates factors affecting the reproduction of expressions. Finally, the impulse was calculated based on the strain curves of the energy consumption of the humanoid robot head to complete different facial expressions. This offers a reference for fellow researchers when designing humanoid robot heads, based on energy consumption ratios. To conclude, this paper offers data references for optimizing the mechanisms and selecting the drive components of the humanoid robot head. This was realized by considering the anthropomorphic degree and energy consumption of each part. Additionally, a new method for evaluating robot facial expressions is proposed. [ABSTRACT FROM AUTHOR]

The Delta robot is an over-actuated parallel robot with highly nonlinear kinematics and dynamics. Designing the control for a Delta robot to carry out various operations is a challenging task. Various advanced control algorithms, such as adaptive control, sliding mode control, and model predictive control, have been investigated for trajectory tracking of the Delta robot. However, these control algorithms require a reliable input–output model of the Delta robot. To address this issue, we have created a control-affine neural network model of the Delta robot with stepper motors. This is a completely data-driven model intended for control design consideration and is not derivable from Newton's law or Lagrange's equation. The neural networks are trained with randomly sampled data in a sufficiently large workspace. The sliding mode control for trajectory tracking is then designed with the help of the neural network model. Extensive numerical results are obtained to show that the neural network model together with the sliding mode control exhibits outstanding performance, achieving a trajectory tracking error below 5 cm on average for the Delta robot. Future work will include experimental validation of the proposed neural network input–output model for control design for the Delta robot. Furthermore, transfer learnings can be conducted to further refine the neural network input–output model and the sliding mode control when new experimental data become available. [ABSTRACT FROM AUTHOR]

This study explores the intricacies of employing two cooperative robotic manipulators for milling operations. Path planning, tool feed rate regulation, and cutting force management constitute essential elements in the milling process executed by these two collaborative robots. The two cooperative robots' milling process requires the coordination of path planning, cutting force management, and tool feed rate regulation. The path planning for the robotic system has been meticulously designed and seamlessly integrated with milling process expertise, while also adhering to the necessary technical specifications. This has been subsequently implemented on two cooperative robots featuring 3 Degrees of Freedom (3-DOF), utilizing the Staubli TX-90 robot model as a foundation. The milling of a typical workpiece by this robotic system is exemplified as a constrained optimization process. Regarding cooperative robot control, a method based on the Multiple Adaptive Neuro-Fuzzy Inference System (MANFIS) has been embraced. In order to validate the effectiveness of the presented approach, a comprehensive simulation has been conducted by using Matlab/SimMechanics software, and the results from this simulation have unequivocally substantiated the efficacy of the method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive robust control scheme for accuracy enhancement of eye-to-hand photogrammetry-based industrial robots subject to uncertainties. The proposed method uses two control loops: internal and external loops. The former is the dynamic controller designed for controlling the robot's joints. The external loop is the kinematic controller to minimize the error of the end-effector detected by the photogrammetry sensor. An adaptive integral terminal fractional-order super-twisting algorithm (AITFOSTA) is developed and employed for both control loops. AITFOSTA is an integral sliding-mode controller (ISMC) whose nominal control law is terminal. Its switching part is replaced with a fractional-order super-twisting algorithm (FOSTA), reducing the chattering to a great extent while rejecting the uncertainties. Additionally, an adaptive uncertainty and disturbance estimator based on radial basis function neural networks (RBFNNs) is designed and employed to reduce the uncertainty bounds, contributing to further chattering reduction. The stability analysis of the proposed controller is also presented. Simulation and experimental results show the superiority of the proposed method over other well-known approaches by reaching an unprecedented tracking accuracy, i.e. 0.06 mm and 0.18 ∘ for position and orientation, respectively. [ABSTRACT FROM AUTHOR]

Colorectal cancer as a major disease that poses a serious threat to human health continues to rise in incidence. And the timely colon examinations are crucial for the prevention, diagnosis, and treatment of this disease. Clinically, gastroscopy is used as a universal means of examination, prevention and diagnosis of this disease, but this detection method is not patient-friendly and can easily cause damage to the intestinal mucosa. Soft robots as an emerging technology offer a promising approach to examining, diagnosing, and treating intestinal diseases due to their high flexibility and patient-friendly interaction. However, existing research on intestinal soft robots mainly focuses on controlled movement and observation within the colon or colon-like environments, lacking additional functionalities such as sample collection from the intestine. Here, we designed and developed an earthworm-like soft robot specifically for colon sampling. It consists of a robot body with an earthworm-like structure for movement in the narrow and soft pipe-environments, and a sampling part with a flexible arm structure resembling an elephant trunk for bidirectional bending sampling. This soft robot is capable of flexible movement and sample collection within an colon-like environment. By successfully demonstrating the feasibility of utilizing soft robots for colon sampling, this work introduces a novel method for nondestructive inspection and sampling in the colon. It represents a significant advancement in the field of medical robotics, offering a potential solution for more efficient and accurate examination and diagnosis of intestinal diseases, specifically for colorectal cancer. [ABSTRACT FROM AUTHOR]

To effectively control a robot's motion, it is common to employ a simplified model that approximates the robot's dynamics. Nevertheless, discrepancies between the actual mechanical properties of the robot and the simplified model can result in motion failures. To address this issue, this study introduces a pneumatic-driven bipedal musculoskeletal robot designed to closely match the mechanical characteristics of a simplified spring-loaded inverted pendulum (SLIP) model. The SLIP model is widely utilized in robotics due to its passive stability and dynamic properties resembling human walking patterns. A musculoskeletal bipedal robot was designed and manufactured to concentrate its center of mass within a compact body around the hip joint, featuring low leg inertia in accordance with SLIP model principles. Furthermore, we validated that the robot exhibits similar dynamic characteristics to the SLIP model through a sequential jumping experiment and by comparing its performance to SLIP model simulation. [ABSTRACT FROM AUTHOR]

The present study focuses on the implementation of Deep Reinforcement Learning (Deep-RL) techniques for a parallel manipulator robot, specifically the Delta Robot, within a simulated setting. We introduced a simulation framework designed to guide the Delta Robot's end-effector to a designated spatial point accurately. Within this environment, the robotic agent undergoes a learning process grounded in trial and error. It garners positive rewards for successful predictions regarding the next action and faces negative repercussions for inaccuracies. Through this iterative learning mechanism, the robot refines its strategies, thereby establishing improved decision-making rules based on the ever-evolving environment states. Our investigation delved into three distinct Deep-RL algorithms: the Deep Q-Network Algorithm (DQN), the Double Deep Q-Network (DDQN), and the Trust Region Policy Optimization Algorithm (TRPO). All three methodologies were adept at addressing the challenge presented, and a comprehensive discussion of the findings is encapsulated in the subsequent sections of the paper. [ABSTRACT FROM AUTHOR]

Climbing manufacturing robots can create a revolutionary manufacturing paradigm for large and complex components, while the motion control of climbing manipulation-oriented robots (CMo-Rs) is still challenging considering anti-slippage problems. In this study, a CMo-R with full-scenery climbing capability and redundant load-bearing mobility is designed based on magnetic adsorption. A four-wheel kinematic model considering the slipping phenomenon is established. An adaptive kinematic control algorithm based on slip estimation using Lyapunov theory is designed for uncertain inclined planes. For comparison, the traditional PID-based algorithm without slip consideration is implemented as well. Numeric simulations are conducted to tackle the trajectory tracking problems for both circular and linear trajectories on the horizontal plane (HP), 50° inclined plane (50° IP), 60° inclined plane (60° IP), and vertical plane (VP). The results prove that our approach achieves better tracking accuracy. It demonstrated applicability in various climbing scenarios with uncertain inclined planes. The results of experiments also validate the feasibility, applicability, and stability of the proposed approach. [ABSTRACT FROM AUTHOR]

High-performance collision detection is the key to achieving automatic trajectory planning and secure motion control for industrial robots. However, most existing solutions reported thus far are difficult to guarantee computational accuracy and efficiency simultaneously when it comes to complex manufacturing environments. Therefore, this work presents a graphic-enabled collision detection method for robotic manufacturing applications in complex environments. With this method, a collision detection model that can be applied to arbitrarily complex polyhedrons is first established by using graphics depth peeling technology. A rapid collision detection algorithm utilizing a programmable graphics pipeline of GPU is developed. Then, the proposed collision detection model is extended to robotic manufacturing applications in complex environments. The effectiveness and practicality of the proposed method are verified through comparative simulation experiments, which have shown that the authors' method can greatly improve the computational efficiency, robustness, and memory consumption when compared with the existing methods, showing great application potential in collision-free trajectory planning and motion planning for robotic manufacturing applications in complex environments. [ABSTRACT FROM AUTHOR]

Visible light positioning or VLP has been identified as a promising technique for accurate indoor localization utilizing pre-existing lighting infrastructure. Robot navigation is one of the many potential applications of VLP. Recent literature shows a small number of works on robots being controlled by fusing location information acquired via VLP that uses a rolling shutter effect camera as a receiver with other sensor data. This paper, in contrast, reports on the experimental performance of a cartesian robot that was controlled solely by a VLP system using a cheap photodiode-based receiver rigidly attached to the robot's end-effector. The receiver's position was computed using an inverse-Lambertian function for ranging followed by multi-lateration. We developed two novel methods to leverage the VLP as an online navigation system to control the robot. The position acquired from the VLP was used by the algorithms to determine the direction the robot needed to move. The developed algorithms guided the end-effector to move from a starting point to target/destination point(s) in a discrete manner, determined by a pre-determined step size. Our experiments consisted of the robot autonomously repeating straight line-, square- and butterfly-shaped paths multiple times. The results show median errors of 27.16 mm and 26.05 mm and 90 percentile errors of 37.04 mm and 47.48 mm, respectively, for the two methods. [ABSTRACT FROM AUTHOR]

As a critical component of the joint gearbox, spur gear pairs play a crucial role in energy conversion, limiting the performance of a collaborative robot. Accurately assessing their instantaneous efficiency and torque fluctuation is essential for developing high-precision robot joint control models. This study proposes a computational model to predict the instantaneous efficiency and torque fluctuation of spur gears under typical operating conditions. The model incorporates a torque balance model, a load distribution model, and a friction model to reflect the relationship between gear meshing position and efficiency. The instantaneous efficiency and torque fluctuation of gear pairs were compared with the Coulomb friction model with an average friction coefficient and the elastohydrodynamic lubrication model with a time-varying friction coefficient. The effect of gear contact ratio on efficiency is analysed, while the instantaneous efficiency and torque fluctuation of gears are studied under varying operating conditions. The results indicate a maximum efficiency difference of 1.86 % between the two friction coefficient models. Under specific operating conditions, the instantaneous efficiency variation of the gear pair can reach 3.34 %, and the torque fluctuation can reach 5.19 Nm. Finally, this study demonstrates the effectiveness and accuracy of the proposed method through comparative analysis. [ABSTRACT FROM AUTHOR]

Industrial robots experience posture-dependent inertial variation and interference forces between joints, which in turn generates undesired vibrations due to the inherent elasticity of the reduction gears, making it difficult to control each axis independently. Conventionally, dynamic compensation is used to compensate the robot dynamics and decouple each axis, while state feedback control based on a two-inertia model is used for dealing with the vibrations caused by the elasticity of the reduction gears. However, the control design for this approach usually assumes a fixed moment of inertia, so its performance is compromised when large and fast changes in the robot posture occurs. In this paper, a posture-variant approach that takes into account the posture-dependent inertial variation in real time is proposed to achieve exact dynamic compensation and independent control of each axis regardless of the robot posture. The state equations of the posture-variant two-inertia system model for each robot axis are discretized using the finite-difference time-domain (FDTD) method and adapted on both the dynamic compensation and feedback control parts, allowing to easily redesign the whole control system considering the inertial variation at each control cycle. The effectiveness of the method is confirmed by experimental verification on a 6-axis industrial robot. [ABSTRACT FROM AUTHOR]

The complexity in the collaboration between humans and robots in smart manufacturing remains a significant challenge. This paper introduces an LLM-based manufacturing execution system enhancing Human-Robot Collaboration (HRC) in smart manufacturing. By leveraging Large Language Models (LLMs), the system provides a natural language interface for operators, integrates with Digital Twins for real-time data, and employs behavior-based control for robots. This integration facilitates intuitive interactions and rapid system programming, addressing communication complexities in HRC. The effectiveness of this approach is validated through two HRC assembly case studies, demonstrating significant improvements in collaboration and efficiency. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to discuss the experimental validation of a tele-operation system for remote center-of-motion tasks, such as laparoscopic surgery. This validation is based on the use of an extra sensor placed on the master manipulator. The tele-operation system is composed of a new hybrid haptic device (nHH) intended to be used as a master manipulator controlling a collaborative robot, used as a slave surgical robot. The resolution of the forward kinematic model (FKM) of the master device is performed experimentally thanks to the use of an extra sensor. The IMU, as the extra sensor, is installed on the serial part of the nHH device to measure the orientation and is enabled to solve the FKM of the parallel part of the nHH device. The use of an extra sensor reduces the calculation time, improves the accuracy of the KFM, and makes it suitable for real-time applications. The preliminary validation of the force feedback in the nHH workspace is validated. Experiments were conducted on the master–slave platform to validate the proposed approach. The results are promising, which proves that the nHH device presents a suitable performance for the desired task. [ABSTRACT FROM AUTHOR]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)