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Human–robot interaction (HRI) is a broad research topic, which is defined as understanding, designing, developing, and evaluating the robotic system to be used with or by humans. This paper presents a survey on the control, safety, and perspectives for HRI systems. The first part of this paper reviews the variable admittance (VA) control for human–robot co-manipulation tasks, where the virtual damping, inertia, or both are adjusted. An overview of the published research for the VA control approaches, their methods, the accomplished collaborative co-manipulation tasks and applications, and the criteria for evaluating them are presented and compared. Then, the performance of various VA controllers is compared and investigated. In the second part, the safety of HRI systems is discussed. The various methods for detection of human–robot collisions (model-based and data-based) are investigated and compared. Furthermore, the criteria, the main aspects, and the requirements for the determination of the collision and their thresholds are discussed. The performance measure and the effectiveness of each method are analyzed and compared. The third and final part of the paper discusses the perspectives, necessity, influences, and expectations of the HRI for future robotic systems.

In this paper, a multilayer feedforward neural network-based approach is proposed for human–robot collision detection taking safety standards into consideration. One multi-output neural network is designed and trained using data from the coupled dynamics of the manipulator with and without external contacts to detect unwanted collisions and to identify the collided link using only the intrinsic joint position and torque sensors of the manipulator. The proposed method is applied to the collaborative robots, which will be very popular in the near future, and is implemented and evaluated in 3D space motion taking into account the effect of the gravity. KUKA LWR manipulator is an example of the collaborative robots, and it is used for doing the experiments. The experimental results prove that the developed system is considerably efficient and very fast in detecting the collisions in the safe region and identifying the collided link along the entire workspace of the three-joint motion of the manipulator. Separate/uncoupled neural networks, one for each joint, are also designed and trained using the same data, and their performance is compared with the coupled one.

While in most industries, most processes are automated and human workers have either been replaced by robots or work alongside them, fewer changes have occurred in industries that use limp materials, like fabrics, clothes, and garments, than might be expected with today’s technological evolution. Integration of robots in these industries is a relatively demanding and challenging task, mostly because of the natural and mechanical properties of limp materials. In this review, information on sensors that have been used in fabric-handling applications is gathered, analyzed, and organized based on criteria such as their working principle and the task they are designed to support. Categorization and related works are presented in tables and figures so someone who is interested in developing automated fabric-handling applications can easily get useful information and ideas, at least regarding the necessary sensors for the most common handling tasks. Finally, we hope this work will inspire researchers to design new sensor concepts that could promote automation in the industry and boost the robotization of domestic chores involving with flexible materials.

In this paper, a multilayer feedforward neural network (NN) is designed and trained, for human–robot collisions detection, using only the intrinsic joint position sensors of a manipulator. The topology of one NN is designed considering the coupled dynamics of the robot and trained, with and without external contacts, by Levenberg–Marquardt algorithm to detect unwanted collisions of the human operator with the manipulator and the link that is collided. The proposed approach could be applied to any industrial robot, where only the joint position signals are available. The designed NN is compared quantitatively and qualitatively with an NN, where both the intrinsic joint position and the torque sensors of the manipulator are used. The proposed method is evaluated experimentally with the KUKA LWR manipulator, which is considered as an example of the collaborative robots, using two of its joints in a planar horizontal motion. The results illustrate that the developed system is efficient and fast to detect the collisions and identify the collided link.

Significant advancements have been made towards the automation of tasks involving highly deformable object manipulation. However, due to its complexity, predicting the behaviour of such objects, tasks requiring precision and flexibility remain far from full automation. In this paper, a method for the generation of a digital twin is proposed to serve as a solution for robotized cloth manipulation tasks. To estimate mechanical parameters and reconstruct a cloth’s digital twin, our approach integrates image processing and a genetic algorithm process. Specifically, a spatio-temporal graph of the cloth is built using Scale-Invariant Feature Transform ( SIFT ), and a genetic algorithm is used to optimize the parameters of a mass-spring-damper (MSD) model. Contrary to prior methods, which were relying on predefined cloth models or computationally expensive simulations (e.g., FEM), the proposed framework enables lightweight, data-driven parameter tuning from a single monocular RGB video showing the deformations of the cloth during a set of predefined movements by a pair of robotic arms. The generated digital twin is then used to calculate the optimal trajectory for the robotic arms in typical cloth manipulation tasks and is subsequently evaluated in real-world experiments. Tasks such as laying a cloth on a table and folding it in half are used to validate the method’s accuracy and applicability in both simulated and physical environments. The proposed method has potential applications in industrial textile handling, domestic service robotics, and elderly care, particularly in settings where low-cost, sensor-efficient automation is desirable.

This paper proposes an approach for variable admittance control in human–robot collaboration depending on the online training of neural network. The virtual inertia is an important factor for the system stability, and its tuning is investigated in improving the human–robot cooperation. The design of the variable virtual inertia controller is analyzed, and the choice of the neural network type and their inputs and output is justified. The error backpropagation analysis of the designed system is elaborated since the end-effector velocity error depends indirectly on the multilayer feedforward neural network output. The proposed controller performance is experimentally investigated, and its generalization ability is evaluated by conducting cooperative tasks with the help of multiple subjects using the KUKA LWR manipulator under different conditions and tasks than the ones used for the neural network training. Finally, a comparative study is presented between the proposed method and previous published ones.

In this manuscript, a recurrent neural network is proposed for variable admittance control in human–robot cooperation tasks. The virtual damping and the virtual inertia of the designed robot’s admittance controller are adjusted online and simultaneously. A Jordan recurrent neural network is designed and trained for this purpose. The network is indirectly trained using the real-time recurrent learning algorithm and based on the velocity error between the reference velocity of the minimum jerk trajectory model and the actual velocity of the robot. The performance of the proposed variable admittance controller is presented in terms of the human required effort, the task completion time, the achieved accuracy at the target, and the oscillations during the movement. Its generalization ability is evaluated experimentally by conducting cooperative tasks along numerous straight-line segments using the KUKA LWR robot and by ten subjects. Finally, a comparison with previous developed variable admittance controllers, where only the variable damping or only the virtual inertia is adjusted, is presented.

Purpose - The paper aims to propose an approach to intelligent evaluation of the tensile test. A robotized system is used that performs the fabrics tensile test and estimates the extensibility of the samples using a feed-forward neural network while trying to imitate the human expert estimation.Design methodology approach - The specifications of the tensile test are derived by an extensive observation of the respective experts' estimation performance. The fabric sample size and the experimental conditions are specified. Linguistic values of the term \"fabric extensibility\" are extracted through a knowledge acquisition process. The tensile test is performed by a robot manipulator with a simple gripper and the experimental measurements (force, strain) are fed online into a neural network. The network is trained according to the extensibility estimations of the experts. The trained network is tested in estimating unknown fabric's extensibility.Findings - The results demonstrate that the system is capable of estimating the extensibility of new fabrics.Originality value - This work can be integrated in the robotized sewing process with intelligent control where the fabric's extensibility in terms of linguistic values is necessary. The proposed system initiates a new approach, in which the fabric properties are expressed and used in a way that will facilitate the introduction of the artificial intelligence methods into the clothing industry.

A new approach for flexible automated handling of fabrics in the sewing process is described, which focuses to control the cloth tension applied by a robot. The proposed hierarchical robot control system includes a Fuzzy decision mechanism combined with a Neuro-controller. The expert's actions during the sewing process are investigated and this human behavior is interpreted in order to design the controller. The Fuzzy Logic decision mechanism utilizes only qualitative knowledge concerning the properties of the fabrics, in order to determine the desired tensional force and the location of the robot hand on the fabric. A Neural Network controller regulates the fabric tension to achieve the desired value by determining the robot end effector velocity. The simulation results demonstrate the efficiency of the system as well as the robustness of the controller performance since the effects of the noise are negligible. The system capabilities are more evident when the controller uses its previously acquired “experience”.