Search

With the growing demand for robots in the industrial field, robot-related technologies with various functions have been introduced. One notable development is the implementation of robots that operate in collaboration with human workers to share tasks, without the need of any physical barriers such as safety fences. The realization of such collaborative operations in practice necessitates the assurance of safety if humans and robots collide. Thus, it is important to establish criteria for such collision scenarios to ensure robot safety and prevent injuries. Collision safety must be ensured in both pinching (quasi-static contact) and impact (transient contact) situations. To this end, we measured the force pain thresholds associated with impacts and evaluated the biomechanical limitations. This measurements were obtained through clinical trials involving physical collisions between human subjects and a device designed for generating impacts, and the force pain thresholds associated with transient collisions between humans and robots were analyzed. Specifically, the force pain threshold was measured at two different locations on the bodies of 37 adults aged 19-32 years, using two impactors with different shapes. The force pain threshold was compared with the results of other relevant studies. The results can help identify biomechanical limitations in a precise and reliable manner to ensure the safety of robots in collaborative applications. [ABSTRACT FROM AUTHOR]

With respect to alternative devices like traditional wheelchairs, handbikes can offer advantages from biomechanical and physiological perspectives, to several kinds of users. Assuring high mechanical efficiency and homogeneous force distributions along cycles, and being suitable for indoor and outdoor activities, these systems are used for rehabilitation, sports, and daily applications. From a technical perspective, their main characteristics can vary with the device final purpose and operational context. This review aims to provide an overall outline of handbikes in the literature from a general and comprehensive point of view, up until 2022. The analysis is performed (i) with a systematic approach, without a priori limitations on document type and content focus, and (ii) to identify the areas of interest for the scientific development of these systems. A systematic evaluation method for the identification and analysis of the documents was designed and implemented and the selection criteria, as well as the rationale for the procedure, are described. A specific taxonomy was defined and applied for the subsequent analysis, and each category is specifically evaluated and described, detailing the main outcomes of the literature analysis and relative discussion. Particular attention is paid to actuation strategies and propulsion efficiency. Finally, the main results of the work and future developments for handbikes are briefly synthesized. [ABSTRACT FROM AUTHOR]

Ensuring the safety of physical human–robot interaction (pHRI) is of utmost importance for industries and organisations seeking to incorporate robots into their workspaces. To address this concern, the ISO/TS 15066:2016 outlines hazard analysis and preventive measures for ensuring safety in Human–Robot Collaboration (HRC). To analyse human–robot contact, it is common practice to separately evaluate the "transient" and "quasi-static" contact phases. Accurately measuring transient forces during close human–robot collaboration requires so-called "biofidelic" sensors that closely mimic human tissue properties, featuring adequate bandwidth and balanced damping. The dynamics of physical human–robot interactions using biofidelic measuring devices are being explored in this research. In this paper, one biofidelic sensor is tested to analyse its dynamic characteristics and identify the main factors influencing its performance and its practical applications for testing. To this aim, sensor parameters, such as natural frequency and damping coefficient, are estimated by utilising a custom physical pendulum setup to impact the sensor. Mathematical models developed to characterise the sensor system and pendulum dynamics are also disclosed. [ABSTRACT FROM AUTHOR]

This study proposes an experimental method to estimate personalized inertial parameters of upper limbs during handcycling by using a planar dynamic model. The handle forces are expressed as a product of a matrix describing the kinematics terms and a vector of inertial parameters of arm and forearm. The parameters are estimated by measuring the handle forces during a suitable "passive test" and inverting the mentioned matrix. The data were acquired while an operator actuated the handle and the subject's muscles were relaxed. To validate the estimation procedure, it was applied to a custom-made artificial arm mechanism, and the results were compared with its known parameters. The method was then used to estimate the inertial parameters of 6 human subjects. The estimated parameters were used to compute the exchanged forces and compared with the measured ones obtaining an average error of 14% both for Fx and Fy. These errors are significantly smaller than those obtained using dynamic parameters extracted from the literature to compute the forces, which were 50% for Fx and 19% for Fy. An individual evaluation of inertial parameters better describes interaction forces during handcycling, especially for subjects whose body structures are different from the average population. [ABSTRACT FROM AUTHOR]

This paper deals with the implementation of a hybrid force/velocity controller for the automatic edge following of two-dimensional unknown planar contours performed by an industrial robot manipulator. In particular, the authors address the problem of compensating the joint friction effects that have to be taken into account in the controller design in order to achieve a reasonable performance with regards to normal force and tangential velocity errors. For that reason, two model-based friction-compensation methods are compared: a static method, based on a previously identified model, and an adaptive method, where joint friction parameters are recursively updated. By means of an extensive experimental activity, it is shown that, in spite of its simplicity and despite the friction effects changing in time during the robot operations, the devised adaptive procedure obtains a high performance in different operating conditions. Index Terms--Adaptation, contour tracking, friction, hybrid force/velocity control, industrial robots.

In this paper, we discuss, from an experimental point of view, the use of different control strategies for the trajectory tracking control of an industrial selective compliance assembly robot arm robot, which is one of the most employed manipulators in industrial environments, especially for assembly tasks. Specifically, we consider decentralized controllers such as proportional-integral-derivative-based and sliding-mode ones and model-based controllers such as the classical computed-torque one and a neural-network-based controller. A simple procedure for the estimation of the dynamic model of the manipulator is given. Experimental results provide a detailed framework about the cost/benefit ratio regarding the use of the different controllers, showing that the performance obtained with decentralized controllers may suffice in a large number of industrial applications, but in order to achieve low tracking errors also for high-speed trajectories, it might be convenient to adopt a neural-network-based control scheme, whose implementation is not particularly demanding. Index Terms--Decentralized control, neural networks, selective compliance assembly robot arm robot, trajectory tracking control.

Modeless industrial robot calibration plays an important role in the increasing employment of robots in industry. This approach allows to develop a procedure able to compensate the pose errors without complex parametric model. The paper presents a study aimed at comparing neural-kinematic (N-K) architectures for a modeless non-parametric robotic calibration. A multilayer perceptron feed-forward neural network, trained in a supervised manner with the back-propagation learning technique, is coupled in different modes with the ideal kinematic model of the robot. A comparative performance analysis of different neural-kinematic architectures was executed on a two degrees of freedom SCARA manipulator, for direct and inverse kinematics. Afterward the optimal schemes have been identified and further tested on a three degrees of freedom full SCARA robot and on a Stewart platform. The analysis on simulated data shows that the accuracy of the robot pose can be improved by an order of magnitude after compensation. [ABSTRACT FROM AUTHOR]