The paper focuses on industrial interaction robotics tasks, investigating a control approach involving multiples learning levels for training the manipulator to execute a repetitive (partially) changeable task, accurately controlling the interaction. Based on compliance control, the proposed approach consists of two main control levels: 1) iterative friction learning compensation controller with reinforcement and 2) iterative force-tracking learning controller with reinforcement. The learning algorithms rely on the iterative learning and reinforcement learning procedures to automatize the controllers parameters tuning. The proposed procedure has been applied to an automotive industrial assembly task. A standard industrial UR 10 Universal Robot has been used, equipped by a compliant pneumatic gripper and a force/torque sensor at the robot end-effector.

In this paper we propose the use of a dynamic model in which the effects of temperature on friction are considered to develop a virtual force sensor for industrial robot manipulators. The estimation of the inertial parameters and of the friction model are explained. The effectiveness of the virtual force sensor has been proven in a polishing task. In fact, the interaction forces between the robot and the environment has been measured both with the virtual force sensor and a common load cell. Moreover, the advantages provided by considering the temperature dependency are highlighted.

In this paper, we present a new approach to manipulability maximisation for a dual-arm manipulator, which takes into account the manipulability of the overall task. This method tries to overcome the drawbacks given by traditional approaches, which optimise the manipulability of the local configuration of the manipulator, but do not take into account the rest of the task, even though it is known a priori. In this way, it is possible to improve the average manipulability index over the task. The method is applied to a dual-arm system, wherein the task is expressed in terms of relative poses between the end-effectors. For this reason, the kinematic of the system is solved by means of the relative Jacobian.

Nowadays industrial production is undergoing a continuous increase in flexibility requirements and production facilities have to cope with products diversities, high levels of uncertainties and frequent changes in production cycles. Robotic cells are an interesting solution for achieving the high level of flexibility essential for small batch production and customization, but they have to deal with an additional increase in complexity due to human interaction. An intuitive framework for fast application development and easy robot integration is then a key factor for achieving all the aforementioned specifications. In this paper, we propose the adoption of the IEC 61499 for workflow management and robotic task modelling. The event-driven Function Block model provides a natural way of describing the flow of operations by means of event connection between subsequent tasks and a convenient channel for human interaction. Low level robotic task execution has been implemented using the ROS framework and a specialized service interface communication channel which connects the closed loop control layer with the workflow management. A practical application scenario tested on a demanufacturing pilot plant has demonstrated the applicability of this approach with an increase in component reusability and a significant reduction in programming time.

In the presented paper an analytically force overshoots-free approach is described for the execution of robotics interaction tasks involving a compliant (of unknown geometrical and mechanical properties) environment. Based on the impedance control, the aim of the work is to perform force-tracking applications avoiding force overshoots that may result in task failures. The developed algorithm shapes the equivalent stiffness and damping of the closed-loop manipulator to regulate the interaction dynamics deforming the impedance control set-point. The force-tracking performance are obtained defining the control gains analytically based on the estimation of the interacting environment stiffness (performed using an Extended Kalman Filter). The method has been validated in a probing task, showing the avoidance of force overshoots and the achieved target dynamic performance.

The impedance shaping control with robot base dynamics compensation is presented in this paper. The method has been conceived to avoid force overshoots in applications where the coupled dynamics of the global system (compliant robot base - controlled robot - interacting compliant environment) affects the force tracking task. Force tracking performance are obtained tuning on-line both the position set-point and the stiffness and damping parameters, based on the force error, the estimated stiffness of the interacting environment (an Extended Kalman Filter is used) and the estimated robot base position (a Kalman Filter is used). The stability of the presented strategy has been studied through Lyapunov. To validate the performance of the control an assembly task is taken into account, considering the geometrical and mechanical properties of the (partially) unknown environment. Results are compared with constant stiffness and damping impedance controllers, which show force overshoots and instabilities.

The identification of the dynamic model of a robotic manipulator represents a fundamental step for designing high performance model-based controllers. Despite the huge number of works presented on this topic, the symbolic dynamic model reduction (i.e., the identification of the set of parameters observable through the measure of joint torques and positions) still remain a challenging task, characterized from tailored solutions, adapted from time to time to specific families of mechanisms. The work here presented, introduces an automatic and analytical reduction of the dynamic model, based on a multi-dimensional Fourier series decomposition of the dynamic equations. The procedure enables to obtain symbolically the base dynamic parameters (BP) starting from a given kinematic structure. The Fourier based model reduction can be applied indifferently both to open- and closed-chain kinematics. A simulated example shows the effectiveness of the proposed algorithm.

A case study of multi-robot platform calibration is reported in the domain of robot-assisted neurosurgery. The robot system is composed of a hybrid parallel kinematic machine and 2 KUKA LWR arms and is dedicated to awake surgery with head motion compensation. The target accuracy is sub-millimeter. Calibration errors are known to cumulate along the kinematic loops in the system, so a method of volumetric compensation of inaccuracies is applied. Configuration-dependent compensation homogeneous transforms populate a look-up table trained offline for a set of discrete subregions of the workspace. At runtime, the compensation is applied to the robot motion so to reach an end-to-end static accuracy distributed with a median 0.75 mm and below 1 mm for the 95% of tests, with a 1:36 reduction factor from the default calibration conditions.

In the domain of human-robot cooperation for robot-assisted manufacturing, the worker protection is always imperative using both intrinsically-safe and standard industrial manipulators. Collaborative workspaces very often involve cluttered layouts and flexible workflows that may require frequent relocation of operators and the concurrent access to resources in close-quarter cooperation. With the objective of maximizing the productivity of workcells, preserving the safety of the cooperation, an optimization of safeguarded workspaces is introduced under the approach of ISO/TS 15066 Speed and Separation Monitoring (SSM) modality. A trajectory-dependent dynamic SSM is considered for establishing the minimum safety area, changing at runtime, in order to avoid overconservative restrictions in nonfunctional volumes. The effects of robot dynamics and distributed robot control are discussed and evaluated.

The impedance shaping control is presented in this paper, providing an extension of standard impedance controller. The method has been conceived to avoid force overshoots in applications where there is the need to track a force reference. Force tracking performance are obtained tuning on-line both the position setpoint and the stiffness and damping parameters, based on the force error and on the estimated stiffness of the interacting environment (an Extended Kalman Filter is used). The stability of the presented strategy has been studied through Lyapunov. To validate the performance of the control an assembly task is taken into account, considering the geometrical and mechanical properties of the environment (partially) unknown. Results are compared with constant stiffness and damping impedance controllers, which show force overshoots and instabilities.

Robot dynamics is commonly modeled as a linear function of the robot kinematic state from a set of dynamic parametersintomotortorques. Baseparameters(i.e.thesetoftheoreticallydemonstratedlinearly-independent parameters) can be reduced to a subset of “essential” parameters by eliminating those that are negligible with respect to their contribution in motor torques. However, generic trajectories, if not properly defined, couple thecontributionofsuchessentialparametersintothemotortorques,actuallyreducingtheestimationaccuracy of the dynamics parameters. The work presented here introduces an index for evaluating correlation influence among essential parameters along an executed trajectory. Such index is then exploited for an optimal search of excitatory patterns consistent with the kinematical coupling constraints. The method is experimentally compared with the results achievable by one of the most popular IRs dynamic calibration method.

Notwithstanding the research on dynamic modelling of Industrial Robots (IRs hereafter) covers the last three decades, improvements are necessary to enable IRs adoption in technological tasks where high dynamics or interaction with environment is needed, e.g. deburring, milling, laser cutting etc. Indeed, this class of applications displays even more the necessity of high-accuracy tracking especially in workspace sub-regions, while common IR dynamic calibration methods often span the workspace at large (in term of positions and high velocities) resulting in an averagely fitting models. Open issues are therefore on the applicability/scalability of standard methods in workspace sub-regions and on the metrics used for the calibration performance evaluation. The paper proposes an algorithm designed to high-accuracy local dynamic identification, comparing it with the results achievable by a common IRs dynamic calibration method and by the same method scaled to a workspace sub-region. In addition, unlike from standard, the here reported experimental comparison is made by evaluating the torque prediction error for IRs robot moving along path programmed by standard/commercial IR motion planner and not along path belonging to the same template-class of trajectory used in identification phase.

The paper presents a control law for interaction tasks with environments of unknown geometrical and mechanical properties by manipulators mounted on compliant bases. Based on force-tracking impedance controls, the control strategy allows the execution of such class of tasks using the estimation of base position as a feedback in the control loop, requiring at the same time the on-line estimation of the environment stiffness. The properties of the control using non co-located sensors and the dynamic configuration of the coupled baserobot-environment system are studied. An Extended Kalman Filter is used for the estimation of the environment because of measurement uncertainties and errors in compound interaction model. The base is modelled as a second-order physical system with known parameters (offline identification before the task execution) and the base position is estimated from the measure of interaction forces. The grounding position estimation and the defined control law are validated in simulation and with experiments, especially dedicated to an insertion-assembly task. Control laws with and without the base compensation in the feedback loop are compared, verifying the effectiveness of the developed control law.

This paper presents LINarm, a device for at-home robotic upper-limb neurorehabilitation. Exploiting peculiar aspects of variable-stiffness actuators, it features functionalities widely addressed by devices specifically designed for assisted rehabilitation as controlled motion, force feedback and safety, together with the low-cost requirement for a widespread installation at patients' home.

This paper proposes a method for the automatic and simultaneous identification of the body-in-white assembly cell design and motion plan. The method solution is based on an iterative algorithm that looks for a global optimum by iteratively identifying the optimum of three sub-problems. These sub-problems concern system layout design and motion planning for single and multi-robot systems, while collision detection is addressed. The sub-problems are handled through ad-hoc developed Mixed Integer Programming (MIP) models. The proposed solution overcomes the limitations of the current design and motion plan approaches. In fact, the design of body-in-white assembly cell and the robot motion planning are two time-expensive and interconnected activities, up to now generally managed from different human operators. The resolution of these two activities as non-interrelated could lead to an increase of the engineer-to-order time and a reduction of the solution quality. Thus, a test bed is described in order to prove the applicability of the approach.

This paper presents a parallel and partially decoupled mechanism characterized by three translational and two rotational degrees of freedom. A set of parallel kinematic chains actuates five degrees of freedom of the mobile platform and constrains one of its rotations. Its kinematics combines advantages typical of parallel architectures, as high dynamics, with positive aspects of partially decoupled ones, in terms of mechanical design, control and motion planning, through a relatively simple direct kinematic formulation. The presented architecture constitutes the mechanical heart of a robotic prototype designed to actively support the patient's head in open-skull awake surgery.

Model-based control are affected by the accuracy of dynamic calibration. For industrial robots, identification techniques predominantly involve rigid body models linearized on a set of minimal lumped parameters that are estimated along excitatory trajectories made by suitable/optimal path. Although the physical meaning of the estimated lumped models is often lost (e.g. negative inertia values), these methodologies get remarkably results when well-conditioned trajectories are applied. Nonetheless, such trajectories have usually to span the workspace at large, resulting in an averagely fitting model. In many technological tasks, instead, the region of dynamics applications is limited, and generation of trajectories in such workspace sub-region results in different specialized models that should increase the predictability of local behavior. Besides this consideration, the paper presents a genetic-based selection of trajectories in constrained sub-region. The methodology places under optimization paths generated by a commercial industrial robot interpolator, and the genes (i.e. the degrees-of-freedom) of the evolutionary algorithms corresponds to a finite set of few via-points and velocities, just like standard motion programming of industrial robots. Remarkably, experiments demonstrate that this algorithm design feature allows a good matching of foreseen current and the actual measured in different task conditions.

A deformation-tracking impedance control strategy is discussed for applications where a manipulator interacts with environments of unknown geometrical and mechanical properties, especially with stiffness comparable to a controlled robot stiffness. Based on force-tracking impedance controls, the deformation-tracking strategy allows the control of a desired deformation of the target environment, requiring the on-line estimation of the environment stiffness. An Extended Kalman Filter is used for the estimation of the environment because of measurement uncertainties and errors in compound interaction model. The tasks presented involve full body spatial interactions with a time-varying environment stiffness. The Extended Kalman Filter and the deformation-tracking impedance control are validated in simulation and with experiments. In particular, a cooperative assembly task is also performed with a human operator acting as varying environment, i.e. unpredictably changing the handling arm stiffness.

The hand-eye problem consists in computing the poses between pairs of different coordinate frames fixed to the same rigid body from measurements of such poses as the body moves. Various procedures have been proposed over the past two decades for solving this problem in presence of noise, especially for a robot as the moving body. As a matter of fact, different formulations of the problem in terms of the well known AX=XB or AX=ZB equations implement different flavors of an error minimization procedure, either least-square or non-linear, on the basis of a common algebra. It is shown in this paper that better results in terms of accuracy can be obtained outside the conventional approach. Rather than fitting the calibration matrices out of a number of random poses, the presented method superimposes easily programmable robot poses in order to attain a set of constant manifolds, like points, circles and axes, among the different coordinate frames. Such manifolds are used for identifying the constant relationships between the coordinate frames that are in fact the poses under estimation. The proposed method presents the implementation of a simple robot motion routine for generating the manifolds. Standard mathematical tools are used for fitting the manifolds out of an actual realization of the procedure with tracked markers. The geometry of the proposed manifolds also reduces the propagation of the measurement noise that usually affects the conventional computation based on relative poses. Results are given in simulation and with a real setup in comparison with the most popular state-of-the-art algorithms.

This work presents a robotic platform for upper-limb rehabilitation robotics. It integrates devices for human multi-sensorial feedback for engaging and immersive therapies. Its modular software design and architecture allows the implementation of advanced control algorithms for effective and customized rehabilitations. A flexible communication infrastructure allows straightforward devices integration and system expandability.

Until now, the presence of fences is a technological barrier for the adoption of robots in small medium enterprises (SME). The work deals with the definition of an intrinsically safe algorithm to avoid collisions between an industrial manipulator and obstacles in its workspace (Standard ISO 10218-1). The suggested strategy aims to offer an industrial solution to the problem: an off-line analysis of the workspace is performed to have an exhaustive and intrinsically description of the static obstacles and a safe spatial grid of ¿pass-through points¿ is calculated; an on-line algorithm, based on an enhanced artificial potential field evaluates the most suitable points to avoid collisions against obstacles and perform a realtime replanning the path of the robot. A Matlab toolbox that elaborates STL CAD files has been developed to obtain a full description of the workcell, and the avoidance algorithm has been designed and implemented in a standard industrial controller. Various experimental results are reported by using a COMAU NS16 arm manipulator.

ITIA-CNR has been investigating the peculiarities and potentialities of machines based on a parallel kinematic structure (PKM). The outcome of these developments are represented by different physical prototypes but also by design methodologies and software tools, used also to support industrial companies willing to develop PK machines. The paper presents the application of those tools to the complete development of an industrial prototype for light deburring in shoes manufacturing with three degrees of freedom based on a PK structure plus a wrist with two rotational DOF.

This paper deals with the evaluation of human biofeedback response in virtual reality and in direct view. The experiments have been performed with a new paradigm for the evaluation of human biofeedback during the telemanipulation performance of a touch task. The controlled motion of one finger is monitored with the surface EMG, while a mechanical robotized hand finger follows the motion imposed by the human finger. The biofeedback is detected in a direct way, by the vision of the robotized finger action, and in an indirect way, with the support of three different types of interfaces. The neuromuscular activity presents different features and delays in the four cases: A measurement of the attention and participation in the man/machine interface is obtained, in a first series of experiments. The paradigm adopted in this research is the result of the integration of robotics and neurology.>

This paper analyses human control performance by means of a telemanipulation system and measurements of muscular activity during teleoperations. The task is performed with the aid of different types of interfaces. The control loop of a telerobotic manipulation system includes the human motion program. The control strategy for human motion is closely dependent on the type of interface available, that is, on the way the performance knowledge is presented to the operator. The analysis here proposed concerns the sensory and logical evaluation of this dependence in a telerobotic application, by means of EMG signal acquisition during teleoperations both in real and virtual environments. The experiments have been performed with a new paradigm for the evaluation of human feedback during the telemanipulation performance of a touch task. The paradigm adopted in this research is the result of the integration of robotics and neurology.>