A crucial aspect of physical human-robot collaboration (HRC) is to maintain a safe common workspace for human operator. However, close proximity between human-robot and unpredictability of human behavior raises serious challenges in terms of safety. This article proposes a risk analysis methodology for collaborative robotic applications, which is compatible with well-known standards in the area and relies on formal verification techniques to automate the traditional risk analysis methods. In particular, the methodology relies on temporal logic-based models to describe the different possible ways in which tasks can be carried out, and on fully automated formal verification techniques to explore the corresponding state space to detect and modify the hazardous situations at early stages of system design.

Fall detection and localization of human operators inside a workspace are major issues in ensuring a safe working environment. Recent research has shown that the perturbations of the radio-frequency (RF) signals commonly adopted for wireless communications can also be used as sensing tools for device-free human motion detection. Device-free RF-based human sensing applications range from tag-less body localization to detection and monitoring of human well-being (e-Health). In this paper, we propose a real-time system for human body motion sensing with special focus on joint body localization and fall detection. The proposed system continuously monitors and processes the RF signals emitted by industry-compliant radio devices operating in the 2.4 GHz ISM band and supporting machine-to-machine communication functions. Human-induced diffraction and multipath phenomena that affect RF signal propagation are leveraged for body localization while for fall detection a hidden Markov model is applied to discern different postures of the operator and to detect safety-relevant events by tracking the received signal strength indicator footprints. Fall detection performances are corroborated by extensive experimental measurements in different settings. In addition, we propose also a sensor fusion tool that is able to integrate the device-free RF-based sensing system within an industrial image sensors framework. Preliminary results, conducted during field trial measurements, confirm the effectiveness of the proposed approach in terms of localization accuracy, and sensitivity/specificity to correctly detect a fall event from preimpact postures.

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.

Safe human-machine interactions promote high flexibility in collaborative workspaces. Fall detection and localization of the operator are major issues in ensuring a safe working environment. However, many proposed solutions are not applicable for deployment in industrial environments due to their performance limitations in practical contexts. In this paper, we propose an integrated framework for both localization and fall detection of operators inside a shared workspace that employs radio-frequency (RF) signal analysis in real-time. Multipath and non-line-of-sight (NLOS) scattering that affect RF signal propagation can be leveraged for human sensing in complex workspaces: the proposed system continuously monitors the fluctuations of the RF field across the space by a dense network of WiFi compliant radio devices operating at 2.4GHz. To increase the accuracy of the localization system, a sensor fusion algorithm using Extended Kalman Filter techniques is employed. The proposed method may be used for integrating measurements from both RF nodes and an additional image-based system. For fall detection, a Hidden Markov Model is applied to discern different postures of the operator and to detect a fall event by tracking the fluctuations of the wireless signal quality. Fall detector performances are validated through experimental measurements. The preliminary results confirm the effectiveness of the proposed approach for different body configurations and pre-impact postures to correctly detect a fall event. Finally, some results about sensor fusion for improved operator localization are presented.

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.

Adaptable workflows in human-robot cooperation (HRC) require a flexible sharing of the same workspace with major impact on human-centered robot motion planning. The standard EN ISO 10218 is fostering the implementation of hybrid production systems characterized by a close relationship among human operators and robots in cooperative tasks. A primary contribution in workers protection is given by real time monitoring of the entire workspace, including tracking of operators trajectories and tentative estimation of motion intentions. Operators localization has the purpose of enabling the Speed and Separation Monitoring (SSM) safety mode, as in draft ISO/TS 15066, and adapting the robot motion to approaching users. The present work discloses some preliminary results about methods of “sensor-less” localization of operators in industrial HRC scenarios, based on wireless sensor networks techniques. The proposed system is composed of a network of small, embedded RF transceivers pervasively distributed in fixed positions inside the robotic cell layout in order to localize the operators, who carry neither wireless active devices (device-free) nor specific tracking sensors (sensor-less sensing). Users positions over time are estimated from the perturbation of the radio field, considering the effect of the concurrently moving robots. Finally, the sensors-robots system is functionally integrated into a safety architecture.

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 article describes a simulation model in which artificial evolution is used to design homogeneous control structures and adaptive communication protocols for a group of three autonomous simulated robots. The agents are required to cooperate in order to approach a light source while avoiding collisions. The robots are morphologically different: Two of them are equipped with infrared sensors, one with light sensors. Thus, the two morphologically identical robots should take care of obstacle avoidance; the other one should take care of phototaxis. Since all of the agents can emit and perceive sound, the group's coordination of actions is based on acoustic communication. The results of this study are a proof of concept: They show that dynamic artificial neural networks can be successfully synthesized by artificial evolution to design the neural mechanisms required to underpin the behavioral strategies and adaptive communication capabilities demanded by this task. Postevaluation analyses unveil operational aspects of the best evolved behavior. Our results suggest that the building blocks and the evolutionary machinery detailed in the article should be considered in future research work dealing with the design of homogeneous controllers for groups of heterogeneous cooperating and communicating robots.

Continuous Time Recurrent Neural Networks (CTRNN) display relevant properties of robustness to noise due to the stability of network dynamics. In this work we test the CTRNN model in a framework of NeuroEvolution (NE) for a real time control task, i.e. the balancing of an unstable nonlinear mechanical system. The task is used to review some theoretical results related to the analysis of stability of the network dynamics, as well as the primary results on the poles balancing task with CTRNNs, referring to similar task context in related works. The local stability of the neural controller dynamics does not undergo disruptive effects when evaluated in conditions different from the evolution ones. Thus, the controller is able to keep the equilibrium of the unstable system also in presence of noise significantly larger than the ratio experienced during the training phase.