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A haptic device (HD) is used to simulate a virtual object (VO) for its operator. Since most real objects can be simulated with a spring and damper, the VO is also implemented as a spring and damper in the discrete domain. On one side, the HD should have a small effective mass and friction so that its dynamics can be neglected in contrast to the dynamics of the VO. On the other side, even the small dynamics of the HD or the operator’s hand affect the stability and performance of the robot. So the dynamic model of the HD and the operator’s hand should be determined accurately. In this research, the mentioned dynamics have been identified on a HD using different artificial intelligence methods of ray optimization method, jaya optimization algorithm, crow search algorithm, Big-Bang Big-Crunch, adaptive dimensional search algorithm, and artificial bee colony optimization (ABCO). To this purpose, the theoretical stability boundary is presented as a function of the HD and the hand’s dynamic parameters, the stiffness and the damping coefficient of the VO, sampling time, and time delay. Simultaneously, with some experiments on the KUKA light weight robot IV (LWR IV), the boundary of experimental stability has been achieved in different situations. Then, using the mentioned methods the values of the HD and the hand’s dynamic parameters are obtained in such a way that the error between the theoretical and the experimental stability boundaries is minimal. Studies show that for predicting the HD parameters all mentioned methods have somehow the same and good accuracy, while the BBBC method has the minimum runtime. For predicting the HD and the operator’s hand dynamic parameters, the BBBC method has the minimum error and runtime.

Manipulation in contrast to grasping is a trajectorial task that needs to use dexterous hands. Improving the dexterity of robot hands increases the controller complexity and thus requires to use the concept of postural synergies. Inspired from postural synergies, this research proposes a new framework called kernelized synergies that focuses on the reusability of the same subspace for precision grasping and dexterous manipulation. In this work, the computed subspace of postural synergies is parameterized by kernelized movement primitives (KMPs) to preserve its grasping and manipulation characteristics and allows its reuse for new objects. The grasp stability of the proposed framework is assessed with the force closure quality index, as a cost function. For performance evaluation, the proposed framework is initially tested on two different simulated robot hand models using the Syngrasp toolbox and experimentally, four complex grasping and manipulation tasks are performed and reported. Results confirm the hand agnostic approach of the proposed framework and its generalization to distinct objects irrespective of their dimensions.

This article proposes a model predictive non-sliding manipulation (MPNSM) control approach to safely transport an object on a tray-like end-effector of a robotic manipulator. For the considered non-prehensile transportation task to succeed, both non-sliding manipulation and the robotic system constraints must always be satisfied. To tackle this problem, we devise a model predictive controller enforcing sticking contacts, i.e., preventing sliding between the object and the tray, and assuring that physical limits such as extreme joint positions, velocities, and input torques are never exceeded. The combined dynamic model of the physical system, comprising the manipulator and the object in contact, is derived in a compact form. The associated non-sliding manipulation constraint is formulated such that the parametrized contact forces belong to a conservatively approximated friction cone space. This constraint is enforced by the proposed MPNSM controller, formulated as an optimal control problem that optimizes the objective of tracking the desired trajectory while always satisfying both manipulation and robotic system constraints. We validate our approach by showing extensive dynamic simulations using a torque-controlled 7-degree-of-freedom (DoF) KUKA LBR IIWA robotic manipulator. Finally, demonstrative results from real experiments conducted on a 21-DoF humanoid robotic platform are shown.

The increasing interest in autonomous robots with a high number of degrees of freedom for industrial applications and service robotics demands control algorithms to handle multiple tasks as well as hard constraints efficiently. This article presents a general framework in which both kinematic (velocity- or acceleration-based) and dynamic (torque-based) control of redundant robots are handled in a unified fashion. The framework allows for the specification of redundancy resolution problems featuring a hierarchy of arbitrary (equality and inequality) constraints, arbitrary weighting of the control effort in the cost function and an additional input used to optimize possibly remaining redundancy. To solve such problems, a generalization of the saturation in the null space algorithm is introduced, which extends the original method according to the features required by our general control framework. Variants of the developed algorithm are presented, which ensure both efficient computation and optimality of the solution. Experiments on a KUKA LBRiiwa robotic arm, as well as simulations with a highly redundant mobile manipulator are reported.

Tissue elasticity is universally recognized as a diagnostic and prognostic biomarker for prostate cancer. As the first diagnostic test, the digital rectal examination is used since malignancy changes the prostate morphology and affects its mechanical properties. Currently, this examination is performed manually by the physician, with an unsatisfactory positive predictive value of 42%. A more objective and spatially selective technique is expected to provide a better prediction degree and understanding of the disease. To this aim, here we propose a miniaturized probe, based on optical fiber sensor technology, for mechanical characterization of the prostate with sub-millimeter resolution. Specifically, the optical system incorporates a customized Fiber Bragg Grating, judiciously integrated in a metallic cannula and moved by a robotic arm. The probe enables the local measurement of the force upon tissue indentation with a resolution of 0.97 mN. The system has been developed in such a way to be potentially used directly in vivo. Measurements performed on phantom tissues mimicking different stages of the prostatic carcinoma demonstrated the capability of our device to distinguish healthy from diseased zones of the prostate. The study on phantoms has been complemented with preliminary ex vivo experiments on real organs obtained from radical surgeries. Our findings lay the foundation for the development of advanced optical probes that, when integrated inside biopsy needle, are able to perform in vivo direct mechanical measurements with high sensitivity and spatial resolution, opening to new scenarios for early diagnosis and enhanced diagnostic accuracy of prostate cancer.

This article focuses on ethical issues raised by increasing levels of autonomy for surgical robots. These ethical issues are explored mainly by reference to state-ofart case studies and imminent advances in Minimally Invasive Surgery (MIS) and Microsurgery. In both area, surgicalworkspace is limited and the required precision is high. For this reason, increasing levels of robotic autonomy can make a significant difference there, and ethically justified control sharing between humans and robots must be introduced. In particular, from a responsibility and accountability perspective suitable policies for theMeaningfulHuman Control (MHC) of increasingly autonomous surgical robots are proposed. It is highlighted how MHC should be modulated in accordance with various levels of autonomy for MIS and Microsurgery robots. Moreover, finer MHC distinctions are introduced to deal with contextual conditions concerning e.g. soft or rigid anatomical environments.

This paper proposes a very effective method for data handling and preparation of the input 3D scans acquired from laser scanner mounted on the Unmanned Ground Vehicle (UGV). The main objectives are to improve and speed up the process of outliers removal for large-scale outdoor environments. This process is necessary in order to filter out the noise and to downsample the input data which will spare computational and memory resources for further processing steps, such as 3D mapping of rough terrain and unstructured environments. It includes the Voxel-subsampling and Fast Cluster Statistical Outlier Removal (FCSOR) subprocesses. The introduced FCSOR represents an extension on the Statistical Outliers Removal (SOR) method which is effective for both homogeneous and heterogeneous point clouds. This method is evaluated on real data obtained in outdoor environment.

This paper introduces a novel iterative 3D mapping framework for large scale natural terrain and complex environments. The framework is based on an Iterative-Closest-Point (ICP) algorithm and an iterative error minimization mechanism, allowing robust 3D map registration. This was accomplished by performing pairwise scan registrations without any prior known pose estimation information and taking into account the measurement uncertainties due to the 6D coordinates (translation and rotation) deviations in the acquired scans. Since the ICP algorithm does not guarantee to escape from local minima during the mapping, new algorithms for the local minima estimation and local minima escape process were proposed. The proposed framework is validated using large scale field test data sets. The experimental results were compared with those of standard, generalized and non-linear ICP registration methods and the performance evaluation is presented, showing improved performance of the proposed 3D mapping framework.

This paper presents a nonlinear least squares approach to deal with dual-hand robotic ball juggling. The task considers the repetitive batting (throwing and catching in a single collision) of a ball between two paddles/hands in a nonprehensile way. In detail, assuming to measure the trajectory of the ball, by solving a sequence of nonlinear minimization problems through a least squares method, the configuration of the paddles at the next impact is computed online to juggle the ball between the hands. Afterwards, an optimal trajectory for the paddles is planned in SE(3). The proposed approach is evaluated on a semi-humanoid robot with 21 degrees of freedom. Numerical tests show the smoothness of the planned trajectories and the precision of the proposed juggling algorithm.

The contact kinematics of a robotic hoop and a pole system is obtained by using the Montana’s equations, considering the case of contact without slipping. The resulting kinematic model is completely nonholonomic. After some mild assumptions, a set of Pfaffian constraints is established. Then, a dynamic model of the system is developed by employing the Lagrange– d’Alembert formulation. This dynamic model, which represents an underactuated mechanical system, is later used to design a controller which does not need velocity measurements. The proposed method, and its robustness against model uncertainties, is validated through numeric simulations.

In this paper, the problem of in-hand dexterous manipulation has been addressed on the base of postural synergies analysis. The computation of the synergies subspace able to represent grasp and manipulation tasks as trajectories connecting suitable configuration sets is based on the observation of the human hand behavior. Five subjects are required to reproduce the most natural grasping configuration belonging to the considered grasping taxonomy and the boundary configurations for those grasps that admit internal manipulation. The measurements on the human hand and the reconstruction of the human grasp configurations are obtained using a vision-based mapping method that assume the kinematics of the robotic hand, used for the experiments, as a simplified model of the human hand. The analysis to determine the most suitable set of synergies able to reproduce the selected grasps and the relative allowed internal manipulation has been carried out. The grasping and in-hand manipulation tasks have been reproduced by means of linear interpolation of the boundary configurations in the selected synergies subspace and the results have been experimentally tested on the UB Hand IV.

In this paper, the impact of the main packet loss models on the Unmanned Aerial Vehicle (UAV) formation control has been evaluated. A simulation environment has been built to introduce a centralized architecture, usually employed in mobile robotics to pursue global tasks, in the presence of loss models affecting the communication through robots hops. Simulation results show that the control performance in terms of tracking, collision avoidance and loss of connectivity are affected by the specific characteristics of packet loss. This suggests that the design of UAV formation control over wireless network has to be carried out strongly taking into account the effects of specific packet loss characteristics (due to wireless protocol, disturbance, traffic) on the performance quality.

Presents a controller for solving the tracking problem of flexible robot arm. Online computation of the desired trajectory for the link coordinates; Arbitrary assignment of desired trajectory for the joint coordinates; Use of a robust control technique to enhance dampening of the system.

Proposes a quaternion-based impedance controller for robot manipulator with a nondiagonal stiffness. Impedance equation; Energy functions; Stiffness analysis.

Euler parameters constitute a well‐known nonminimal singularity‐free representation of rigid body orientation. This paper is aimed at illustrating the role of Euler parameters in robot control; namely, the kinematic control, dynamic control and impedance control problems are surveyed in a unifying perspective, where robot's end‐effector orientation displacements are described in terms of Euler parameters. The advantages over the Euler angles typically used in the operational space framework are demonstrated.

This paper is aimed at presenting a study on the kinematics of the Tricept robot, which comprises a three-degree-of-freedom (dof) parallel structure having a radial link of variable length. The robot workspace is characterized and the inverse kinematics equation is obtained by using spherical coordinates. The inverse differential kinematics and statics are derived in terms of both an analytical and a geometric Jacobian, and a manipulability analysis along the various workspace directions is developed using the concept of force and velocity ellipsoids. A Jacobian-based Closed-Loop Direct Kinematics (CLDK) algorithm is presented to solve the direct kinematics problem along a given trajectory. Simulation results are illustrated for an industrial robot of the Tricept family.

The goal of this paper is to report the first experimental results of parallel force/position control schemes on an industrial robot manipulator with open control architecture. An inverse dynamics controller, a modified inverse dynamics controller, and a regulator are tested in a task involving interaction of the end effector with an elastically compliant surface having different values of contact stiffness. A performance comparison of the three schemes is developed in terms of the end-effector position tracking capability during unconstrained motion and force regulation capability during constrained motion.

In this paper, we present a tentatively comprehensive tutorial report of the most recent literature on kinematic control of redundant robot manipulators. Our goal is to lend some perspective to the most widely adopted on-line instantaneous control solutions, namely those based on the simple manipulator's Jacobian, those based on the local optimization of objective functions in the null space of the Jacobian, those based on the task space augmentation by additional constraint tasks (with task priority), and those based on the construction of inverse kinematic functions.

An adaptive force/position controller for robot manipulators in contact with a compliant surface is presented in this paper. The control law is designed in the task space and contains a nonlinear model-based term and a linear compensator action. This is obtained as a linear combination of the position error, the velocity error and the integral of the force error. The scheme is adaptive with respect to the dynamic parameters of the model of the robot manipulator. By using the classical Lyapunov method it is demonstrated that the proposed control law ensures tracking of the unconstrained components of the desired end-effector trajectory with regulation of the desired contact force along the constrained direction. Numerical case studies are developed for an industrial robot manipulator.

The trajectory-tracking control problem is considered for a one-link flexible arm described by a non-linear model. Two meaningful system outputs are chosen; namely, the joint angle and the angular position of a suitable point along the link. The common goal is to stiffen the behaviour of the flexible link with respect to the chosen output. Based on the input-output inversion algorithm, a state-feedback control law is designed that enables exact tracking of any smooth trajectory specified for the output. In the closed loop an unobservable dynamics naturally arises, related to the variables describing the arm's distributed flexibility. Joint-based design is shown to be always stable, whereas in the link-point design the closed-loop dynamics may become unstable depending on the location of the output along the link. Open- versus closed-loop strategies are developed and compared. Extensive simulation results are included.

This paper is aimed at presenting the dynamic model of a gear-driven rigid robot manipulator. The dynamic effects of the motion of the motors driving the joints through gears are analyzed. A complete model is derived using the Lagrange formulation in which the contributions of rotor inertias and rotor-link interactions are evidenced. The resulting equations of motion are shown to be linear in terms of a suitable set of dynamic parameters for the augmented links (links with motors). These are utilized for model derivation using the recursive Newton-Euler formulation. The explicit dynamic model for an elbow manipulator is developed.

The goal of this paper is to provide a critical review of the well-known resolved-acceleration technique for the tracking control problem of robot manipulators in the task space. Various control schemes are surveyed and classified according to the type of end-effector orientation error; namely, those based on Euler angles feedback, quaternion feedback, and angle/axis feedback. In addition to the assessed schemes in the literature, an alternative Euler angles feedback scheme is proposed which shows an advantage in terms of avoidance of representation singularities. An insight into the features of each scheme is given, with special concern to the stability properties of those schemes leading to nonlinear closed-loop dynamic equations. A comparison is carried out in terms of computational burden. Experiments on an industrial robot with open control architecture have been carried out, and the tracking performance of the resolved-acceleration control schemes in a case study involving the occurrence of a representation singularity is evaluated. The pros and cons of each scheme are evidenced in a final discussion focused on practical implementation issues.

Dynamic modelling of gear-driven rigid robot manipulators is discussed in this work. The dynamic effects of the motion of the motors driving the joints through gears are analyzed. A complete model is derived using the Lagrange formulation in which the contributions of rotor inertias and rotor-link interactions are evidenced. The resulting equations of motion are shown to be linear in terms of a suitable set of dynamic parameters for the augmented links (links with motors). These are utilized for model derivation using the recursive Newton-Euler formulation. The example of an elbow manipulator is developed.

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Biologically inspired robotic systems can find important applications in biomedical robotics, since studying and replicating human behaviour can provide new insights into motor recovery, functional substitution and human-robot interaction. The analysis of human hand motion is essential for collecting information about human hand movements useful for generalizing reaching and grasping actions on a robotic system. This paper focuses on the definition and extraction of quantitative indicators for describing optimal hand grasping postures and replicating them on an anthropomorphic robotic hand. A motion analysis has been carried out on six healthy human subjects performing a transverse volar grasp. The extracted indicators point to invariant grasping behaviours between the involved subjects, thus providing some constraints for identifying the optimal grasping configuration. Hence, an optimization algorithm based on the Nelder-Mead simplex method has been developed for determining the optimal grasp configuration of a robotic hand, grounded on the aforementioned constraints. It is characterized by a reduced computational cost. The grasp stability has been tested by introducing a quality index that satisfies the form-closure property. The grasping strategy has been validated by means of simulation tests and experimental trials on an arm-hand robotic system. The obtained results have shown the effectiveness of the extracted indicators to reduce the non-linear optimization problem complexity and lead to the synthesis of a grasping posture able to replicate the human behaviour while ensuring grasp stability. The experimental results have also highlighted the limitations of the adopted robotic platform (mainly due to the mechanical structure) to achieve the optimal grasp configuration.