Model-based Manipulation of Deformable Linear Objects by Multivariate Dynamic Splines

In this paper, the modelling and the simulation of a Deformable Linear Object (DLO) manipulation are reported. The main motivation of this study is to define a strategy to enable a robotic manipulator to predict in real time the shape a DLO will achieve during the execution of a manipulation action. To accomplish this target in a reasonable time, according to the possibility of adopting this solution in an industrial manufacturing system, an approximate but physically consistent model of the DLO is adopted considering the predominant plasticity of the object to be manipulated, as in the case of electric cable manipulation. The DLO manipulation model is based on multivariate dynamic splines solved iteratively in real-time to interpolate the DLO shape during the manipulation sequence. The systems assumes to be able to detect the initial configuration of the DLO at each iteration of the algorithm by means of a proper vision system. Preliminary simulation results are presented to show the effectiveness of the method

Pointcloud-based Identification of Optimal Grasping Poses for Cloth-like Deformable Objects

In this paper, the problem of identifying optimal grasping poses for cloth-like deformable objects is addressed by means of a four-steps algorithm performing the processing of the data coming from a 3D camera. The first step segments the source pointcloud, while the second step implements a wrinkledness measure able to robustly detect graspable regions of a cloth. In the third step the identification of each individual wrinkle is accomplished by fitting a piecewise curve. Finally, in the fourth step, a target grasping pose for each detected wrinkle is estimated. Compared to deep learning approaches where the availability of a good quality dataset or trained model is necessary, our general algorithm can find employment in very different scenarios with minor parameters tweaking. Results showing the application of our method to the clothes bin picking task are presented

Validating DLO models from shape observation

In this paper, the problem of fitting the model of deformable linear objects from the observation of the shape under the effect of known external forces like gravity is taken into account. The model of the deformable linear object is based on dynamic splines, allowing to obtain a reliable prediction of the object behavior while preserving a suitable efficiency and simplicity of the model. The object shape is measured by means of a calibrated vision system, and a fitting between the observed shape and the theoretical model is defined for validation. Experiments are executed in different conditions, showing the reliability of the proposed spline-based model

A safe and energy efficient robotic system for industrial automatic tests on domestic appliances: Problem statement and proof of concept

In this paper, the design and the development of a robotic platform conceived to perform accelerated life tests on a newly manufactured domestic appliances is presented. The proposed system aims at improving the safety of human operators that share the workspace with the robotic platform which is a common scenario of test laboratories. A deep learning algorithm is used for the human detection and pose estimation, while the integration between a conventional motion planning algorithm with a fast 3D collision checker has been implemented as a global planner plugin for the ROS navigation stack. With the twofold objective of improving safety and saving energy in the battery-powered mobile manipulator used in this project, the problem of minimizing the overall kinetic energy is addressed through a properly designed task priority controller, in which the manipulator inertia matrix is used to weight the joint speeds while satisfying multiple robotic tasks according to a hierarchy designed to interact with the appliances while preserving the safety of the human operators. Simulations are carried out to evaluate the overall control architecture and preliminary results indicate the effectiveness of the developed system in the test laboratory floors

Combined joint-cartesian mapping for simultaneous shape and precision teleoperation of anthropomorphic robotic hands

There are many applications involving robotic hands in which teleoperation-based approaches are preferred to autonomous solutions. The main reason is that cognitive skills of human operators are desirable in some task scenarios, in order to overcome limitations of robotic hands abilities in dealing with unstructured environments and/or unpredetermined requirements. In particular, in this work we focus on the use of anthropomorphic grasping devices and, specifically, on their teleoperation based on movements of the human operator's hand (the master hand.) Indeed, the mapping of human hand configurations to an anthropomorphic robotic hand (the slave device) is still an open problem, because of the presence of dissimilar kinematics between master and slave that produce shape and/or Cartesian errors - as addressed within our study. In this work, we propose a novel algorithm that combines joint and Cartesian mappings in order to enhance the preservation of both finger shapes and fingertip positions during the teleoperation of the robotic hand. In particular, a transition between the joint and Cartesian mappings is realized on the basis of the distance between the fingertip of the master hands' thumb and the opposite fingers, in which the mapping of the thumb fingertip is specifically addressed. The result of the testing of the algorithm with a ROS-based simulator of a commercially available robotic hand is reported, showing the effectiveness of the proposed mapping

Experimental evaluation of synergy-based in-hand manipulation

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 themost 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 bymeans of linear interpolation of the boundary configurations in the selected synergies subspace and the results have been experimentally tested on the UB Hand IV

Robot Programming by Demonstration: Trajectory Learning Enhanced by sEMG-Based User Hand Stiffness Estimation

Trajectory learning is one of the key components of robot Programming by Demonstration approaches, which in many cases, especially in industrial practice, aim at defining complex manipulation patterns. In order to enhance these methods, which are generally based on a physical interaction between the user and the robot, guided along the desired path, an additional input channel is considered in this article. The hand stiffness, that the operator continuously modulates during the demonstration, is estimated from the forearm surface electromyography and translated into a request for a higher or lower accuracy level. Then, a constrained optimization problem is built (and solved) in the framework of smoothing B-splines to obtain a minimum curvature trajectory approximating, in this manner, the taught path within the precision imposed by the user. Experimental tests in different applicative scenarios, involving both position and orientation, prove the benefits of the proposed approach in terms of the intuitiveness of the programming procedure for the human operator and characteristics of the final motion

Self-Supervised Regression of sEMG Signals Combining Non-Negative Matrix Factorization With Deep Neural Networks for Robot Hand Multiple Grasping Motion Control

Advanced Human-In-The-Loop (HITL) control strategies for robot hands based on surface electromyography (sEMG) are among major research questions in robotics. Due to intrinsic complexity and inaccuracy of labeling procedures, unsupervised regression of sEMG signals has been employed in literature, however showing several limitations in realizing multiple grasping motion control. In this letter, we propose a novel Human-Robot interface (HRi) based on self-supervised regression of sEMG signals, combining Non-Negative Matrix Factorization (NMF) with Deep Neural Networks (DNN) in order to both avoid explicit labeling procedures and have powerful nonlinear fitting capabilities. Experiments involving 10 healthy subjects were carried out, consisting of an offline session for systematic evaluations and comparisons with traditional unsupervised approaches, and an online session for assessing real-time control of a wearable anthropomorphic robot hand. The offline results demonstrate that the proposed self-supervised regression approach overcame traditional unsupervised methods, even considering different robot hands with dissimilar kinematic structures. Furthermore, the subjects were able to successfully perform online control of multiple grasping motions of a real wearable robot hand, reporting for high reliability over repeated grasp-transportation-release tasks with different objects. Statistical support is provided along with experimental outcomes

Non-linear Finite-Time Tracking Control of Uncertain Robotic Manipulators Using Time-Varying Disturbance Observer-Based Sliding Mode Method

In this paper, a time-varying chattering-free disturbance observer-based position tracking control law of serial robotic manipulators is presented to track a reference signal in a finite time. The key idea is to employ a positive-increasing function associated with the control/observer objectives to improve the control performance. First, the model of an uncertain robotic manipulator is presented as the case study of the proposed strategy. Then, the time-varying form of the robotic manipulator model is obtained to provide finite-time boundedness using the first-order sliding mode method. Moreover, without any knowledge about the upper bounds of the uncertainties, a reduced-order observer is presented to estimate the uncertainties in a finite time. Subsequently, a disturbance observer-based finite-time position tracking control law is designed. The time-varying gains are provided to converge the position tracking error to a neighborhood of zero in a finite time. Finally, comparative simulations are presented to show the effectiveness of the proposed scheme compared to other existing strategies