Handover Control for Human-Robot and Robot-Robot Collaboration

Modern scenarios in robotics involve human-robot collaboration or robot-robot cooperation in unstructured environments. In human-robot collaboration, the objective is to relieve humans from repetitive and wearing tasks. This is the case of a retail store, where the robot could help a clerk to refill a shelf or an elderly customer to pick an item from an uncomfortable location. In robot-robot cooperation, automated logistics scenarios, such as warehouses, distribution centers and supermarkets, often require repetitive and sequential pick and place tasks that can be executed more efficiently by exchanging objects between robots, provided that they are endowed with object handover ability. Use of a robot for passing objects is justified only if the handover operation is sufficiently intuitive for the involved humans, fluid and natural, with a speed comparable to that typical of a human-human object exchange. The approach proposed in this paper strongly relies on visual and haptic perception combined with suitable algorithms for controlling both robot motion, to allow the robot to adapt to human behavior, and grip force, to ensure a safe handover. The control strategy combines model-based reactive control methods with an event-driven state machine encoding a human-inspired behavior during a handover task, which involves both linear and torsional loads, without requiring explicit learning from human demonstration. Experiments in a supermarket-like environment with humans and robots communicating only through haptic cues demonstrate the relevance of force/tactile feedback in accomplishing handover operations in a collaborative task

Control of sliding velocity in robotic object pivoting based on tactile sensing

Control of robots manipulating objects using only the sense of touch is a challenge. In-hand motion of the manipulated object highly depends on the friction forces acting at the contact surfaces. Soft contacts allow torsional frictions as well as friction forces, therefore robots can perform more complex manipulation abilities, like object pivoting. Control of the pivoting sliding motion is very difficult especially without any visual feedback. The paper proposes a novel method to control the sliding velocity of the object by using a simple parallel gripper endowed with force/tactile sensors only. The strategy is based on a nonlinear observer that estimates the sliding velocity from force/torque measurements and a model of the sliding dynamics

Manipulation Planning and Control for Shelf Replenishment

Manipulation planning and control are relevant building blocks of a robotic system and their tight integration is a key factor to improve robot autonomy and allows robots to perform manipulation tasks of increasing complexity, such as those needed in the in-store logistics domain. Supermarkets contain a large variety of objects to be placed on the shelf layers with specific constraints, doing this with a robot is a challenge and requires a high dexterity. However, an integration of reactive grasping control and motion planning can allow robots to perform such tasks even with grippers with limited dexterity. The main contribution of the paper is a novel method for planning manipulation tasks to be executed using a reactive control layer that provides more control modalities, i.e., slipping avoidance and controlled sliding. Experiments with a new force/tactile sensor equipping the gripper of a mobile manipulator show that the approach allows the robot to successfully perform manipulation tasks unfeasible with a standard fixed grasp.Comment: 8 pages, 12 figures, accepted at RA

Serum Albumin Is Inversely Associated With Portal Vein Thrombosis in Cirrhosis

We analyzed whether serum albumin is independently associated with portal vein thrombosis (PVT) in liver cirrhosis (LC) and if a biologic plausibility exists. This study was divided into three parts. In part 1 (retrospective analysis), 753 consecutive patients with LC with ultrasound-detected PVT were retrospectively analyzed. In part 2, 112 patients with LC and 56 matched controls were entered in the cross-sectional study. In part 3, 5 patients with cirrhosis were entered in the in vivo study and 4 healthy subjects (HSs) were entered in the in vitro study to explore if albumin may affect platelet activation by modulating oxidative stress. In the 753 patients with LC, the prevalence of PVT was 16.7%; logistic analysis showed that only age (odds ratio [OR], 1.024; P = 0.012) and serum albumin (OR, -0.422; P = 0.0001) significantly predicted patients with PVT. Analyzing the 112 patients with LC and controls, soluble clusters of differentiation (CD)40-ligand (P = 0.0238), soluble Nox2-derived peptide (sNox2-dp; P < 0.0001), and urinary excretion of isoprostanes (P = 0.0078) were higher in patients with LC. In LC, albumin was correlated with sCD4OL (Spearman's rank correlation coefficient [r(s)], -0.33; P < 0.001), sNox2-dp (r(s), -0.57; P < 0.0001), and urinary excretion of isoprostanes (r(s), -0.48; P < 0.0001) levels. The in vivo study showed a progressive decrease in platelet aggregation, sNox2-dp, and urinary 8-iso prostaglandin F2 alpha-III formation 2 hours and 3 days after albumin infusion. Finally, platelet aggregation, sNox2-dp, and isoprostane formation significantly decreased in platelets from HSs incubated with scalar concentrations of albumin. Conclusion: Low serum albumin in LC is associated with PVT, suggesting that albumin could be a modulator of the hemostatic system through interference with mechanisms regulating platelet activation

Tactile Feedback Enabling In-Hand Pivoting and Internal Force Control for Dual-Arm Cooperative Object Carrying

The main purpose of this letter is to demonstrate that smart exploitation of force/tactile feedback can enable successful physical cooperation of two robot manipulators to handle a common object with a high degree of dexterity. The novelty of the letter is that dexterity is provided not only by the degrees of freedom of the robot arms but also by the grasp controller of the sensorized parallel grippers, which allow the robots to manipulate the object either with a tight grasp or with a one-degree-of-freedom rolling contact. The coordinated motion of the robots depends on both the desired motion of the carried object and the control of the internal forces during transportation and in-hand manipulation. The solution exploits only kinematic models of the robots and a dynamic model of the distributed soft contact, which includes both linear force and torsional moment

Dual-Arm In-Hand Manipulation with Parallel Grippers Using Tactile Feedback

Pick and place tasks of large and heavy objects are challenging for robots and typically require a multi-robot system. On one hand, the availability of multiple robots can increase dexterity, on the other hand, this dexterity might be strongly limited if only tight grasps are allowed. Grasping objects with unilateral constraints using simple parallel grippers is quite demanding from a control point of view. This paper relies on tactile feedback to perform the control of the grasp forces in such a way both tight grasp and controlled sliding are possible. This in-hand manipulation ability allows the robotic system to perform both translational and rotational motions of the objects in pick and place tasks with a simple inverse kinematics approach. This manipulation dexterity avoids re-grasping actions, which are often not compatible with task or environment constraints

Slipping Control Algorithms for Object Manipulation with Sensorized Parallel Grippers

Parallel jaw grippers have a limited dexterity, however they can still be used for in-hand manipulation tasks, such as pivoting or other controlled sliding motions of the grasped object. A rotational sliding maneuver is challenging since the grasped object can easily slip if the grip force is not properly adjusted to allow rotational sliding while avoiding translational sliding at the same time. This paper has a twofold aim. First, it intends to refine control algorithms to avoid both rotational and linear slippage, already presented by the authors, by proposing a novel sliding motion model that leads to a grip force as small as possible to avoid slippage, so as to enlarge the set of fragile and deformable objects that can be safely grasped with this approach. Second, the paper exploits the motion model to set up a new algorithm for controlled rotational sliding, thus enabling challenging in-hand manipulation actions. All control algorithms are sensor-based, exploiting a sensorized gripper equipped with a six-axis force/tactile sensor, which provides contact force and torque measurements as well as orientation of the object with respect to the gripper. A set of experiments are executed on a Kuka iiwa showing how the proposed control algorithms are effective to both avoid slippage and allow a controlled sliding motion

Detecting and Controlling Slip through Estimation and Control of the Sliding Velocity

Slipping detection and avoidance are key issues in dexterous robotic manipulation. The capability of robots to grasp and manipulate objects of common use can be greatly enhanced by endowing these robots with force/tactile sensors on their fingertips. Object slipping can be caused by both tangential and torsional loads when the grip force is too low. Contact force and moment measurements are required to counteract such loads and avoid slippage by controlling the grip force. In this paper, we use the SUNTouch force/tactile sensor, which provides the robotic control system with reliable measurements of both normal and tangential contact force components together with the torsional moment. By exploiting the limit surface concept and the LuGre friction model, we build a model of the object/fingertip planar sliding. This model is the basis of a nonlinear observer that estimates the sliding velocity and the friction state variable from the measured contact force and torsional moment. The slipping control system uses the estimated friction state to detect the slipping event and the estimated sliding velocity to control the grasp force. The control modality is twofold: the first one is aimed at avoiding object slip, while the second one allows the object to perform a controlled pivoting about the grasping axis. Experiments show that the robot is able to safely manipulate objects that require grasping forces in a large range, from 0.2 N to 10 N. This level of manipulation autonomy is attained by a suitably identified dynamic model that overcomes the limited generalization capability of existing learning-based approaches in the general roto-translational slip control

A fuzzy inference approach to control robot speed in human-robot shared workspaces

Nowadays, human-robot collaboration (HRC) is an important topic in the industrial sector. According to the current regulations, the robot no longer needs to be isolated in a work cell, but a collaborative workspace in which human operators and robots coexist can be acceptable. Human-robot interaction (HRI) is made possible by proper design of the robot and by using advanced sensors with high accuracy, which are adopted to monitor collaborative operations to ensure the human safety. Goal of this article is to implement a fuzzy inference system, based on the ISO/TS 15066, to correctly compute the minimum protective separation distance and adjust the robot speed by considering different possible situations, with the aim to avoid any collisions between operators and robots trying to minimize cycle time as well