Electrical Impedance Tomography (EIT) is a medical imaging technique that has been recently used to realize stretchable pressure sensors. In this method, voltage measurements are taken at electrodes placed at the boundary of the sensor and are used to reconstruct an image of the applied touch pressure points. The drawback with EIT-based sensors, however, is their low spatial resolution due to the ill-posed nature of the EIT reconstruction. In this paper, we show our performance evaluation of different EIT drive patterns, specifically strategies for electrode selection when performing current injection and voltage measurements. We compare voltage data with Signal-to-Noise Ratio (SNR) and Boundary Voltage Changes (BVC), and study image quality with Size Error (SE), Position Error (PE) and Ringing (RNG) parameters, in the case of one-point and two-point simultaneous contact locations. The study shows that, in order to improve the performance of EIT based sensors, the electrode selection strategies should dynamically change correspondingly to the location of the input stimuli. In fact, the selection of one drive pattern over another can improve the target size detection and position accuracy up to 0.04. and 0.18, respectively.
Despite the importance of softness, there is no evidence of wearable haptic systems able to deliver controllable softness cues. Here, we present the Wearable Fabric Yielding Display (W-FYD), a fabric-based display for multi-cue delivery that can be worn on user's finger and enables, for the first time, both active and passive softness exploration. It can also induce a sliding effect under the finger-pad. A given stiffness profile can be obtained by modulating the stretching state of the fabric through two motors. Furthermore, a lifting mechanism allows to put the fabric in contact with the user's finger-pad, to enable passive softness rendering. In this paper, we describe the architecture of W-FYD, and a thorough characterization of its stiffness workspace, frequency response and softness rendering capabilities. We also computed device Just Noticeable Difference in both active and passive exploratory conditions, for linear and non-linear stiffness rendering as well as for sliding direction perception. The effect of device weight was also considered. Furthermore, performance of participants and their subjective quantitative evaluation in detecting sliding direction and softness discrimination tasks are reported. Finally, applications of W-FYD in tactile augmented reality for open palpation are discussed, opening interesting perspectives in many fields of human-machine interaction.
Humans are able to intuitively exploit the shape of an object and environmental constraints to achieve stable grasps and perform dexterous manipulations. In doing that, a vast range of kinematic strategies can be observed. However, in this work we formulate the hypothesis that such ability can be described in terms of a synergistic behavior in the generation of hand postures, i.e., using a reduced set of commonly used kinematic patterns. This is in analogy with previous studies showing the presence of such behavior in different tasks, such as grasping. We investigated this hypothesis in experiments performed by six subjects, who were asked to grasp objects from a flat surface. We quantitatively characterized hand posture behavior from a kinematic perspective, i.e., the hand joint angles, in both pre-shaping and during the interaction with the environment. To determine the role of tactile feedback, we repeated the same experiments but with subjects wearing a rigid shell on the fingertips to reduce cutaneous afferent inputs. Results show the persistence of at least two postural synergies in all the considered experimental conditions and phases. Tactile impairment does not alter significantly the first two synergies, and contact with the environment generates a change only for higher order Principal Components. A good match also arises between the first synergy found in our analysis and the first synergy of grasping as quantified by previous work. The present study is motivated by the interest of learning from the human example, extracting lessons that can be applied in robot design and control. Thus, we conclude with a discussion on implications for robotics of our findings.
This paper presents an approach to achieve adaptive grasp of unknown objects whose position is only approximately known via point-cloud data. We exploit the adaptability of a soft robotic hand which can autonomously conform to the shape of a grasped object if properly approached. Once a grasp approach has been preliminarily planned based only on rough estimates of the object position, the hand is shaped to a pregrasp configuration. Before closing the hand, a sensor-based algorithm is applied that corrects the relative hand-object posture so as to enhance the probability that the object is uniformly approached by all fingers, thus avoiding undesired premature contacts. The algorithm minimizes the distance between the hand's fingerpads and the object by continuously controlling both the wrist pose and orientation and the hand closure. Experimental studies with a Kuka-LWR arm and a Pisa/IIT Softhand illustrate the benefit of the developed technique and the improvement in grasping performance with respect to open-loop execution of grasps planned on the basis of prior RGB-D cues only.
In humanoids and other redundant robots interacting with the environment, one can often choose between different configurations and control parameters to achieve a given task. A classic tool to describe specifications of the desired force/displacement behavior in such problems is the stiffness ellipsoid, whose geometry is affected by the choice of parameters in both joint control and redundancy resolution—namely, gains and angles. As is well known, impedance control techniques can regulate gains to realize any desired shape of the Cartesian stiffness ellipsoid at the end-effector, so that robot geometry selection could appear secondary. However, humans do not use this possibility: To control the stiffness of our arms, we predominantly use arm configurations. Why is that, and does it makes sense to do the same in robots? To understand this discrepancy, we provide a more complete analysis of the task-space force/deformation behavior of compliant redundant arms to illustrate why the arm geometry plays a dominant role in interaction capabilities of robots. We introduce the notion of allowable Cartesian force/displacement (“stiffness feasibility”) regions (SFR) for compliant robots with given torque boundaries. We show that different robot configurations modify such regions and explore the role of robot geometry in achieving an appropriate SFR for the task at hand. The novel concepts and definitions are first illustrated in simulations. Experimental results are then provided to verify the effectiveness of the proposed Cartesian force and stiffness control.
The endpoint stiffness of the human arm has been long recognized as a key component ensuring the quasi-static stability of the arm physical interactions with the external world. Similarly, the understanding of the joint stiffness behavior can provide complementary insights, e.g., on the underlying stiffness regulation principles across different joints including the nullspace stiffness profiles. Traditionally, the experimental modeling and estimation of the human arm joint stiffness is achieved by the transformation of the identified arm endpoint stiffness to the joint coordinates. Due to the underlying kinematic redundancy, the obtained joint stiffness matrix is rank-deficient which implies that the information in the joint stiffness matrix is incomplete. While in robotics applications this issue can be addressed by designing a desired nullspace stiffness behavior through appropriate projections, the use of a similar technique in the identification of human joint stiffness profile is meaningless. Hence, the first objective of this work is to address this issue by developing a novel technique to identify the complete and physiologically meaningful joint stiffness of human arm. Second, we present a model-based online estimation technique to estimate the seven-dimensional complete joint stiffness in various arm poses and activation levels of the two dominant arm muscles that correspond to the geometric and volume modifications of the joint stiffness profile, respectively.
In this work, we present WALK-MAN, a humanoid platform that has been developed to operate in realistic unstructured environment, and demonstrate new skills including powerful manipulation, robust balanced locomotion, high-strength capabilities, and physical sturdiness. To enable these capabilities, WALK-MAN design and actuation are based on the most recent advancements of series elastic actuator drives with unique performance features that differentiate the robot from previous state-of-the-art compliant actuated robots. Physical interaction performance is benefited by both active and passive adaptation, thanks to WALK-MAN actuation that combines customized high-performance modules with tuned torque/velocity curves and transmission elasticity for high-speed adaptation response and motion reactions to disturbances. WALK-MAN design also includes innovative design optimization features that consider the selection of kinematic structure and the placement of the actuators with the body structure to maximize the robot performance. Physical robustness is ensured with the integration of elastic transmission, proprioceptive sensing, and control. The WALK-MAN hardware was designed and built in 11 months, and the prototype of the robot was ready four months before DARPA Robotics Challenge (DRC) Finals. The motion generation of WALK-MAN is based on the unified motion-generation framework of whole-body locomotion and manipulation (termed loco-manipulation). WALK-MAN is able to execute simple loco-manipulation behaviors synthesized by combining different primitives defining the behavior of the center of gravity, the motion of the hands, legs, and head, the body attitude and posture, and the constrained body parts such as joint limits and contacts. The motion-generation framework including the specific motion modules and software architecture is discussed in detail. A rich perception system allows the robot to perceive and generate 3D representations of the environment as well as detect contacts and sense physical interaction force and moments. The operator station that pilots use to control the robot provides a rich pilot interface with different control modes and a number of teleoperated or semiautonomous command features. The capability of the robot and the performance of the individual motion control and perception modules were validated during the DRC in which the robot was able to demonstrate exceptional physical resilience and execute some of the tasks during the competition.
Decellularized human livers are considered the perfect extracellular matrix (ECM) surrogate because both three-dimensional architecture and biological features of the hepatic microenvironment are thought to be preserved. However, donor human livers are in chronically short supply, both for transplantation or as decellularized scaffolds, and will become even scarcer as life expectancy increases. It is hence of interest to determine the structural and biochemical properties of human hepatic ECM to derive design criteria for engineering biomimetic scaffolds. The intention of this work was to obtain quantitative design specifications for fabricating scaffolds for hepatic tissue engineering using human livers as a template. To this end, hepatic samples from five patients scheduled for hepatic resection were decellularized using a protocol shown to reproducibly conserve matrix composition and microstructure in porcine livers. The decellularization outcome was evaluated through histological and quantitative image analyses to evaluate cell removal, protein, and glycosaminoglycan content per unit area. Applying the same decellularization protocol to human liver samples obtained from five different patients yielded five different outcomes. Only one liver out of five was completely decellularized, while the other four showed different levels of remaining cells and matrix. Moreover, protein and glycosaminoglycan content per unit area after decellularization were also found to be patient- (or donor-) dependent. This donor-to-donor variability of human livers thus precludes their use as templates for engineering a generic "one-size fits all" ECM-mimic hepatic scaffold.
Soft robots (SRs) represent one of the most significant recent evolutions in robotics. Designed to embody safe and natural behaviors, they rely on compliant physical structures purposefully designed to embody desirable and sometimes variable impedance characteristics. This article discusses the problem of controlling SRs. We start by observing that most of the standard methods of robotic control—e.g., high-gain robust control, feedback linearization, backstepping, and active impedance control—effectively fight against or even completely cancel the physical dynamics of the system, replacing them with a desired model. This defeats the purpose of introducing physical compliance. After all, what is the point of building soft actuators if we then make them stiff by control? An alternative to such approaches can be conceived by observing humans, who can obtain good motion accuracy and repeatability while maintaining the intrinsic softness of their bodies. In this article, we show that an anticipative model of human motor control, using a feedforward action combined with low-gain feedback, can be used to achieve human-like behavior. We present an implementation of such an idea that uses iterative learning control. Finally, we present the experimental results of the application of such learned anticipative control to a physically compliant robot. The control application achieves the desired behavior much better than a classical feedback controller used for comparison.
Notes
This work is supported by European Commission grant H2020-ICT-645599 (“SOMA”: SOft Manipulation) and European Research Council Advanced grant 291166 (“SoftHands”).
Common haptic devices are designed to effectively provide kinaesthetic and/or cutaneous discriminative inputs to the users by modulating some physical parameters. However, in addition to this behavior, haptic stimuli were proven to convey also affective inputs to the brain. Nevertheless, such affective properties of touch are often disregarded in the design (and consequent validation) of haptic displays. In this paper we present some preliminary experimental evidences about how emotional feelings, intrinsically present while interacting with tactile displays, can be assessed. We propose a methodology based on a bidimensional model of elicited emotions evaluated by means of simple psychometric tests and statistical inference. Specifically, affective dimensions are expressed in terms of arousal and valence, which are quantified through two simple one-question psychometric tests, whereas statistical inference is based on rank-based non-parametric tests. In this work we consider two types of haptic systems: (i) a softness display, FYD-2, which was designed to convey purely discriminative softness haptic stimuli and (ii) a system designed to convey affective caress-like stimuli (by regulating the velocity and the strength of the “caress”) on the user forearm. Gender differences were also considered. In both devices, the affective component clearly depends on the stimuli and it is gender-related. Finally, we discuss how such outcomes might be profitably used to guide the design and the usage of haptic devices, in order to take into account also the emotional component, thus improving system performance.
This paper has developed a coordination control method for a dual-arm exoskeleton robot based on human impedance transfer skills, where the left (master) robot arm extracts the human limb impedance stiffness and position profiles, and then transfers the information to the right (slave) arm of the exoskeleton. A computationally efficient model of the arm endpoint stiffness behavior is developed and a co-contraction index is defined using muscular activities of a dominant antagonistic muscle pair. A reference command consisting of the stiffness and position profiles of the operator is computed and realized by one robot in real-time. Considering the dynamics uncertainties of the robotic exoskeleton, an adaptive-robust impedance controller in task space is proposed to drive the slave arm tracking the desired trajectories with convergent errors. To verify the robustness of the developed approach, a study of combining adaptive control and human impedance transfer control under the presence of unknown interactive forces is conducted. The experimental results of this paper suggest that the proposed control method enables the subjects to execute a coordination control task on a dual-arm exoskeleton robot by transferring the stiffness from the human arm to the slave robot arm, which turns out to be effective.
This paper presents a study of analysis of minimum-time trajectories for a differential drive robot equipped with a fixed and limited field-of-view camera, which must keep a given landmark in view during maneuvers. Previous works have considered the same physical problem and provided a complete analysis/synthesis for the problem of determining the shortest paths. The main difference in the two cost functions (length vs. time) lays on the rotation on the spot. Indeed, this maneuver has zero cost in terms of length and hence leads to a 2D shortest path synthesis. On the other hand, in case of minimum time, the synthesis depends also on the orientations of the vehicle. In other words, the not zero cost of the rotation on the spot maneuvers leads to a 3D minimum-time synthesis. Moreover, the shortest paths have been obtained by exploiting the geometric properties of the extremal arcs, i.e., straight lines, rotations on the spot, logarithmic spirals and involute of circles. Conversely, in terms of time, even if the extremal arcs of the minimum-time control problem are exactly the same, the geometric properties of these arcs change, leading to a completely different analysis and characterization of optimal paths. In this paper, after proving the existence of optimal trajectories and showing the extremal arcs of the problem at hand, we provide the control laws that steer the vehicle along these arcs and the time-cost along each of them. Moreover, this being a crucial step toward numerical implementation, optimal trajectories are proved to be characterized by a finite number of switching points between different extremal arcs, i.e., the concatenations of extremal arcs with infinitely many junction times are shown to violate the optimality conditions.
