Advanced grasping with the Pisa/IIT softHand

This chapter presents the hardware, software and overall strategy used by the team UNIPI-IIT-QB to participate to the Robotic Grasping and Manipulation Competition. It relies on the PISA/IIT SoftHand, which is underactuated soft robotic hand that can adapt to the grasped object shape and is compliant with the environment. It was used for the hand-in-hand and for the simulation tracks, where the team reached first and third places respectively

Dynamic grasping of objects with a high-speed parallel robot

Underactuated grippers aim to simplify the control strategies for performing stable grasps due to their inherent shape adaptability. While at the beginning, the main research area was focused on developing human-like robotic hands for disabled people, in the last years, a new eld of application appeared with the constant evolution of the industry: the implementation of a single underactuated gripper as a replacement of diverse dedicated fully-actuated grippers. However, two main issues are restraining its use: the stability of the grasp and the speed of performance. The rst is an active topic as all underactuated grippers need to ensure the stability of the grasped object through an adequate kinematic design, while, the latter is not widely treated as there weren't many application elds where high-speed was required and, at the end, the quasi-static analysis must be also ensured. For this reason, the present research work has been focused on the speed of the grasping. In the rst place, an introduction to underactuated hands is made, and is followed by two main stability criteria. Then, the development of a model for an underactuated nger that allows analyzing the complete grasping sequence at high-speed along with a collision model are presented. Following, a design-based analysis to simplify the model is performed, and the graspstate volume tool is introduced in order to inspect the impact of the design variables on the proposed criteria. In the last chapter, an optimization over the design space is performed and a design is chosen, crosschecked with ADAMS software and prototyped. Finally, an overview remarking the strengths and gaps in the research is presented in the form of conclusions, and closing them, future works that could be interesting to develop

Incrementality and Hierarchies in the Enrollment of Multiple Synergies for Grasp Planning

Postural hand synergies or eigenpostures are joint angle covariation patterns observed in common grasping tasks. A typical definition associates the geometry of synergy vectors and their hierarchy (relative statistical weight) with the principal component analysis of an experimental covariance matrix. In a reduced complexity representation, the accuracy of hand posture reconstruction is incrementally improved as the number of synergies is increased according to the hierarchy. In this work, we explore whether and how hierarchy and incrementality extend from posture description to grasp force distribution. To do so, we study the problem of optimizing grasps w.r.t. hand/object relative pose and force application, using hand models with an increasing number of synergies, ordered according to a widely used postural basis. The optimization is performed numerically, on a data set of simulated grasps of four objects with a 19-DoF anthropomorphic hand. Results show that the hand/object relative poses that minimize (possibly locally) the grasp optimality index remain roughly the same as more synergies are considered. This suggests that an incremental learning algorithm could be conceived, leveraging on the solution of lower dimensionality problems to progressively address more complex cases as more synergies are added. Second, we investigate whether the adopted hierarchy of postural synergies is indeed the best also for force distribution. Results show that this is not the case

Advancing the Underactuated Grasping Capabilities of Single Actuator Prosthetic Hands

The last decade has seen significant advancements in upper limb prosthetics, specifically in the myoelectric control and powered prosthetic hand fields, leading to more active and social lifestyles for the upper limb amputee community. Notwithstanding the improvements in complexity and control of myoelectric prosthetic hands, grasping still remains one of the greatest challenges in robotics. Upper-limb amputees continue to prefer more antiquated body-powered or powered hook terminal devices that are favored for their control simplicity, lightweight and low cost; however, these devices are nominally unsightly and lack in grasp variety. The varying drawbacks of both complex myoelectric and simple body-powered devices have led to low adoption rates for all upper limb prostheses by amputees, which includes 35% pediatric and 23% adult rejection for complex devices and 45% pediatric and 26% adult rejection for body-powered devices [1]. My research focuses on progressing the grasping capabilities of prosthetic hands driven by simple control and a single motor, to combine the dexterous functionality of the more complex hands with the intuitive control of the more simplistic body-powered devices with the goal of helping upper limb amputees return to more active and social lifestyles. Optimization of a prosthetic hand driven by a single actuator requires the optimization of many facets of the hand. This includes optimization of the finger kinematics, underactuated mechanisms, geometry, materials and performance when completing activities of daily living. In my dissertation, I will present chapters dedicated to improving these subsystems of single actuator prosthetic hands to better replicate human hand function from simple control. First, I will present a framework created to optimize precision grasping – which is nominally unstable in underactuated configurations – from a single actuator. I will then present several novel mechanisms that allow a single actuator to map to higher degree of freedom motion and multiple commonly used grasp types. I will then discuss how fingerpad geometry and materials can better grasp acquisition and frictional properties within the hand while also providing a method of fabricating lightweight custom prostheses. Last, I will analyze the results of several human subject testing studies to evaluate the optimized hands performance on activities of daily living and compared to other commercially available prosthesis

Design and development of robust hands for humanoid robots

Design and development of robust hands for humanoid robot

Grasp plannind under task-specific contact constraints

Several aspects have to be addressed before realizing the dream of a robotic hand-arm system with human-like capabilities, ranging from the consolidation of a proper mechatronic design, to the development of precise, lightweight sensors and actuators, to the efficient planning and control of the articular forces and motions required for interaction with the environment. This thesis provides solution algorithms for a main problem within the latter aspect, known as the {\em grasp planning} problem: Given a robotic system formed by a multifinger hand attached to an arm, and an object to be grasped, both with a known geometry and location in 3-space, determine how the hand-arm system should be moved without colliding with itself or with the environment, in order to firmly grasp the object in a suitable way. Central to our algorithms is the explicit consideration of a given set of hand-object contact constraints to be satisfied in the final grasp configuration, imposed by the particular manipulation task to be performed with the object. This is a distinguishing feature from other grasp planning algorithms given in the literature, where a means of ensuring precise hand-object contact locations in the resulting grasp is usually not provided. These conventional algorithms are fast, and nicely suited for planning grasps for pick-an-place operations with the object, but not for planning grasps required for a specific manipulation of the object, like those necessary for holding a pen, a pair of scissors, or a jeweler's screwdriver, for instance, when writing, cutting a paper, or turning a screw, respectively. To be able to generate such highly-selective grasps, we assume that a number of surface regions on the hand are to be placed in contact with a number of corresponding regions on the object, and enforce the fulfilment of such constraints on the obtained solutions from the very beginning, in addition to the usual constraints of grasp restrainability, manipulability and collision avoidance. The proposed algorithms can be applied to robotic hands of arbitrary structure, possibly considering compliance in the joints and the contacts if desired, and they can accommodate general patch-patch contact constraints, instead of more restrictive contact types occasionally considered in the literature. It is worth noting, also, that while common force-closure or manipulability indices are used to asses the quality of grasps, no particular assumption is made on the mathematical properties of the quality index to be used, so that any quality criterion can be accommodated in principle. The algorithms have been tested and validated on numerous situations involving real mechanical hands and typical objects, and find applications in classical or emerging contexts like service robotics, telemedicine, space exploration, prosthetics, manipulation in hazardous environments, or human-robot interaction in general

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Exploitation of environmental constraints in human and robotic grasping

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. These constraints, leveraged through motion in contact, counteract uncertainty in state variables relevant to grasp success. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been established. We present support for this view found through the analysis of human grasp behavior and by showing robust robotic grasping based on constraint-exploiting grasp strategies. Furthermore, we show that it is possible to design robotic hands with inherent capabilities for the exploitation of environmental constraints

Fall Prediction and Controlled Fall for Humanoid Robots

Humanoids which resemble humans in their body structure and degrees of freedom are anticipated to work like them within infrastructures and environments constructed for humans. In such scenarios, even humans who have exceptional manipulation, balancing, and locomotion skills are vulnerable to fall, humanoids being their approximate imitators are no exception to this. Furthermore, their high center of gravity position in relation to their small support polygon makes them more prone to fall, unlike other robots such as quadrupeds. The consequences of these falls are so devastating that it can instantly annihilate both the robot and its surroundings. This has become one of the major stumbling blocks which humanoids have to overcome to operate in real environments. As a result, in this thesis, we have strived to address the imminent fall over of humanoids by developing different control techniques. The fall over problem as such can be divided into three subissues: fall prediction, controlled fall, and its recovery. In the presented work, the first two issues have been addressed, and they are presented in three parts. First, we define what is fall over for humanoids, different sources for it to happen, the effect fall over has both on the robot and to its surroundings, and how to deal with them. Following which, we give a brief introduction to the overall system which includes both the hardware and software components which have been used throughout the work for varied purposes. Second, the first sub-issue is addressed by proposing a generic method to predict the falling over of humanoid robots in a reliable, robust, and agile manner across various terrains, and also amidst arbitrary disturbances. The aforementioned characteristics are strived to attain by proposing a prediction principle inspired by the human balance sensory systems. Accordingly, the fusion of multiple sensors such as inertial measurement unit and gyroscope (IMU), foot pressure sensor (FPS), joint encoders, and stereo vision sensor, which are equivalent to the human\u2019s vestibular, proprioception, and vision systems are considered. We first define a set of feature-based fall indicator variables (FIVs) from the different sensors, and the thresholds for those FIVs are extracted analytically for four major disturbance scenarios. Further, an online threshold interpolation technique and an impulse adaptive counter limit are proposed to manage more generic disturbances. For the generalized prediction process, both the instantaneous and cumulative sum of each FIVs are normalized, and a suitable value is set as the critical limit to predict the fall over. To determine the best combination and the usefulness of multiple sensors, the prediction performance is evaluated on four different types of terrains, in three unique combinations: first, each feature individually with their respective FIVs; second, an intuitive performance based (PF); and finally, Kalman filter based (KF) techniques, which involve the usage of multiple features. For PF and KF techniques, prediction performance evaluations are carried out with and without adding noise. Overall, it is reported that KF performs better than PF and individual sensor features under different conditions. Also, the method\u2019s ability to predict fall overs during the robot\u2019s simple dynamic motion is also tested and verified through simulations. Experimental verification of the proposed prediction method on flat and uneven terrains was carried out with the WALK-MAN humanoid robot. Finally, in reference to the second sub-issue, i.e., the controlled fall, we propose two novel fall control techniques based on energy concepts, which can be applied online to mitigate the impact forces incurred during the falling over of humanoids. Both the techniques are inspired by the break-fall motions, in particular, Ukemi motion practiced by martial arts people. The first technique reduces the total energy using a nonlinear control tool, called energy shaping (ES) and further distributes the reduced energy over multiple contacts by means of energy distribution polygons (EDP). We also include an effective orientation control to safeguard the end-effectors in the event of ground impacts. The performance of the proposed method is numerically evaluated by dynamic simulations under the sudden falling over scenario of the humanoid robot for both lateral and sagittal falls. The effectiveness of the proposed ES and EDP concepts are verified by diverse comparative simulations regarding total energy, distribution, and impact forces. Following the first technique, we proposed another controller to generate an online rolling over motion based on the hypothesis that multi-contact motions can reduce the impact forces even further. To generate efficient rolling motion, critical parameters are defined by the insights drawn from a study on rolling, which are contact positions and attack angles. In addition, energy-injection velocity is proposed as an auxiliary rolling parameter to ensure sequential multiple contacts in rolling. An online rolling controller is synthesized to compute the optimal values of the rolling parameters. The first two parameters are to construct a polyhedron, by selecting suitable contacts around the humanoid\u2019s body. This polyhedron distributes the energy gradually across multiple contacts, thus called energy distribution polyhedron. The last parameter is to inject some additional energy into the system during the fall, to overcome energy drought and tip over successive contacts. The proposed controller, incorporated with energy injection, minimization, and distribution techniques result in a rolling like motion and significantly reduces the impact forces, and it is verified in numerical experiments with a segmented planar robot and a full humanoid model