847 research outputs found
Human-like arm motion generation: a review
In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.FCT Project UID/MAT/00013/2013FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020
MULTI-DIGIT HUMAN PREHENSION
The current dissertation addresses the central nervous system (CNS) strategies to solve kinetic redundancy in multi-digit static prehension under different geometries of hand-held objects and systematically varied mechanical constraints such as translation and rotation of the hand-held object. A series of experiments conducted for this dissertation tested the following hypotheses suggested in the current literatures for multi-digit human static prehension: Hierarchical organization hypothesis, principle of superposition hypothesis, proximity hypothesis, and mechanical advantage hypothesis. (1) Forces and moments produced by fingers during circular object prehension were grouped into two independent subsets: one subset related to grasping stability control and the other associated with rotational equilibrium control. This result supports the principle of superposition hypothesis. Individual fingers acted synergistically to compensate each other's errors. This result confirms the hierarchical organization hypothesis in circular object prehension. (2) During fixed object prehension of a rectangular object, the closer the non-task fingers positioned to the task finger, the greater the forces produced by the non-task fingers. However, during free object prehension, the non-task fingers with longer moment arms produced greater forces. The former and latter results support the proximity hypothesis and the mechanical advantage hypothesis, respectively. (3) The grasping stability control and rotational equilibrium control were decoupled during fixed object prehension as well as free object prehension. This result supports the principle of superposition hypothesis regardless of the mechanical constraints provided for these two prehension types. (4) During torque production, the fingers with longer moment arms produced greater forces when the fingers acted as agonists for the torque production. Therefore, the mechanical advantage hypothesis was supported for agonist fingers. (5) Coupling of thumb normal force and virtual finger normal force was not necessitated when horizontal translation of hand-held object was mechanically fixed. However, the coupling of two normal forces was always observed regardless of given translational constraints, and these two normal forces were independent to other mechanical variables such as tangential forces and moments. This result supports the principle of superposition hypothesis in static prehension under varied combinations of translational constraints
Descriptive and explanatory tools for human movement and state estimation in humanoid robotics
Le sujet principal de cette thèse est le mouvement des systèmes anthropomorphes, et plus particulièrement la locomotion bipède des humains et des robots humanoïdes. Pour
caractériser et comprendre la locomotion bipède, il est instructif d'en étudier les causes, qui résident dans le contrôle et l'organisation du mouvement, et les conséquences qui en résultent, que sont le mouvement et les interactions physiques avec l'environnement. Concernant les causes, par exemple, quels sont les principes qui régissent l'organisation des ordres moteurs pour élaborer une stratégie de déplacement spécifique ? Puis, quelles grandeurs physiques pouvons-nous calculer pour décrire au mieux le mouvement résultant de ces commandes motrices ? Ces questions sont en partie abordées par la proposition d'une extension mathématique de l'approche du Uncontrolled Manifold au contrôle moteur de tâches dynamiques, puis par la présentation d'un nouveau descripteur de la locomotion anthropomorphe. En lien avec ce travail analytique vient le problème de l'estimation de l'état pour les systèmes anthropomorphes. La difficulté d'un tel problème vient du fait que les mesures apportent un bruit qui n'est pas toujours séparable des données informatives, et que l'état du système n'est pas nécessairement observable. Pour se débarrasser du bruit, des techniques de filtrage classiques peuvent être employées, mais elles sont susceptibles d'altérer le contenu des signaux d'intérêt. Pour faire face à ce problème, nous présentons une méthode récursive, basée sur le filtrage complémentaire, pour estimer la position du centre de masse et la variation du moment cinétique d'un système en contact, deux quantités centrales de la locomotion bipède. Une autre idée pour se débarrasser du bruit de mesure est de réaliser qu'il résulte en une estimation irréaliste de la dynamique du système. En exploitant les équations du mouvement, qui dictent la dynamique temporelle du système, et en estimant une trajectoire plutôt qu'un point unique, nous présentons
ensuite une estimation du maximum de vraisemblance en utilisant l'algorithme de programmation différentielle dynamique pour effectuer une estimation optimale de l'état
centroidal des systèmes en contact. Finalement, une réflexion pluridisciplinaire est présentée, sur le rôle fonctionnel et computationnel joué par la tête chez les animaux. La pertinence de son utilisation en robotique mobile y est discutée, pour l'estimation d'état et la perception multisensorielle.The substantive subject of this thesis is the motion of anthropomorphic systems, and more particularly the bipedal locomotion of humans and humanoid robots. To characterize and understand bipedal locomotion, it is instructive to study its motor causes and its resulting physical consequences, namely, the interactions with the environment. Concerning the causes, for instance, what are the principles that govern the organization of motor orders in humans for elaborating a specific displacement strategy? And then, which physical quantities can we compute for best describing the motion resulting from these motor orders ? These questions are in part addressed by the proposal of a mathematical extension of the Uncontrolled Manifold approach for the motor control of dynamic tasks and through the presentation of a new descriptor of anthropomorphic locomotion. In connection with this analytical work, comes the problem of state estimation in anthropomorphic systems. The difficulty of such a problem comes from the fact that the measurements carry noise which is not always separable from the informative data, and that the state of the system is not necessarily observable. To get rid of the noise,
classical filtering techniques can be employed but they are likely to distort the signals. To cope with this issue, we present a recursive method, based on complementary filtering, to estimate the position of the center of mass and the angular momentum variation of the human body, two central quantities of human locomotion. Another idea to get
rid of the measurements noise is to acknowledge the fact that it results in an unrealistic estimation of the motion dynamics. By exploiting the equations of motion, which
dictate the temporal dynamics of the system, and by estimating a trajectory versus a single point, we then present maximum likelihood estimation using the dynamic differential programming algorithm to perform optimal centroidal state estimation for systems in contact.
Finally, a multidisciplinary reflection on the functional and computational role played by the head in animals is presented. The relevance of using this solution in mobile
robotics is discussed, particularly for state estimation and multisensory perception
Sensory Motor Remapping of Space in Human-Machine Interfaces
Studies of adaptation to patterns of deterministic forces have revealed the ability of the motor control system to form and use predictive representations of the environment. These studies have also pointed out that adaptation to novel dynamics is aimed at preserving the trajectories of a controlled endpoint, either the hand of a subject or a transported object. We review some of these experiments and present more recent studies aimed at understanding how the motor system forms representations of the physical space in which actions take place. An extensive line of investigations in visual information processing has dealt with the issue of how the Euclidean properties of space are recovered from visual signals that do not appear to possess these properties. The same question is addressed here in the context of motor behavior and motor learning by observing how people remap hand gestures and body motions that control the state of an external device. We present some theoretical considerations and experimental evidence about the ability of the nervous system to create novel patterns of coordination that are consistent with the representation of extrapersonal space. We also discuss the perspective of endowing human–machine interfaces with learning algorithms that, combined with human learning, may facilitate the control of powered wheelchairs and other assistive devices
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Analysis and synthesis of bipedal humanoid movement : a physical simulation approach
textAdvances in graphics and robotics have increased the importance of tools for synthesizing humanoid movements to control animated characters and physical robots. There is also an increasing need for analyzing human movements for clinical diagnosis and rehabilitation. Existing tools can be expensive, inefficient, or difficult to use. Using simulated physics and motion capture to develop an interactive virtual reality environment, we capture natural human movements in response to controlled stimuli. This research then applies insights into the mathematics underlying physics simulation to adapt the physics solver to support many important tasks involved in analyzing and synthesizing humanoid movement. These tasks include fitting an articulated physical model to motion capture data, modifying the model pose to achieve a desired configuration (inverse kinematics), inferring internal torques consistent with changing pose data (inverse dynamics), and transferring a movement from one model to another model (retargeting). The result is a powerful and intuitive process for analyzing and synthesizing movement in a single unified framework.Computer Science
Recent Advances in Laparoscopic Surgery
The implementation of laparoscopy has revolutionized surgery over the past few years, incorporating significant benefits for the patient. However, this evolution has also entailed many technical obstacles for surgeons. This book is for readers wanting to learn more about recent surgical techniques and technologies. Topics cover novel sophisticated approaches for single-site surgery, natural orifice transluminal endoscopic surgery, and transanal surgery, among others. Also included are reviews of new innovative surgical devices, robotic platforms, and methodological guidelines for improving surgical performance and surgeon ergonomics
A family of asymptotically stable control laws for flexible robots based on a passivity approach
A general family of asymptotically stabilizing control laws is introduced for a class of nonlinear Hamiltonian systems. The inherent passivity property of this class of systems and the Passivity Theorem are used to show the closed-loop input/output stability which is then related to the internal state space stability through the stabilizability and detectability condition. Applications of these results include fully actuated robots, flexible joint robots, and robots with link flexibility
Influence of low back pain and its remission on motor abundance in a low-load lifting task
Having an abundance of motor solutions during movement may be advantageous for the health of musculoskeletal tissues, given greater load distribution between tissues. The aim of the present study was to understand whether motor abundance differs between people with and without low back pain (LBP) during a low-load lifting task. Motion capture with electromyography (EMG) assessment of 15 muscles was performed on 48 participants [healthy control (con) = 16, remission LBP (rLBP) = 16, current LBP (cLBP) = 16], during lifting. Non-negative matrix factorization and uncontrolled manifold analysis were performed to decompose inter-repetition variability in the temporal activity of muscle modes into goal equivalent (GEV) and non-goal equivalent (NGEV) variabilities in the control of the pelvis and trunk linear displacements. Motor abundance occurs when the ratio of GEV to NGEV exceeds zero. There were significant group differences in the temporal activity of muscle modes, such that both cLBP and rLBP individuals demonstrated greater activity of muscle modes that reflected lumbopelvic coactivation during the lifting phase compared to controls. For motor abundance, there were no significant differences between groups. Individuals with LBP, including those in remission, had similar overall motor abundance, but use different activation profiles of muscle modes than asymptomatic people during lifting
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