Grasping algorithms for anthropomorphic robotic hands inspired to human behavior
Cordella, Francesca (2011) Grasping algorithms for anthropomorphic robotic hands inspired to human behavior. [Tesi di dottorato] (Inedito)
Full text disponibile come:
Biologically inspired robotic systems are becoming increasingly popular, especially in the field of medical robotics, in which building robotic devices able to replicate the human behavior guarantees obtaining motor recovery, functional substitution or human-robot interaction as human-like as possible. It is widely recognized that robotic rehabilitation devices improve the performance of the rehabilitation therapy performed by a human therapist in terms of action repetition and accurate tracking of the desired trajectory. Taking advantage from the plasticity of the neuro-muscular system, a human-inspired robotic rehabilitation therapy helps patients to re-learn movements. In the field of upper limb prosthetics, since the aim of a prosthetic hand is to replace a human hand, the robotic device has to be not only functional, but also as similar as possible to the human one both from the morphological point of view and as regards movement naturalness. On the other hand, since grasping is one of the human skills that robotic researchers mostly attempt at imitating, in the development of new robotic hands, the inspiration to the human hand behavior is increasing. From the analysis of the grasping action performed by human beings and from the study of the anatomy of the human hand and of its behavior during grasping, it is possible to obtain useful information for developing human-like grasping algorithms so as to acquire a better knowledge of the hand kinematics in order to design new human-like robotic hands and new rehabilitation devices. The definition of the kinematic structure of the hand and of the fingers is, in fact, the basis for designing new dexterous robotic hands and devices devoted to interact with the human hand (such as rehabilitation devices). Therefore this work is focused on the study of the hand kinematics, providing the basis for a further study regarding the hand dynamics. All the experiments done are in fact adaptable for a future study of the hand dynamics. In assistive robotics, as well as in the field of hand prostheses, the ability of performing smooth movements and obtaining a stable grasp is essential. Therefore, one of the aims of this thesis is to develop a bio-inspired approach for posture prediction and finger trajectory planning with a robotic hand. In order to do that, the human grasping action has been deeply analyzed. It has been decomposed in three main phases: reaching, pre-shaping and grasping. In order to reduce the complexity of planning dexterous hand grasps, it is useful to find the best hand preshape: therefore, this work is focused on this grasping phase. An accurate analysis of anatomy, surgery and rehabilitation literature has been done. In order to confirm the literature results and to cope with the lack of information, e.g. about thumb behavior, different methods for acquiring information about the human hand behavior have been used. Some important features about grasping have been collected from the analysis of the data obtained from two different devices for movement analysis: the Vicon system and a sensorized glove (the CyberGlove). The hand joint behavior during the grasping action has been analyzed asking different subjects to realize four different grasping tasks. The selected tasks guarantee that the subjects pose the hand in the most commonly used configurations. The experiments were performed asking subjects to wear the CyberGlove or attaching on their hands markers visible by the Vicon cameras. The obtained data have been analyzed using different hand kinematic human-inspired models. In order to overcome the drawbacks of the motion analysis devices listed before (as the not completely natural movements performed wearing a data glove, the impossibility to use the CyberGlove from people of different hand sizes and the high cost of the Vicon system), and to obtain information about the hand movements, the Kinect motion sensing device has also been used. For determining the finger joint positions and trajectories during hand movements, a finger tracking algorithm for the Kinect camera has been implemented. Blue markers have been placed on the hand joints following the same configuration used in the experiments performed with the Vicon cameras. A coloured blob detection algorithm and a multiple object tracking algorithm based on particle filters and extended Kalman filter has been implemented. When observing the human grasping behavior, thanks to the input devices listed before, it has been possible to notice some common characteristics among different subjects. The literature results about the dependence of grasping shape on object properties and grip types have been confirmed. The relationship between hand joints for each subject and among different subject has been investigated. One of the obtained results has been finding a constant value of the hand aperture angle (the angle between thumb and index finger). Also the curvature of the fingers is constant among different subjects (related to hand dimensions). Therefore, on the basis of neurological studies and of the analysis of the obtained data, a bio-inspired algorithm for predicting the power-grip posture and planning the finger trajectory of a robotic hand has been developed. The method estimates the best joint hand configuration during diagonal and transverse volar grasp minimizing a purposely defined objective function given by the sum of the joint distances from the object center of rotation (COR). The developed grasping algorithm calculates the position of the fingers for grasping, finding the best hand configuration that ensures a stable human-like grasp. The implementation of the algorithm on a real robotic platform has validated its effectiveness. From the above discussion, it is clear that the aim of this work is to find a way of exploiting the knowledge about a natural system, namely the human hand, in order to design a robotic system. After investigating and understanding in depth the human grasping action, the obtained results have multiple applications such as: overcoming the structural lack of the actual robotic hands (for instance, the non opposable thumb); developing new interfaces for rehabilitation (the finger tracking algorithm developed for the Kinect motion sensing device could be a new rehabilitation interface with potential application in the rehabilitation field); developing bio-inspired approaches for posture prediction and finger trajectory planning in order to perform a stable human-like grasp with a robotic hand.
Solo per gli Amministratori dell'archivio: edita il record