Sistema ETD

banca dati delle tesi e dissertazioni accademiche elettroniche

 

Tesi etd-06102014-193314


Tipo di tesi
Tesi di dottorato di ricerca
Autore
AJOUDANI, ARASH
URN
etd-06102014-193314
Titolo
Transferring Human Impedance Regulation Skills to Robots
Settore scientifico disciplinare
ING-INF/04
Corso di studi
INGEGNERIA
Commissione
tutor Ing. Tsagarakis, Nikos G.
tutor Prof. Bicchi, Antonio
Parole chiave
  • teleimpedance
  • rehabilitation robotics
  • natural redundancy resolution
  • impedance control
  • teleoperation
Data inizio appello
14/07/2014;
Disponibilità
completa
Riassunto analitico
This thesis introduces novel thinking and techniques to the control of robotic manipulation. In particular, the concept of teleimpedance control as an alternative method to bilateral force-reflecting teleoperation control for robotic manipulation is introduced. In teleimpedance control, a compound reference command is sent to the slave robot including both the desired motion trajectory and impedance profile, which are then realized by the remote controller. This concept forms a basis for the development of the controllers for a robotic arm, a dual-arm setup, a synergy-driven robotic hand, and a compliant exoskeleton for improved interaction performance.

First part of this thesis concerns the teleimpedance control of a robotic arm. Here, the reference commands are derived from a novel Body-Machine Interface (BMI) applied to the master operator's arm, using only non-intrusive position and electromyography (EMG) measurements. The proposed BMI exploits a novel algorithm to decouple the estimates of force and stiffness of the human arm while performing the task. The endpoint (wrist) position of the human arm is monitored by an optical tracking system and used for the closed--loop position control of the robot's end effector. A Cartesian stiffness controller is adopted to realize the desired compliant profile, derived from the proposed BMI model. The concept is demonstrated in two experiments, namely a peg-in-the-hole and a ball-catching task, which illustrate complementary aspects of the method. In addition to the improved interaction performance achieved, results suggest that contribution of the human/robot configuration to realization of a desired endpoint stiffness ellipsoid directionality is major. To that end, the concept of common mode stiffness (CMS) and configuration dependent stiffness (CDS) control is proposed and experimentally evaluated in an assembly task.

In the second part, relying on the observations on human bimanual coordination, a novel realtime motion control strategy is proposed to regulate the desired Cartesian stiffness profile during the execution of bimanual tasks. The novelty of the proposed control scheme relies on the use of CMS and CDS to regulate the size and directionality of the task space stiffness ellipsoid. Thanks to the CDS control, the proposed scheme is not only proved to be effective in regulating the desired stiffness ellipsoid but also permits to resolve the manipulator redundancy in a manner that results in motions exhibiting similarities with those performed by human during the execution of bimanual tasks. The effectiveness of the controller is evaluated in an experimental setup in which two cooperating robotic arms are executing an assembly task. Experimental results demonstrate that the proposed dual-arm CDS-CMS controller is effective in tracking the desired stiffness ellipsoids as well as in producing human-like natural motions for the two robotic arms.

Third part proposes a teleimpedance controller with tactile feedback for more intuitive control of the Pisa/IIT SoftHand. With the aim to realize a robust, efficient and low-cost hand prosthesis design, the SoftHand is developed based on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. In addition, for intuitive control of the hand, two tactile interfaces are developed. The first interface (mechanotactile) exploits a disturbance observer which estimates the interaction forces in contact with the grasped object. Estimated interaction forces are then converted and applied to the upper arm of the user via a custom made pressure cuff. The second interface employs vibrotactile feedback based on surface irregularities and acceleration signals and is used to provide the user with information about the surface properties of the object as well as detection of object slippage while grasping. Grasp robustness and intuitiveness of hand control were evaluated in two sets of experiments. Results suggest that incorporating the aforementioned haptic feedback strategies, together with user-driven compliance of the hand, facilitate execution of safe and stable grasps, while suggesting that a low-cost, robust hand employing hardware-based synergies might be a good alternative to traditional myoelectric prostheses.

Finally, last part presents a teleimpedance based assistive control scheme for a compliant knee exoskeleton. Proposed controller captures the user intent to generate task-related assistive torques by means of the exoskeleton. To do so, a detailed musculoskeletal model of the human knee is developed and experimentally calibrated to better match the user's kinematic and dynamic behavior. EMG signals are acquired, processed and used for the estimation of the knee joint torque, trajectory and the stiffness trend, in real time. Estimated stiffness trend is then scaled and mapped to a task-related stiffness interval to agree with the desired degree of assistance. The desired stiffness and equilibrium trajectories are then tracked by the exoskeleton's impedance controller. As a consequence, while the minimum muscular activity corresponds to low stiffness, i.e. highly transparent motion, higher cocontractions result in stiffer joint and stronger level of assistance.

Overall results suggest that incorporation of the human motor control principles into the design of the robot controllers will eventually permit them to reach interaction performances close to those achieved by the humans, demonstrating a versatile and stable behavior even when interacting with environments with dynamic uncertainties.
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