title: Human-like reflex control for an artificial hand.
abstract: In this paper, we illustrate the low level reflex control used to govern an anthropomorphic artificial hand. The paper develops the position and stiffness control strategy based on dynamic artificial neurons able to simulate the neurons acting in the human reflex control. The controller has a hierarchical structure. At the lowest level there are the receptors able to convert the analogical signal into a neural impulsive signal appropriate to govern the reflex control neurons. Immediately upon it, the artificial motoneurons set the actuators inner pressure to control the finger joint position and moment. Other auxiliary neurons in combination with the motoneurons are able to set the finger stiffness and emulate the inverse myotatic reflex control. Stiffness modulation is important both to save energy during task execution, and to manage objects made of different materials. The inverse myotatic reflex is able to protect the hand from possible harmful external actions. The paper also presents the dynamic model of the joints and of the artificial muscles actuating Blackfingers, our artificial hand. This new type of neural control has been simulated on the Blackfingers model; the results indicate that the developed control is very flexible and efficient for all kind of joints present in the humanoid hand.
authors.affiliation: Politecnico di Milano, DEI, Piazza Leonardo da Vinci 32, Italy. folghera@elet.polimi.it
authors.forename: Michele
authors.initials: M
authors.lastname: Folgheraiter
authors.forename: Giuseppina
authors.initials: G
authors.lastname: Gini
mesh_terms: D001186:Artificial Limbs; D001701:Bionics / Q000379:methods; D003198:Computer Simulation; D005246:Feedback / Q000502:physiology; D006225:Hand / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000502:physiology*; D011474:Prosthesis Design; D011475:Prosthesis Failure; D012018:Reflex / Q000502:physiology*; D012371:Robotics / Q000379:methods*