THE BIOMECHATRONIC APPROACH
FOR THE DEVELOPMENT OF ARTIFICIAL HANDS.:
The main goal in designing a
novel humanoid hands is to fulfill critical requirements such as functionality,
controllability, low weight, low energy consumption and noiseless. These
requirements can be fulfilled by an integrated design approach called
biomechatronic design.
The first step towards this
objective is to enhance the hand dexterity by increasing the DOF and reducing
size of the system.The main problem in developing such a hand is the limited
space available to integrate actuators within the hand. Anyway, recent progress
in sensors, actuators and embedded control technologies are encouraging the
development of such hand.
The proposed
biomechatronic hand will be equipped with three actuators systems to provide a
tripod grasping: two identical finger actuators systems and one thumb actuator
system.
The finger actuator system is
based on two micro actuators which drive respectively the metacarpo-phalangeal
joint (MP) and the proximal inter-phalangeal joint (PIP); for cosmetic reasons,
both actuators are fully integrated in the hand structure: the first in the
palm and the second within the proximal phalanx. The distal inter-phalangeal
(DIP) joint is driven by a four bar link connected to the PIP joint.
The grasping task is divided in
two subsequent phases:
1>
Reaching and shape adapting phase;
2> Grasping phase with thumb
opposition.
In fact, in
phase one the first actuator system allows the finger to adapt to the
morphological characteristics of the grasped object by means of a low output
torque motor. In phase two, the thumb actuator system provides a power
opposition useful to manage critical grips, especially in case of heavy or
slippery objects.
KINEMATIC
ARCHITECTURE:
A first analysis based on the
kinematics characteristics of the human hand, during grasping tasks, led us to
approach the mechanical design with a multi-DOF hand structure. Index and
middle finger are equipped with active DOF respectively in the MP and in the
PIP joints, while the DIP joint is actuated by one driven passive DOF.
The thumb
movements are accomplished with two active DOF in the MP joint and one driven
passive DOF in the IP joint. This configuration will permit to oppose the thumb
to each finger.
1.ANTHROPOMORPHIC
SENSORY-MOTOR CO-ORDINATION SCHEMES:
A general
framework for artificial perception and sensory-motor co-ordination in robotic
grasping has been proposed at the ARTS LAB, based on the integration of visual
and tactile perception, processed through anthropomorphic schemes for control,
behavioral planning and learning. The problem of grasping has been sub-divided
into four key problems, for which specific solutions have been implemented and
validated through experimental trials, relying on anthropomorphic sensors and
actuators, such as an integrated fingertip (including a tactile, a thermal and
a dynamic sensor), a retina-like visual sensor, and the anthropomorphic Dexter
arm and Marcus hand.
In particular,
1.
Planning of the pre-grasping hand shaping,
2.
Learning of motor co-ordination strategies.
3.
Tactile-motor co-ordination in graspind and
4. Object classification based
on the visuo-tactile information are described and reported in the following
paragraphs.
1.1 A NEURO-FUZZY APPROACH
TO GRASP PLANNING:
The first
module has the aim of providing the capability of planning the proper hand, in
the case of a multi-fingered hand, based on geometrical features of the object
to be grasped. A neuro-fuzzy approach is adopted for trying to replicate human
capability of processing qualitative data and of learning.
The base of knowledge on which
the fuzzy system can process inputs and determine outputs is built by a neural
network (NN). The trained system has been validated on a test set of 200 rules,
of which the 92.15% was correctly identified.
1.2 INTEGRATION OF VISION
AND TOUCH IN EDGE
TRACKING:
In order to
validate the anthropomorphic model of sensory-motor co-ordination in grasping,
a module was implemented to perform visual and tactile edge tracking,
considered as the first step of sensory-motor co-ordination in grasping actions.
The proposed
methodology includes the application of the reinforcement-learning paradigm to
back propagation NNs, in order to replicate the human capability of creating
associations between sensory data and motor schemes, based on the results of
attempts to perform movements.The resulting robot behavior consists in
co-ordinating the movement of the fingertip along an object edge, by
integrating visual information on the edge, proprioceptive information on the
arm configuration, and tactile information on the contact, and by processing
this information in a neural framework based on the reinforcement-learning
paradigm. The aimed goal of edge tracking is pursued by a strategy starting
from a totally random policy and evolving via rewards and punishments.
