H.Y.K. Lau
Mechatronics, No.13, 2003
Summary
Robotic assembly is a defined as a classical
problem in industrial robotics where a rigid robot with accurate actuators
performed sequences of predetermined operations in an assigned workspace
through a position-based controller. Limitations during assembly tasks appear
to be due to fixtures, features and tolerances, in order to overcome these
limitations force-based control strategies have been proposed, such as: spring
damper, hybrid force/position control, impendence control and so on. The
fundamental issue in robotic assembly is the ability to recognize assembly
states and this should be addressed before the deployment of appropriate
control strategies. The proposed system (HMM-based contact state recognized)
can be considered a high-level feedback control system for perceiving the
environment in terms of symbolic expressions. With Fundamental Contact (FC) the
author intends to describe contact formations that may consist of zero, one or
more pairs between the work piece and the environment in which it is involved
(it is a primitive for of contact formation involving a single pair of contact
features). A contact is defined as a result of geometric arrangement, contact
based control strategy for robot assembly is based on the pattern of
force/torque which is formed between a work piece and the manipulator, so that
the d.o.f. is reduced at least by one. Different contact states are then
defined: 1) Configuration of an object (c, is the set of kinematics parameters
that locates an object for a given set of joint angles); 2) Configuration Space
(C, the Rn space which collects different c); 3) Contact Feature ( F≜{f,e,v}, where
f is the face, e the edge and v the vertex of the object); 4) Fundamental
Contact (FC ordered pair of contact feature; 5) Contact State (CS is a set of
FC between to polyhedra).
In
robotic assembly Contact Recognition is defined as the process using a contact
sensing techniques to obtain geometrical information of contact with
corresponding symbolic interpretations. Symbolic interpretations of an assembly
provide high-level knowledge of assembly operations to robot programmers, but
some limitations in the models (Petri-nets, ANN, Rule-Based contact analysis
and further on) have been encountered due to complexity. HMM, already used
successfully for speech recognition, is proposed considering assembly states
interrelated and occurrence of contact formation between a work piece and the mating
parts normally distributed.
·
Model
A
HMM is composed of two stochastic processes, a basic Markov chain and an
observable stochastic process. HMM are defined with a triplet λ=(A,B,π) (A
is the transition matrix for the transition probabilities, B the matrix of
probabilities for the observables and π is the initial state
of the distribuition). HMM can be used to generate the observation sequence,
mainly observation probabilities can be evaluated, the most probable sequence,
given the observation sequence, can be also obtained and the probability of a
sequence given an observable for a certain triplet can be maximized to find the
HMM performance. The architecture of the HMM-based contact state recognition
works in the following manner: defining the force torque signals, process them,
map them and transfer the data to the HMM, after which the model with maximum
probability must be selected in order to finally classify the state. The
method, which for its final implementation uses forward and backward algorithm,
Baum Welch algorithm (Appendix A),ALBG-VQ algorithm and symbol mapping
algorithm (Appendix B), has been tested to investigate sensitivity, performance
of recognizing 2D and 3D contact formations and the ability to classify
sequence of contact formation during peg-in-hole tasks.
Key
Concepts
HMM, assembly contact
recognition
Key Results
The system is insensitive
to the number of states and small amount of training data is required.
Superiority of HMM-based system has been shown compared with other traditional
methods already mentioned above.
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