Abstract
Evaluation of robotic systems and complex behaviours
using evolutionary algorithms : in this thesis, an
original approach for evaluation of robotic systems in
the context of simultaneous structure/control design is
presented. It relies on the evolutionary algorithms.
The initial procedures for evaluation are usually
difficult to implement and expensive in computing time.
The developed method uses genetic programming within an
evolutionary symbolic regression algorithm, to generate
expressions with various levels of refinement which are
intended to approximate the original evaluations
(according to the concept of metamodels). The interest
of this approach is illustrated by various applications
of gradual complexity where the initial evaluation
methods can be simple functions, algorithms or a value
drawn from a simulation considering the globality of
the system to be designed, its interactions with the
environment and its tasks. Reliable and fast generic
models, which are solutions of the inverse kinematic
problem for any 6R manipulator geometry (analytical or
not), have been produced via approximating functions.
The application of these techniques to a problem with
dynamics resulted in fixing restrictions to the use of
our method for direct approximation of constrained
behaviours. Evolutionary symbolic regression is then
applied within the framework of optimisations by
genetic algorithms (GA), for simple cases like when a
GA seeks a solution of the 2D inverse kinematic
problem, or more complex like preliminary design of
smart active endoscopes for minimally invasive surgery.
Additionally, an extension allowing to increase the
evolutionarity of GA is deduced.
- algorithm,
- computer-aided
- control,
- design,
- devices,
- evaluation,
- genetic
- invasive
- inverse
- kinematic
- medical
- minimally
- models,
- problem,
- programming,
- regression,
- robotic
- simulation,
- simultaneous
- structure/control
- surgery
- symbolic
- systems,
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