9,166 research outputs found
Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior
In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad
The Ariadne's Clew Algorithm
We present a new approach to path planning, called the "Ariadne's clew
algorithm". It is designed to find paths in high-dimensional continuous spaces
and applies to robots with many degrees of freedom in static, as well as
dynamic environments - ones where obstacles may move. The Ariadne's clew
algorithm comprises two sub-algorithms, called Search and Explore, applied in
an interleaved manner. Explore builds a representation of the accessible space
while Search looks for the target. Both are posed as optimization problems. We
describe a real implementation of the algorithm to plan paths for a six degrees
of freedom arm in a dynamic environment where another six degrees of freedom
arm is used as a moving obstacle. Experimental results show that a path is
found in about one second without any pre-processing
Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression
Developing mathematical models of dynamic systems is central to many
disciplines of engineering and science. Models facilitate simulations, analysis
of the system's behavior, decision making and design of automatic control
algorithms. Even inherently model-free control techniques such as reinforcement
learning (RL) have been shown to benefit from the use of models, typically
learned online. Any model construction method must address the tradeoff between
the accuracy of the model and its complexity, which is difficult to strike. In
this paper, we propose to employ symbolic regression (SR) to construct
parsimonious process models described by analytic equations. We have equipped
our method with two different state-of-the-art SR algorithms which
automatically search for equations that fit the measured data: Single Node
Genetic Programming (SNGP) and Multi-Gene Genetic Programming (MGGP). In
addition to the standard problem formulation in the state-space domain, we show
how the method can also be applied to input-output models of the NARX
(nonlinear autoregressive with exogenous input) type. We present the approach
on three simulated examples with up to 14-dimensional state space: an inverted
pendulum, a mobile robot, and a bipedal walking robot. A comparison with deep
neural networks and local linear regression shows that SR in most cases
outperforms these commonly used alternative methods. We demonstrate on a real
pendulum system that the analytic model found enables a RL controller to
successfully perform the swing-up task, based on a model constructed from only
100 data samples
A Robotic CAD System using a Bayesian Framework
We present in this paper a Bayesian CAD system
for robotic applications. We address the problem of the
propagation of geometric uncertainties and how esian
CAD system for robotic applications. We address the
problem of the propagation of geometric uncertainties
and how to take this propagation into account when
solving inverse problems. We describe the methodology
we use to represent and handle uncertainties using
probability distributions on the system's parameters
and sensor measurements. It may be seen as a
generalization of constraint-based approaches where we
express a constraint as a probability distribution instead
of a simple equality or inequality. Appropriate
numerical algorithms used to apply this methodology
are also described. Using an example, we show how
to apply our approach by providing simulation results
using our CAD system
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