2,888 research outputs found
Evolving controllers for simulated car racing
This paper describes the evolution of controllers for racing a simulated radio-controlled car around a track, modelled on a real physical track. Five different controller architectures were compared, based on neural networks, force fields and action sequences. The controllers use either egocentric (first person), Newtonian (third person) or no information about the state of the car (open-loop controller). The only controller that is able to evolve good racing behaviour is based on a neural network acting on egocentric inputs
An empirical evaluation of evolutionary controller design methods for collective gathering task
This research aims to evaluate the performance of evolutionary controller design methods for developing a collective behaviour for a team of robots. The methods tested in this research are NEAT which is capable of finding minimal solution quickly, and SANE which maintains high genetic diversity through neuron level evolution. The task chosen for these methods was a collective gathering task which required a team of robots to cooperate in finding and retrieving item of interest. Our results showed that NEAT consistently produced better controllers compared to SANE
Evolution of Prehension Ability in an Anthropomorphic Neurorobotic Arm
In this paper we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment. The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robot’s body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators, and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules
Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior
Previous research using evolutionary computation in Multi-Agent Systems
indicates that assigning fitness based on team vs.\ individual behavior has a
strong impact on the ability of evolved teams of artificial agents to exhibit
teamwork in challenging tasks. However, such research only made use of
single-objective evolution. In contrast, when a multiobjective evolutionary
algorithm is used, populations can be subject to individual-level objectives,
team-level objectives, or combinations of the two. This paper explores the
performance of cooperatively coevolved teams of agents controlled by artificial
neural networks subject to these types of objectives. Specifically, predator
agents are evolved to capture scripted prey agents in a torus-shaped grid
world. Because of the tension between individual and team behaviors, multiple
modes of behavior can be useful, and thus the effect of modular neural networks
is also explored. Results demonstrate that fitness rewarding individual
behavior is superior to fitness rewarding team behavior, despite being applied
to a cooperative task. However, the use of networks with multiple modules
allows predators to discover intelligent behavior, regardless of which type of
objectives are used
An AER handshake-less modular infrastructure PCB with x8 2.5Gbps LVDS serial links
Nowadays spike-based brain processing emulation is
taking off. Several EU and others worldwide projects are
demonstrating this, like SpiNNaker, BrainScaleS, FACETS, or
NeuroGrid. The larger the brain process emulation on silicon is,
the higher the communication performance of the hosting
platforms has to be. Many times the bottleneck of these system
implementations is not on the performance inside a chip or a
board, but in the communication between boards. This paper
describes a novel modular Address-Event-Representation (AER)
FPGA-based (Spartan6) infrastructure PCB (the AER-Node
board) with 2.5Gbps LVDS high speed serial links over SATA
cables that offers a peak performance of 32-bit 62.5Meps (Mega
events per second) on board-to-board communications. The
board allows back compatibility with parallel AER devices
supporting up to x2 28-bit parallel data with asynchronous
handshake. These boards also allow modular expansion
functionality through several daughter boards. The paper is
focused on describing in detail the LVDS serial interface and
presenting its performance.Ministerio de Ciencia e Innovación TEC2009-10639-C04-02/01Ministerio de Economía y Competitividad TEC2012-37868-C04-02/01Junta de Andalucía TIC-6091Ministerio de Economía y Competitividad PRI-PIMCHI-2011-076
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