6,884 research outputs found
An Experimental Platform for Multi-spacecraft Phase-Array Communications
The emergence of small satellites and CubeSats for interplanetary exploration
will mean hundreds if not thousands of spacecraft exploring every corner of the
solar-system. Current methods for communication and tracking of deep space
probes use ground based systems such as the Deep Space Network (DSN). However,
the increased communication demand will require radically new methods to ease
communication congestion. Networks of communication relay satellites located at
strategic locations such as geostationary orbit and Lagrange points are
potential solutions. Instead of one large communication relay satellite, we
could have scores of small satellites that utilize phase arrays to effectively
operate as one large satellite. Excess payload capacity on rockets can be used
to warehouse more small satellites in the communication network. The advantage
of this network is that even if one or a few of the satellites are damaged or
destroyed, the network still operates but with degraded performance. The
satellite network would operate in a distributed architecture and some
satellites maybe dynamically repurposed to split and communicate with multiple
targets at once. The potential for this alternate communication architecture is
significant, but this requires development of satellite formation flying and
networking technologies. Our research has found neural-network control
approaches such as the Artificial Neural Tissue can be effectively used to
control multirobot/multi-spacecraft systems and can produce human competitive
controllers. We have been developing a laboratory experiment platform called
Athena to develop critical spacecraft control algorithms and cognitive
communication methods. We briefly report on the development of the platform and
our plans to gain insight into communication phase arrays for space.Comment: 4 pages, 10 figures, IEEE Cognitive Communications for Aerospace
Applications Worksho
The Evolution of Reaction-diffusion Controllers for Minimally Cognitive Agents
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Is there an integrative center in the vertebrate brain-stem? A robotic evaluation of a model of the reticular formation viewed as an action selection device
Neurobehavioral data from intact, decerebrate, and neonatal rats, suggests that the reticular formation provides
a brainstem substrate for action selection in the vertebrate central nervous system. In this article, Kilmer,
McCulloch and Blum’s (1969, 1997) landmark reticular formation model is described and re-evaluated, both in
simulation and, for the first time, as a mobile robot controller. Particular model configurations are found to
provide effective action selection mechanisms in a robot survival task using either simulated or physical robots.
The model’s competence is dependent on the organization of afferents from model sensory systems, and a genetic
algorithm search identified a class of afferent configurations which have long survival times. The results support
our proposal that the reticular formation evolved to provide effective arbitration between innate behaviors
and, with the forebrain basal ganglia, may constitute the integrative, ’centrencephalic’ core of vertebrate brain
architecture. Additionally, the results demonstrate that the Kilmer et al. model provides an alternative form of
robot controller to those usually considered in the adaptive behavior literature
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
Knowledge-based vision and simple visual machines
The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong
Evolution of Swarm Robotics Systems with Novelty Search
Novelty search is a recent artificial evolution technique that challenges
traditional evolutionary approaches. In novelty search, solutions are rewarded
based on their novelty, rather than their quality with respect to a predefined
objective. The lack of a predefined objective precludes premature convergence
caused by a deceptive fitness function. In this paper, we apply novelty search
combined with NEAT to the evolution of neural controllers for homogeneous
swarms of robots. Our empirical study is conducted in simulation, and we use a
common swarm robotics task - aggregation, and a more challenging task - sharing
of an energy recharging station. Our results show that novelty search is
unaffected by deception, is notably effective in bootstrapping the evolution,
can find solutions with lower complexity than fitness-based evolution, and can
find a broad diversity of solutions for the same task. Even in non-deceptive
setups, novelty search achieves solution qualities similar to those obtained in
traditional fitness-based evolution. Our study also encompasses variants of
novelty search that work in concert with fitness-based evolution to combine the
exploratory character of novelty search with the exploitatory character of
objective-based evolution. We show that these variants can further improve the
performance of novelty search. Overall, our study shows that novelty search is
a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final
publication will be available at link.springer.co
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary
figures. Accepted at Information Sciences journal (in press). Supplemental
videos are available online at, see http://goo.gl/uyY1R
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