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Evolved transistor array robot controllers
For the first time a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were also evolved and these were used in conjunction with the controllers to enable voice control of the robot, triggering behavioural switching. Poor quality visual sensors were deliberately used to test the ability of evolved analog circuits to deal with noisy uncertain data in realtime. Visual features were coevolved with the controllers to automatically achieve dimensionality reduction and feature extraction and selection in an integrated way. An efficient new method was developed for simulating the robot in its visual environment. This allowed controllers to be evaluated in a simulation connected to the FPTA. The controllers then transferred seamlessly to the real world. The circuit replication issue was also addressed in experiments where circuits were evolved to be able to function correctly in multiple areas of the FPTA. A methodology was developed to
analyse the evolved circuits which provided insights into their operation. Comparative experiments demonstrated the superior evolvability of the transistor array medium
An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to
solving mobile robot navigation problems is presented and tested in both real
and simulated environments. The LTL consists of rapid simulations that use a
Genetic Algorithm to derive diverse sets of behaviours. These sets are then
transferred to an idiotypic Artificial Immune System (AIS), which forms the STL
phase, and the system is said to be seeded. The combined LTL-STL approach is
compared with using STL only, and with using a handdesigned controller. In
addition, the STL phase is tested when the idiotypic mechanism is turned off.
The results provide substantial evidence that the best option is the seeded
idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS
for the STL. They also show that structurally different environments can be
used for the two phases without compromising transferabilityComment: 13 pages, 5 tables, 4 figures, 7th International Conference on
Artificial Immune Systems (ICARIS2008), Phuket, Thailan
Evolution of Adaptive Behaviour in Robots by Means of Darwinian Selection
ii.34., humans have been intrigued by the origin and mechanisms underlying complexity in nature. Darwin suggested that adaptation and complexity could evolve by natural selection acting successively on numerous small, heritable modifications. But is this enough? Here, we describe selected studies of experimental evolution with robots to illustrate how the process of natural selection can lead to the evolution of complex traits such as adaptive behaviours. Just a few hundred generations of selection are sufficient to allow robots to evolve collision-free movement, homing, sophisticate
Two-Timescale Learning Using Idiotypic Behaviour Mediation For A Navigating Mobile Robot
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to
solving mobile-robot navigation problems is presented and tested in both the
real and virtual domains. The LTL phase consists of rapid simulations that use
a Genetic Algorithm to derive diverse sets of behaviours, encoded as variable
sets of attributes, and the STL phase is an idiotypic Artificial Immune System.
Results from the LTL phase show that sets of behaviours develop very rapidly,
and significantly greater diversity is obtained when multiple autonomous
populations are used, rather than a single one. The architecture is assessed
under various scenarios, including removal of the LTL phase and switching off
the idiotypic mechanism in the STL phase. The comparisons provide substantial
evidence that the best option is the inclusion of both the LTL phase and the
idiotypic system. In addition, this paper shows that structurally different
environments can be used for the two phases without compromising
transferability.Comment: 40 pages, 12 tables, Journal of Applied Soft Computin
Robust Parafoil Terminal Guidance Using Massively Parallel Processing
Terminal guidance of autonomous parafoils is a difficult problem in which wind uncertainty and system underactuation are major challenges. Existing strategies almost exclusively use impact error as the criterion for optimality. Practical airdrop systems, however, must also include other criteria that maybe even more important than impact error for some missions, such as ground speed at impact or constraints imposed by drop zones with restrictions on flight patterns. Furthermore, existing guidance schemes determine terminal trajectories using deterministic wind information and may result in a solution that works in ideal wind but may be sensitive to variations. The work described here develops a guidance strategy that uses massively parallel Monte Carlo simulation performed on a graphics processing unit to rank candidate trajectories in terms of robustness to wind uncertainty. The result is robust guidance, as opposed to optimal guidance. Through simulation results, the proposed path planning scheme proves more robust in realistic dynamic wind environments compared with previous optimal trajectory planners that assume perfect knowledge of a constant wind
Quality Diversity: Harnessing Evolution to Generate a Diversity of High-Performing Solutions
Evolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of satisfying quality emerges. At the same time, research in open-ended evolution (OEE) draws different lessons from nature, seeking to identify and recreate processes that lead to the type of perpetual innovation and indefinitely increasing complexity observed in natural evolution. New algorithms in EC such as MAP-Elites and Novelty Search with Local Competition harness the toolkit of evolution for a related purpose: finding as many types of good solutions as possible (rather than merely the single best solution). With the field in its infancy, no empirical studies previously existed comparing these so-called quality diversity (QD) algorithms. This dissertation (1) contains the first extensive and methodical effort to compare different approaches to QD (including both existing published approaches as well as some new methods presented for the first time here) and to understand how they operate to help inform better approaches in the future. It also (2) introduces a new technique for encoding neural networks for evolution with indirect encoding that contain multiple sensory or output modalities. Further, it (3) explores the idea that QD can act as an engine of open-ended discovery by introducing an expressive platform called Voxelbuild where QD algorithms continually evolve robots that stack blocks in new ways. A culminating experiment (4) is presented that investigates evolution in Voxelbuild over a very long timescale. This research thus stands to advance the OEE community\u27s desire to create and understand open-ended systems while also laying the groundwork for QD to realize its potential within EC as a means to automatically generate an endless progression of new content in real-world applications
Visual Homing in Dynamic Indoor Environments
Institute of Perception, Action and BehaviourOur dissertation concerns robotic navigation in dynamic indoor environments using
image-based visual homing. Image-based visual homing infers the direction to a
goal location S from the navigator’s current location C using the similarity between
panoramic images IS and IC captured at those locations. There are several ways to
compute this similarity. One of the contributions of our dissertation is to identify a
robust image similarity measure – mutual image information – to use in dynamic indoor
environments. We crafted novel methods to speed the computation of mutual
image information with both parallel and serial processors and demonstrated that these
time-savers had little negative effect on homing success. Image-based visual homing
requires a homing agent tomove so as to optimise themutual image information signal.
As the mutual information signal is corrupted by sensor noise we turned to the stochastic
optimisation literature for appropriate optimisation algorithms. We tested a number
of these algorithms in both simulated and real dynamic laboratory environments and
found that gradient descent (with gradients computed by one-sided differences) works
best
Engineering evolutionary control for real-world robotic systems
Evolutionary Robotics (ER) is the field of study concerned with the application
of evolutionary computation to the design of robotic systems. Two main
issues have prevented ER from being applied to real-world tasks, namely scaling to
complex tasks and the transfer of control to real-robot systems. Finding solutions
to complex tasks is challenging for evolutionary approaches due to the bootstrap
problem and deception. When the task goal is too difficult, the evolutionary process
will drift in regions of the search space with equally low levels of performance
and therefore fail to bootstrap. Furthermore, the search space tends to get rugged
(deceptive) as task complexity increases, which can lead to premature convergence.
Another prominent issue in ER is the reality gap. Behavioral control is typically
evolved in simulation and then only transferred to the real robotic hardware when
a good solution has been found. Since simulation is an abstraction of the real
world, the accuracy of the robot model and its interactions with the environment
is limited. As a result, control evolved in a simulator tends to display a lower
performance in reality than in simulation.
In this thesis, we present a hierarchical control synthesis approach that enables
the use of ER techniques for complex tasks in real robotic hardware by mitigating
the bootstrap problem, deception, and the reality gap. We recursively decompose
a task into sub-tasks, and synthesize control for each sub-task. The individual
behaviors are then composed hierarchically. The possibility of incrementally
transferring control as the controller is composed allows transferability issues to
be addressed locally in the controller hierarchy. Our approach features hybridity,
allowing different control synthesis techniques to be combined. We demonstrate
our approach in a series of tasks that go beyond the complexity of tasks where ER
has been successfully applied. We further show that hierarchical control can be applied
in single-robot systems and in multirobot systems. Given our long-term goal
of enabling the application of ER techniques to real-world tasks, we systematically
validate our approach in real robotic hardware. For one of the demonstrations in
this thesis, we have designed and built a swarm robotic platform, and we show the
first successful transfer of evolved and hierarchical control to a swarm of robots
outside of controlled laboratory conditions.A Robótica Evolutiva (RE) é a área de investigação que estuda a aplicação de
computação evolutiva na conceção de sistemas robóticos. Dois principais desafios
têm impedido a aplicação da RE em tarefas do mundo real: a dificuldade em solucionar
tarefas complexas e a transferência de controladores evoluídos para sistemas
robóticos reais. Encontrar soluções para tarefas complexas é desafiante para as
técnicas evolutivas devido ao bootstrap problem e à deception. Quando o objetivo
é demasiado difícil, o processo evolutivo tende a permanecer em regiões do espaço
de procura com níveis de desempenho igualmente baixos, e consequentemente não
consegue inicializar. Por outro lado, o espaço de procura tende a enrugar à medida
que a complexidade da tarefa aumenta, o que pode resultar numa convergência
prematura. Outro desafio na RE é a reality gap. O controlo robótico é tipicamente
evoluído em simulação, e só é transferido para o sistema robótico real quando uma
boa solução tiver sido encontrada. Como a simulação é uma abstração da realidade,
a precisão do modelo do robô e das suas interações com o ambiente é limitada,
podendo resultar em controladores com um menor desempenho no mundo real.
Nesta tese, apresentamos uma abordagem de síntese de controlo hierárquica
que permite o uso de técnicas de RE em tarefas complexas com hardware robótico
real, mitigando o bootstrap problem, a deception e a reality gap. Decompomos
recursivamente uma tarefa em sub-tarefas, e sintetizamos controlo para cada subtarefa.
Os comportamentos individuais são então compostos hierarquicamente.
A possibilidade de transferir o controlo incrementalmente à medida que o controlador
é composto permite que problemas de transferibilidade possam ser endereçados
localmente na hierarquia do controlador. A nossa abordagem permite
o uso de diferentes técnicas de síntese de controlo, resultando em controladores
híbridos. Demonstramos a nossa abordagem em várias tarefas que vão para além
da complexidade das tarefas onde a RE foi aplicada. Também mostramos que o
controlo hierárquico pode ser aplicado em sistemas de um robô ou sistemas multirobô.
Dado o nosso objetivo de longo prazo de permitir o uso de técnicas de
RE em tarefas no mundo real, concebemos e desenvolvemos uma plataforma de
robótica de enxame, e mostramos a primeira transferência de controlo evoluído e
hierárquico para um exame de robôs fora de condições controladas de laboratório.This work has been supported by the Portuguese Foundation for Science
and Technology (Fundação para a Ciência e Tecnologia) under the grants
SFRH/BD/76438/2011, EXPL/EEI-AUT/0329/2013, and by Instituto de Telecomunicações
under the grant UID/EEA/50008/2013
Evolutionary and Computational Advantages of Neuromodulated Plasticity
The integration of modulatory neurons into evolutionary artificial neural networks is proposed here. A model of modulatory neurons was devised to describe a plasticity mechanism at the low level of synapses and neurons. No initial assumptions were made on the network structures or on the system level dynamics. The work of this thesis studied the outset of high level system dynamics that emerged employing the low level mechanism of neuromodulated plasticity. Fully-fledged control networks were designed by simulated evolution: an evolutionary algorithm could evolve networks with arbitrary size and topology using standard and modulatory neurons as building blocks. A set of dynamic, reward-based environments was implemented with the purpose of eliciting the outset of learning and memory in networks. The evolutionary time and the performance of solutions were compared for networks that could or could not use modulatory neurons. The experimental results demonstrated that modulatory neurons provide an evolutionary advantage that increases with the complexity of the control problem. Networks with modulatory neurons were also observed to evolve alternative neural control structures with respect to networks without neuromodulation. Different network topologies were observed to lead to a computational advantage such as faster input-output signal processing. The evolutionary and computational advantages induced by modulatory neurons strongly suggest the important role of neuromodulated plasticity for the evolution of networks that require temporal neural dynamics, adaptivity and memory functions
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