1,633 research outputs found
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
Open-Ended Evolutionary Robotics: an Information Theoretic Approach
This paper is concerned with designing self-driven fitness functions for
Embedded Evolutionary Robotics. The proposed approach considers the entropy of
the sensori-motor stream generated by the robot controller. This entropy is
computed using unsupervised learning; its maximization, achieved by an on-board
evolutionary algorithm, implements a "curiosity instinct", favouring
controllers visiting many diverse sensori-motor states (sms). Further, the set
of sms discovered by an individual can be transmitted to its offspring, making
a cultural evolution mode possible. Cumulative entropy (computed from ancestors
and current individual visits to the sms) defines another self-driven fitness;
its optimization implements a "discovery instinct", as it favours controllers
visiting new or rare sensori-motor states. Empirical results on the benchmark
problems proposed by Lehman and Stanley (2008) comparatively demonstrate the
merits of the approach
Evolution of collective behaviors for a real swarm of aquatic surface robots
Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.info:eu-repo/semantics/publishedVersio
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
Environment induced emergence of collective behaviour in evolving swarms with limited sensing
Designing controllers for robot swarms is challenging, because human
developers have typically no good understanding of the link between the details
of a controller that governs individual robots and the swarm behavior that is
an indirect result of the interactions between swarm members and the
environment. In this paper we investigate whether an evolutionary approach can
mitigate this problem. We consider a very challenging task where robots with
limited sensing and communication abilities must follow the gradient of an
environmental feature and use Differential Evolution to evolve a neural network
controller for simulated robots. We conduct a systematic study to measure the
flexibility and scalability of the method by varying the size of the arena and
number of robots in the swarm. The experiments confirm the feasibility of our
approach, the evolved robot controllers induced swarm behavior that solved the
task. We found that solutions evolved under the harshest conditions (where the
environmental clues were the weakest) were the most flexible and that there is
a sweet spot regarding the swarm size. Furthermore, we observed collective
motion of the swarm, showcasing truly emergent behavior that was not
represented in- and selected for during evolution.Comment: (1) Three authors contributed equally to this researc
Information driven self-organization of complex robotic behaviors
Information theory is a powerful tool to express principles to drive
autonomous systems because it is domain invariant and allows for an intuitive
interpretation. This paper studies the use of the predictive information (PI),
also called excess entropy or effective measure complexity, of the sensorimotor
process as a driving force to generate behavior. We study nonlinear and
nonstationary systems and introduce the time-local predicting information
(TiPI) which allows us to derive exact results together with explicit update
rules for the parameters of the controller in the dynamical systems framework.
In this way the information principle, formulated at the level of behavior, is
translated to the dynamics of the synapses. We underpin our results with a
number of case studies with high-dimensional robotic systems. We show the
spontaneous cooperativity in a complex physical system with decentralized
control. Moreover, a jointly controlled humanoid robot develops a high
behavioral variety depending on its physics and the environment it is
dynamically embedded into. The behavior can be decomposed into a succession of
low-dimensional modes that increasingly explore the behavior space. This is a
promising way to avoid the curse of dimensionality which hinders learning
systems to scale well.Comment: 29 pages, 12 figure
A Framework for Automatic Behavior Generation in Multi-Function Swarms
17 USC 105 interim-entered record; under review.Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of Radio Frequency (RF) emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed
An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots
The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so
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