Swarm robotic systems are a type of multi-robot systems which generally consist of many homogeneous autonomous robots without any type of global controllers. Swarm robotics aims at designing desired collective behavio...
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ISBN:
(纸本)9781467359252
Swarm robotic systems are a type of multi-robot systems which generally consist of many homogeneous autonomous robots without any type of global controllers. Swarm robotics aims at designing desired collective behaviors through many interactions with other robots or their environment. Since a robotic swarm is controlled by an emergent way such as a result of self-organization by using robot learning or artificial evolution, no method has been known to grasp the macroscopic collective behavior in a practical sense, according to the best of our knowledge. In this paper, we propose a novel method for analyzing the collective behavior by introducing the concept of behavioral sequence, which stems from ethology. Analysis about behavioral sequence reveals the transition of robot's action from the viewpoint of specialization and helps us to understand the role of subgroups in a robotic swarm. Applying this method, we observe collective behavior in a foraging task of autonomous mobile robots.
After bipedal locomotion, dance is one of the most commonly studied behaviours for researchers seeking to replicate human-like motion in humanoid robots. Many of the methods employed involve direct interaction with, o...
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ISBN:
(纸本)9781479906529
After bipedal locomotion, dance is one of the most commonly studied behaviours for researchers seeking to replicate human-like motion in humanoid robots. Many of the methods employed involve direct interaction with, or imitation of, human participant(s). For example, the generation of dance movements using interactive evolutionary computation (IEC) involves the replacement of an objective fitness function with the subjective evaluations of human observer(s). In this paper we present an alternative approach to the synthesis of humanoid robot dance using non-interactive evolutionary computation (non-IEC) methods. We propose a novel fitness function for the evolution of robotic dance, and we present initial results of the application of this evolutionary process to the generation of dance patterns for the 18-DOF Bioloid humanoid robot. We conclude that even without the presence of a human or humans in the evolutionary loop, it is possible to produce surprisingly lifelike and novel dances using this approach.
This article an evolutionary robot is implemented with the aim to stand upright and level as possible. evolutionary Robots are characterised by performing automated tasks trying to reach an objective function, which i...
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ISBN:
(纸本)9781509003235
This article an evolutionary robot is implemented with the aim to stand upright and level as possible. evolutionary Robots are characterised by performing automated tasks trying to reach an objective function, which is the key to good performance. The evolution of the system is made possible by a genetic algorithm, which based on natural biological evolution process, achieved through several generations a possible solution. The evolutionary robot was physically implemented with sensors and actuators, also the genetic algorithm was embedded in a microcontroller.
Traditional robots locomotion relied on the kinetic analysis to design a set of instructions to control the robot. When the surrounding environment changed, human had to develop a code to deal with the changes in the ...
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ISBN:
(纸本)9781614994848;9781614994831
Traditional robots locomotion relied on the kinetic analysis to design a set of instructions to control the robot. When the surrounding environment changed, human had to develop a code to deal with the changes in the environment. Using evolutionary algorithm to solve this problem, evolutionary robot can adapt its behavior to fit the current environment immediately, which is the advantage of evolutionary robots in saturations that no human can help. evolutionary robot research has become an interesting topic recently and this research specifically focuses on evolution and learning. Evolution is the adaptation of robots to the environment. Learning is a task-oriented process whereby the robot gains the ability to achieve a given goal in the environment. In this paper, we apply traditional GA (Genetic Algorithm) and design the BRMA (Biomorphic Robot Memetic Algorithm) to control the robot. Our biomorphic robots have four legs and each leg has several joints. We also test the algorithms on a partially breakdown robot. Our study is a multi-objective evolutionary task since the robot has to evolve to fit several indexes. In our experiments, we set up a beacon light as a target, and the robot evolves to move quickly and smoothly toward the target. We adopt online evolutionary algorithms and test them on the quadrupedal robot. The experimental results show that the robot, from totally random behaviors, can adjust its actions to move quickly and smoothly toward the target.
Recent developments in robotics demonstrated that bioinspiration and embodiement are powerful tools to achieve robust behavior in presence of little control. In this context morphological design is usually performed b...
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ISBN:
(纸本)9781450334723
Recent developments in robotics demonstrated that bioinspiration and embodiement are powerful tools to achieve robust behavior in presence of little control. In this context morphological design is usually performed by humans, following a set of heuristic principles: in general this can be limiting, both from an engineering and an artificial life perspectives. In this work we thus suggest a different approach, leveraging evolutionary techniques. The case study is the one of improving the locomotion capabilities of an existing bioinspired robot. First, we explore the behavior space of the robot to discover a number of qualitatively different morphology-enabled behaviors, from whose analysis design indications are gained. The suitability of novelty search a recent open-ended evolutionary algorithm for this intended purpose is demonstrated. Second, we show how it is possible to condense such behaviors into a reconfigurable robot capable of online morphological adaptation (morphosis, morphing). Examples of successful morphing are demonstrated, in which changing just one morphological parameter entails a dramatic change in the behavior: this is promising for a future robot design. The approach here adopted represents a novel computed-aided, bioinspired, design paradigm, merging human and artificial creativity. This may result in interesting implications also for artificial life, having the potential to contribute in exploring underwater locomotion "as-it-could-be".
We are interested in the construction of ecological models of the evolution of learning behavior using methodological tools developed in the field of evolutionary robotics. In this article, we explore the applicabilit...
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We are interested in the construction of ecological models of the evolution of learning behavior using methodological tools developed in the field of evolutionary robotics. In this article, we explore the applicability of integrated (i.e., nonmodular) neural networks with fixed connection weights and simple "leaky-integrator" neurons as controllers for autonomous learning robots. In contrast to Yamauchi and Beer (1994a), we show that such a control system is capable of integrating reactive and learned behaviour without explicitly needing hand-designed modules, dedicated to a particular behavior, or an externally introduced reinforcement signal. In our model, evolutionary and ecological contingencies structure the controller and the behavioral responses of the robot. This allows us to concentrate on examining the conditions under which learning behavior evolves.
Morphological evolution in a robotic system produces novel robot bodies after each reproduction event. This implies the necessity for lifetime learning so that newborn robots can acquire a controller that tits their b...
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ISBN:
(纸本)9781538692769
Morphological evolution in a robotic system produces novel robot bodies after each reproduction event. This implies the necessity for lifetime learning so that newborn robots can acquire a controller that tits their body. Thus, we obtain a system where evolution and learning are combined. This combination can be Darwinian or Lamarckian and in this paper, we compare the two. In particular, we investigate the evolved \morphologies under these regimes for modular robots evolved for good locomotion. Using eight quantifiable morphological descriptors to characterize the physical properties of robots we compare the regions of attraction in the resulting 8-dimensional space. The results show prominent differences in symmetry, size, proportion, and coverage.
We present our work on the training of robotised architectural components of intelligent buildings, focusing on main architectural components and features such as facades, roofs and partitions. The parameters governin...
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ISBN:
(纸本)9783319165486;9783319165493
We present our work on the training of robotised architectural components of intelligent buildings, focusing on main architectural components and features such as facades, roofs and partitions. The parameters governing such components may be either quantitative (such as temperature, humidity, configuration of the elements) or qualitative (such as ergonomics and aesthetics), which cannot easily be described by mathematical parameters. Due to their complexity,it is often impossible -or at least impractical, to hardcode suitable controllers for such robotised structures. Thus, we propose the use of Artificial Intelligence learning techniques, concretely evolutionary Algorithms, so that the user can teach the robotised components how to behave in response to changing environmental conditions or user preferences. This idea is tested on an intelligent rooftop with variable geometry, that learns optimal configurations with respect to ambient light during training sessions.
In this paper, we reintroduce evolutionary algorithms into Auto-MoDe, an automatic design approach which optimizes behavioural modules into a probabilistic finite automaton. We evaluate three approaches, with differen...
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ISBN:
(纸本)9781450392686
In this paper, we reintroduce evolutionary algorithms into Auto-MoDe, an automatic design approach which optimizes behavioural modules into a probabilistic finite automaton. We evaluate three approaches, with different encodings of the probabilistic finite automaton phenotype, and observe their performances. This work opens modular designs to more advanced evolutionary robotics methods, such as novelty search and embodied evolution.
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