In the development of autonomous robots, control program learning systems are important since they allow the robots to adapt to changes in their surroundings. evolutionary Computation (EC) is a method that is used wid...
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ISBN:
(纸本)9781467317146
In the development of autonomous robots, control program learning systems are important since they allow the robots to adapt to changes in their surroundings. evolutionary Computation (EC) is a method that is used widely in learning systems. In previous research, we used a Cyclic Genetic Algorithm (CGA), a form of EC, to evolve a simulated predator robot to test the effectiveness of a learning system in the predator/prey problem. The learned control program performed search, chase, and capture behavior using 64 sensor states relative to the nearest obstacle and the target, a simulated prey robot. In this paper, we present the results of a new set of trials, which were tested on the actual robots. The actual robots successfully performed desired behaviors, showing the effectiveness of the CGA learning system.
Transferring virtual robotic designs into physical robots has become possible with the development of 3D printers. Accurately simulating the performance of real robots in a virtual environment requires modeling a vari...
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ISBN:
(纸本)9781450311779
Transferring virtual robotic designs into physical robots has become possible with the development of 3D printers. Accurately simulating the performance of real robots in a virtual environment requires modeling a variety of conditions, including the physical composition of the robots themselves. In this paper, we investigate how modeling material flexibility through the use of a passive joint affects the resulting arm morphology and gait of a crawling virtual robot. Results indicate that flexibility can be a beneficial characteristic of robotic morphology design while also providing insight into the benefits of modeling material properties in a simulation environment.
Compared with fixed morphology robotic systems, self-reconfigurable modular (SRM) robots can reconfigure themselves to form a variety of morphologies, and carry on various types of motions. Recently, some co-evolution...
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ISBN:
(纸本)9781450311786
Compared with fixed morphology robotic systems, self-reconfigurable modular (SRM) robots can reconfigure themselves to form a variety of morphologies, and carry on various types of motions. Recently, some co-evolutionary approaches have been proposed to co-evolve the robot morphology and associated controller simultaneously for locomotion tasks. However, these co-evolution approaches don't consider some physical limitations of SRM robots and usually request longer evolution process due to extensive searching space. To address these issues, we proposed a species-based co-evolution (S-CoE) algorithm. The S-CoE algorithm is applied on a simulated modular robot system and evaluated under two testing scenarios.
This paper presents the use of a bio-inspired method in robotics research. We discuss the Particle swarm optimization (PSO) for two ground robots: an omnidirectional rolling robot and a biped walker robot. For the whe...
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ISBN:
(纸本)9781467347662
This paper presents the use of a bio-inspired method in robotics research. We discuss the Particle swarm optimization (PSO) for two ground robots: an omnidirectional rolling robot and a biped walker robot. For the wheeled robot, we studied the navigation in a flat environment with eventual obstacles. Thus, for the biped robot, we applied on the gesture of the straight walk. The PSO algorithm shows that is very tempting for contribution in the evolutionary robotics researches.
evolutionary robotics (ER) is a methodology that uses evolutionary computation to develop controllers for autonomous robots. The existing works focus on single robot systems or physically homogenous multi-robot teams,...
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evolutionary robotics (ER) is a methodology that uses evolutionary computation to develop controllers for autonomous robots. The existing works focus on single robot systems or physically homogenous multi-robot teams, although physical heterogeneousness is more prevalent in the real world. It is therefore instructive to examine whether cooperative behaviours can be synthesized using artificial evolution for a team of physically heterogeneous robots. This paper makes an important contribution in answering the question of whether robots with distinct capabilities can synthesize their control strategies to accommodate their own capabilities without human intervention. We present an empirical analysis of the collaboration mechanisms and suggest guidelines about how to choose appropriate evolution methods. Simulated experiments with a team of e-puck robots show that evolution can lead to effective controllers for robots with distinct capabilities.
We propose a novel approach for transferring evolved control systems from a simulated environment to a real robot. Multiple dynamic simulation systems are simultaneously employed to provide a valid range of simulation...
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ISBN:
(纸本)9781467318716
We propose a novel approach for transferring evolved control systems from a simulated environment to a real robot. Multiple dynamic simulation systems are simultaneously employed to provide a valid range of simulation variance that can be exploited to generate robust controllers in a purely virtual environment. These controllers can then be directly transferred to a physical robot.
It has become increasingly popular to employ evolutionary algorithms to solve problems in different domains, and parallel models have been widely used for performance enhancement. Instead of using parallel computing f...
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It has become increasingly popular to employ evolutionary algorithms to solve problems in different domains, and parallel models have been widely used for performance enhancement. Instead of using parallel computing facilities or public computing systems to speed up the computation, we propose to implement parallel evolutionary computation models on networked personal computers (PCs) that are locally available and manageable. To realize the parallelism, a multi-agent system is presented in which mobile agents play the major roles to carry the code and move from machine to machine to complete the computation dynamically. To evaluate the proposed approach, we use our multi-agent system to solve two types of time-consuming applications. Different kinds of experiments were conducted to assess the developed system, and the preliminary results show its promise and efficiency. (C) 2010 Elsevier Ltd. All rights reserved.
Populations of simulated agents controlled by dynamical neural networks are trained by artificial evolution to access linguistic instructions and to execute them by indicating, touching, or moving specific target obje...
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Populations of simulated agents controlled by dynamical neural networks are trained by artificial evolution to access linguistic instructions and to execute them by indicating, touching, or moving specific target objects. During training the agent experiences only a subset of all object/action pairs. During postevaluation, some of the successful agents proved to be able to access and execute also linguistic instructions not experienced during training. This owes to the development of a semantic space, grounded in the sensory motor capability of the agent and organized in a systematized way in order to facilitate linguistic compositionality and behavioral generalization. Compositionality seems to be underpinned by a capability of the agents to access and execute the instructions by temporally decomposing their linguistic and behavioral aspects into their constituent parts (i.e., finding the target object and executing the required action). The comparison between two experimental conditions, in one of which the agents are required to ignore rather than to indicate objects, shows that the composition of the behavioral set significantly influences the development of compositional semantic structures.
In this paper, we study the problem of exploration and navigation in an unknown environment from an evolutionary swarm robotics perspective. In other words, we search for an efficient exploration and navigation strate...
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In this paper, we study the problem of exploration and navigation in an unknown environment from an evolutionary swarm robotics perspective. In other words, we search for an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to cope with the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. The collective strategy is synthesised through evolutionary robotics techniques, and is based on the emergence of a dynamic structure formed by the robots moving back and forth between the two target areas. Due to this structure, each robot is able to maintain the right heading and to efficiently navigate between the two areas. The evolved behaviour proved to be effective in finding the shortest path, adaptable to new environmental conditions, scalable to larger groups and larger environment size, and robust to individual failures.
evolutionary robotics (ER) is a powerful approach for the automatic synthesis of robot controllers, as it requires little a priori knowledge about the problem to be solved in order to obtain good solutions. This is pa...
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evolutionary robotics (ER) is a powerful approach for the automatic synthesis of robot controllers, as it requires little a priori knowledge about the problem to be solved in order to obtain good solutions. This is particularly true for collective and swarm robotics, in which the desired behavior of the group is an indirect result of the control and communication rules followed by each individual. However, the experimenter must make several arbitrary choices in setting up the evolutionary process, in order to define the correct selective pressures that can lead to the desired results. In some cases, only a deep understanding of the obtained results can point to the critical aspects that constrain the system, which can be later modified in order to re-engineer the evolutionary process toward better solutions. In this article, we discuss the problem of engineering the evolutionary machinery that can lead to the desired result in the swarm robotics context. We also present a case study about self-organizing synchronization in a swarm of robots, in which some arbitrarily chosen properties of the communication system hinder the scalability of the behavior to large groups. We show that by modifying the communication system, artificial evolution can synthesize behaviors that scale properly with the group size.
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