In this paper we introduce the development of dedicated hardware capable of controlling autonomous micro-scale robots for fault detection/repair in complex inaccessible fluidic environments. This work is part of a Eur...
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ISBN:
(纸本)0780385667
In this paper we introduce the development of dedicated hardware capable of controlling autonomous micro-scale robots for fault detection/repair in complex inaccessible fluidic environments. This work is part of a European Union funded project entitled SOCIAL, (Self-Organized societies of Connectionist Intelligent Agents capable of Learning No IST-2001-38911). The project's aim is to produce a swarm of micro-scale (5cm(3)) autonomous robots that, through indirect communication, are capable of achieving fault detection and reparation in difficult, challenging and inaccessible environments. An application benchmark for this project is the on-line monitoring and maintenance of underwater pipelines like those found in the oil industry or desalination plants. The robots would move through the fluidic environment, continuously sensing for corrosion and scaling faults in the pipeline.
We describe the results of a set of evolutionary experiments in which a simulated robotic arm provided with a two-fingered hand has to reach and grasp objects with different shapes and orientations on the basis of sim...
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We describe the results of a set of evolutionary experiments in which a simulated robotic arm provided with a two-fingered hand has to reach and grasp objects with different shapes and orientations on the basis of simple tactile information. The results that we obtained are encouraging and demonstrate that the problem of grasping objects with characteristics that vary within a certain range can be solved by producing rather simple forms of behavior. These forms of behavior exploit emergent characteristics of the interaction between the body of the robot, its control system, and the environment. In particular we show that evolved individuals do not try to keep the environment stable, but rather push and pull the objects;thus, they produce a dynamic in the environment and exploit the interaction between the body of the robot and the dynamic environment to master different environmental conditions with similar control strategies.
In recent years, evolutionary algorithms have been proposed to synthesize robot controllers. However, admittedly, it is not satisfactory just to evolve the control system, because the performance of the control system...
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In recent years, evolutionary algorithms have been proposed to synthesize robot controllers. However, admittedly, it is not satisfactory just to evolve the control system, because the performance of the control system depends on other hardware parameters, the robot body plan. In this paper, an evolutionary framework is presented to evolve complete robot systems, including controllers and bodies, to achieve fitness-specified tasks. In order to assess the performance of the developed system, we use it to evolve controller and body plan together for a robot in a simulated environment to achieve an obstacle avoidance task. Experimental results show the promise of this hybrid evolutionary approach. In addition, the importance of simultaneously evolving controller and morphology is emphasized, and the distribution of body parameters in the morphological space is also studied.
This research develops methods of automating the production of behavioral robotics controllers. Population-based artificial evolution was employed to train neural network-based controllers to play a robotic version of...
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ISBN:
(纸本)0780382420
This research develops methods of automating the production of behavioral robotics controllers. Population-based artificial evolution was employed to train neural network-based controllers to play a robotic version of the team game Capture the Flag. The robot agents used processed video data for sensing their environment. To accommodate the 35 to 150 sensor inputs required, large neural networks of arbitrary connectivity and structure were evolved. An intra-population competitive genetic algorithm was used and selection at each generation was based on whether the different controllers won or lost games over the course of a tournament. This paper focuses on the evolutionary neural controller architecture. Evolved controllers were tested in a series of competitive games and transferred to real robots for physical verification.
The field of evolutionary robotics has demonstrated the ability to automatically design the morphology and controller of simple physical robots through synthetic evolutionary processes. However, it is not clear if var...
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The field of evolutionary robotics has demonstrated the ability to automatically design the morphology and controller of simple physical robots through synthetic evolutionary processes. However, it is not clear if variation-based search processes can attain the complexity of design necessary for practical engineering of robots. Here., we demonstrate an automatic design system that produces complex robots by exploiting the principles of regularity, modularity, hierarchy, and reuse. These techniques are already established principles of scaling in engineering design and have been observed in nature, but have not been broadly used in artificial evolution. We gain these advantages through the use of a generative representation, which combines a programmatic representation with an algorithmic process that compiles the representation into a detailed construction plan. This approach is shown to have two benefits: it can reuse components in regular and hierarchical ways, providing a systematic way to create more complex modules from simpler ones;and the evolved representations can capture intrinsic properties of the design space, so that variations in the representations move through the design space more effectively than equivalent-sized changes in a nongenerative representation. Using this system, we demonstrate for the first time the evolution and construction of modular, three-dimensional, physically locomoting robots, comprising many more components than previous work on body-brain evolution.
This article describes the simulation of distributed autonomous robots for search and rescue operations. The simulation system is utilized to perform experiments with various control strategies for the robot team and ...
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This article describes the simulation of distributed autonomous robots for search and rescue operations. The simulation system is utilized to perform experiments with various control strategies for the robot team and team organizations, evaluating the comparative performance of the strategies and organizations. The objective of the robot team is to, once deployed in an environment (floor-plan) with multiple rooms, cover as many rooms as possible. The simulated robots are capable of navigation through the environment, and can communicate using simple messages. The simulator maintains the world, provides each robot with sensory information, and carries out the actions of the robots. The simulator keeps track of the rooms visited by robots and the elapsed time, in order to evaluate the performance of the robot teams. The robot teams are composed of homogenous robots, i.e., identical control strategies are used to generate the behavior of each robot in the team. The ability to deploy. autonomous robots, as opposed to humans, in hazardous search and rescue missions could provide immeasurable benefits. (C) 2003 Elsevier Science Ltd. All rights reserved.
In this paper we introduce and apply the concept of local evolvability to investigate the behaviour of populations during evolutionary search. We focus on the evolution of GasNet neural network controllers for a robot...
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In this paper we introduce and apply the concept of local evolvability to investigate the behaviour of populations during evolutionary search. We focus on the evolution of GasNet neural network controllers for a robotic visual discrimination problem, showing that the evolutionary process undergoes long neutral fitness epochs. We show that the local evolvability properties of the search space surrounding a group of statistically neutral solutions do vary across the course of an evolutionary run, especially during periods of population takeover. However, once takeover is complete there is no evidence for further increase in local evolvability across fitness epochs. We also see no evidence for the neutral evolution of increased solution robustness, but show that this may be due to the ability of evolutionary algorithms to focus search on volumes of the fitness landscape with above average robustness. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.
This paper describes the development and testing of a new evolutionary robotics research test bed. The test bed consists of a colony of small computationally powerful mobile robots that use evolved neural network cont...
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ISBN:
(纸本)0780378601
This paper describes the development and testing of a new evolutionary robotics research test bed. The test bed consists of a colony of small computationally powerful mobile robots that use evolved neural network controllers and vision based sensors to generate team game-playing behaviors. The vision based sensors function by converting video images into range and object color data. Large evolvable neural network controllers use these sensor data to control mobile robots. The networks require 150 individual input connections to accommodate the processed video sensor data. Using evolutionary computing methods, the neural network based controllers were evolved to play the competitive team game Capture the Flag with teams of mobile robots. Neural controllers were evolved in simulation and transferred to real robots for physical verification. Sensor signals in the simulated environment are formatted to duplicate the processed real video sensor values rather than the raw video images. Robot controllers receive sensor signals and send actuator commands of the same format, whether they are driving physical robots in a real environment or simulated robots agents in an artificial environment. Evolved neural controllers can be transferred directly to the real mobile robots for testing and evaluation. Experimental results generated with this new evolutionary robotics research test bed are presented.
This paper presents a Genetic Algorithm (GA) approach to evolving robot behaviours. We use fuzzy logic controllers (FLCs) to design robot behaviours. The antecedents of the FLCs are pre-designed, while their consequen...
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ISBN:
(纸本)0780378660
This paper presents a Genetic Algorithm (GA) approach to evolving robot behaviours. We use fuzzy logic controllers (FLCs) to design robot behaviours. The antecedents of the FLCs are pre-designed, while their consequences are learned using a GA. The Sony quadruped robots are used to evaluate proposed approaches in the robotic football domain. Two behaviours, ball-chasing and position-reaching, are studied and implemented. An embodied evolution scheme is adopted, by which the robot autonomously evolves its behaviours based on a layered control architecture. The results show that the robot behaviours can be automatically acquired through the GA-based learning of FLCs.
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