This paper introduces a new type of artificial neural network (GasNets) and shows that it is possible to use evolutionary computing techniques to find robot controllers based on them. The controllers are built from ne...
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This paper introduces a new type of artificial neural network (GasNets) and shows that it is possible to use evolutionary computing techniques to find robot controllers based on them. The controllers are built from networks inspired by the modulatory effects of freely diffusing gases, especially nitric oxide, in real neuronal networks. evolutionary robotics techniques were used to develop control networks and visual morphologies to enable a robot to achieve a target discrimination task under very noisy lighting conditions. A series of evolutionary runs with and without the gas modulation active demonstrated that networks incorporating modulation by diffusing gases evolved to produce successful controllers considerably faster than networks without this mechanism. GasNets also consistently achieved evolutionary success in far fewer evaluations than were needed when using more conventional connectionist style networks.
Great interest has been shown in the application of the principles of artificial life to physically embedded systems such as mobile robots, computer networks, home devices able continuously and autonomously to adapt t...
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Great interest has been shown in the application of the principles of artificial life to physically embedded systems such as mobile robots, computer networks, home devices able continuously and autonomously to adapt their behavior to changes of the environments. At the same time researchers have been working on the development of evolvable hardware, and new integrated circuits that are able to adapt their hardware autonomously and in real time in a changing environment. This article describes the navigation task for a real mobile robot and its implementation on evolvable hardware. The robot must track a colored ball, while avoiding obstacles in an environment that is unknown and dynamic. Although a model-free evolution method is not feasible for real-world applications due to the sheer number of possible interactions with the environment, we show that a model-based evolution can reduce these interactions by two orders of magnitude, even when some of the robot's sensors are blinded, thus allowing us to apply evolutionary processes online to obtain a self-adaptive tracking system in the real world, when the implementation is accelerated by the utilization of evolvable hardware.
Sensors and effecters determine how events in the world at large are related to the internal informational states of organisms and robotic devices. Sensors determine what kinds of distinctions (perceptual categories, ...
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ISBN:
(纸本)0780344235
Sensors and effecters determine how events in the world at large are related to the internal informational states of organisms and robotic devices. Sensors determine what kinds of distinctions (perceptual categories, features, primitives) can be made on the environment. By "evolving the sensors" perceptual repertoires can be adaptively altered and/or enlarged. To the extent that devices can adaptively choose their own feature primitives for themselves, they gain a greater measure of "epistemic autonomy" vis-a-vis their designers. Such devices are useful in ill-defined situations where the designer does not know a priori what feature primitives are adequate or optimum for solving a particular task. Several general strategies for adaptively altering or augmenting sensor function are proposed: 1) prosthesis: adaptive fabrication of new front-ends for existing sensors (e.g. telescopes), 2) active sensing: using motor-actions to alter what is sensed through interaction (poking, pushing, bending), 3) sensory evolution: adaptive construction of entirely new sensors (adaptive antibody construction, Gordon Pask's electrochemical device) and 4) internalized sensing: "bringing the world into the device" by creating internal, analog representations of the world out of which internal sensors extract newly-relevant properties (perceptual learning). Since many neural sensory representations appear to be analog and iconic in nature, neural assemblies can be adaptively formed to function as internal sensors that can switch behavior according to new perceptual categories.
The pitfalls of naive robot simulations have been recognised for areas such as evolutionary robotics. It has been suggested that carefully validated simulations with a proper treatment of noise may overcome these prob...
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ISBN:
(纸本)3540594965
The pitfalls of naive robot simulations have been recognised for areas such as evolutionary robotics. It has been suggested that carefully validated simulations with a proper treatment of noise may overcome these problems. This paper reports the results of experiments intended to test some of these claims. A simulation was constructed of a two-wheeled Khepera robot with IR and ambient light sensors. This included detailed mathematical models of the robot-environment interaction dynamics with empirically determined parameters. Artificial evolution was used to develop recurrent dynamical network controllers for the simulated robot, for obstacle-avoidance and light-seeking tasks, using different levels of noise in the simulation. The evolved controllers were down-loaded onto the real robot and the correspondence between behaviour in simulation and in reality was tested. The level of correspondence varied according to how much noise was used in the simulation, with very good results achieved when realistic quantities were applied. It has been demonstrated that it is possible to develop successful robot controllers in simulation that generate almost identical behaviours in reality, at least for a particular class of robot-environment interaction dynamics.
Automatic development and learning of robot soccer strategies are presented in this paper. It is shown that using a novel control system, it is possible to allow teams of robots to acquire strategies for playing a bet...
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ISBN:
(纸本)0780336135
Automatic development and learning of robot soccer strategies are presented in this paper. It is shown that using a novel control system, it is possible to allow teams of robots to acquire strategies for playing a better game of soccer through successive generations, utilizing simulated evolution. A number of soccer techniques, as developed through robot games, are discussed. The mechanism presented in the paper is suitable for other tasks requiring multiple robots to interact and cooperate in teams.
Recently, a new approach involving a form of simulated evolution has been proposed to build autonomous robots. However, it is still not clear if this approach is adequate for real life problems. In this paper we show ...
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Recently, a new approach involving a form of simulated evolution has been proposed to build autonomous robots. However, it is still not clear if this approach is adequate for real life problems. In this paper we show how control systems that perform a non-trivial sequence of behaviors can be obtained with this methodology by "canalizing" the evolutionary process in the right direction. In the experiment described in the paper, a mobile robot was successfully trained to keep clear an arena surrounded by walls by locating, recognizing, and grasping "garbage" objects and by taking collected objects outside the arena. The controller of the robot was evolved in simulation and then downloaded and tested on the real robot. We also show that while a given amount of supervision may canalize the evolutionary process in the right direction the addition of unnecessary constraints can delay the evolution of the desired behavior. Copyright (C) 1997 Elsevier Science B.V.
The contribution of this paper is the introduction of an abstract Sensor-Actuator pair to the subsumption architecture of robots introduced by R.A. Brooks. The perceiving side of this pair derives from J.J. Gibson'...
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ISBN:
(纸本)078034054X
The contribution of this paper is the introduction of an abstract Sensor-Actuator pair to the subsumption architecture of robots introduced by R.A. Brooks. The perceiving side of this pair derives from J.J. Gibson's affordance, which is a form of grasping of situations involving perceived objects. This study is part of a new form of evolutionary robotics called cognitive robotics. It considers a new context for a classical form of learning, namely, habitation. The inspiration for the form of affordances described in this paper comes from Heidegger's notion of the convergence of the concurrent activities of building, dwelling, and thinking. In some sense, building and dwelling are at the threshold of thinking. A brief description of the form and functioning of abstract S-A pairs is given.
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