Evolutionary design and behavior analysis of neuromodulatory neural networks for mobile robots control
Evolutionary design and behavior analysis of neuromodulatory neural networks for mobile robots control
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DOI：
10.1016/j.asoc.2005.05.004
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发表时间：
2007-01-01
影响因子：
8.7
通讯作者：
Kondo, Toshiyuki
中科院分区：
文献类型：
--
作者：
Kondo, Toshiyuki
进化机器人是设计具有复杂特性的机器人控制器的一种有效方法。在大多数ER研究中，机器人的感觉-运动映射被表示为人工神经网络，并且它们的连接权重（有时是网络的结构）可以通过使用进化计算在参数空间中被优化。然而，一般来说，进化的神经控制器可能是脆弱的，在没有经验的环境中，特别是在真实的世界中，因为进化优化过程将在理想化的模拟器中执行。这被称为模拟世界和真实的世界之间的差距问题。为了克服这一点，作者专注于在模拟环境中进化在线学习能力而不是权重参数。近年来的生物学研究发现，在昆虫和甲壳类动物的真实的神经系统中，存在着各种各样的在线适应能力，并且有多种神经调质（neuromodulators，NM）在调节网络特性（如激活/阻断/改变突触连接）方面发挥着重要作用。在此基础上，提出了一种神经调节神经网络模型，并将其作为移动的机器人控制器。文中还讨论了进化神经调节网络的详细行为分析。(C)2005 Elsevier B. V.保留所有权利。
Evolutionary Robotics (ER) is one of promising approaches to design robot controllers which essentially have complicated and/or complex properties. In most ER research, the sensory - motor mappings of robots are represented as artificial neural networks, and their connection weights ( and sometimes the structure of the networks) can be optimized in the parameter spaces by using evolutionary computation. However, generally, the evolved neural controllers could be fragile in unexperienced environments, especially in real worlds, because the evolutionary optimization processes would be executed in idealized simulators. This is known as the gap problem between the simulated and real worlds. To overcome this, the author focused on evolving an on-line learning ability instead of weight parameters in a simulated environment. According to recent biological findings, actually, the kinds of on-line adaptation abilities can be found in real nervous systems of insects and crustaceans, and it is also known that a variety of neuromodulators (NMs) play crucial roles to regulate the network characteristics (i.e. activating/ blocking/changing of synaptic connections). Based on this, a neuromodulatory neural network model was proposed and it was utilized as a mobile robot controller. In the paper, the detail behavior analysis of the evolved neuromodulatory neural network is also discussed. (C) 2005 Elsevier B.V. All rights reserved.