Vector-valued function estimation by grammatical evolution for autonomous robot control
Vector-valued function estimation by grammatical evolution for autonomous robot control
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DOI：
10.1016/j.ins.2013.09.044
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发表时间：
2014-02
期刊：
影响因子：
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通讯作者：
R. Burbidge;Myra S. Wilson
中科院分区：
文献类型：
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作者：
R. Burbidge;Myra S. Wilson
自主移动机器人需要一个强大的车载控制器，在动态环境中做出智能响应。当前的解决方案往往会导致不必要的复杂解决方案，这些解决方案只在利基环境中有效。进化技术，如遗传编程(GP)，可以成功地用于对控制器进行自动编程，根据机器人对世界的经验，将显式或隐式人类设计标准引起的限制降至最低。语法进化(GE)是一种新的进化算法，已被应用于各种问题，特别是GP已经解决过的问题。我们将机器人控制描述为向量值函数估计，并提出了一种新的向量值函数生成文法。考虑到交叉算子，我们提出了GE在向量值函数估计中应用的设计准则，以及满足该准则的第二种新的生成文法。在Khepera机器人的模拟任务上，对这些文法在向量值函数估计中的适用性进行了经验评估。
An autonomous mobile robot requires a robust onboard controller that makes intelligent responses in dynamic environments. Current solutions tend to lead to unnecessarily complex solutions that only work in niche environments. Evolutionary techniques such as genetic programming (GP) can successfully be used to automatically program the controller, minimizing the limitations arising from explicit or implicit human design criteria, based on the robot’s experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been applied to various problems, particularly those for which GP has performed. We formulate robot control as vector-valued function estimation and present a novel generative grammar for vector-valued functions. A consideration of the crossover operator leads us to propose a design criterion for the application of GE to vector-valued function estimation, along with a second novel generative grammar which meets this criterion. The suitability of these grammars for vector-valued function estimation is assessed empirically on a simulated task for the Khepera robot.