进化型晶体管阵列机器人控制器

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Evolutionary Computation Pub Date : 2020-12-02 DOI:10.1162/evco_a_00272
Michael Garvie;Ittai Flascher;Andrew Philippides;Adrian Thompson;Phil Husbands
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引用次数: 1

摘要

首次使用现场可编程晶体管阵列(FPTA)直接在模拟硬件中开发机器人控制电路。控制器成功地为参与一系列视觉引导行为的物理机器人逐步进化,包括在目标对大多数位置都隐藏的复杂环境中找到目标。识别口头命令的电路也得到了发展,这些电路与控制器一起使用,以实现机器人的语音控制,触发行为切换。低质量的视觉传感器被故意用来测试进化模拟电路实时处理有噪声的不确定数据的能力。视觉特征与控制器共同进化,以集成的方式自动实现降维和特征提取与选择。提出了一种在视觉环境下对机器人进行仿真的有效方法。这允许在连接到FPTA的模拟中对控制器进行评估。控制器然后无缝地转移到现实世界。电路复制问题也在实验中得到了解决,在实验中,电路被进化为能够在FPTA的多个区域中正确工作。开发了一种分析进化电路的方法,为其操作提供了见解。比较实验证明了晶体管阵列介质的优越演化性。
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Evolved Transistor Array Robot Controllers
For the first time, a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were also evolved and these were used in conjunction with the controllers to enable voice control of the robot, triggering behavioural switching. Poor quality visual sensors were deliberately used to test the ability of evolved analog circuits to deal with noisy uncertain data in realtime. Visual features were coevolved with the controllers to automatically achieve dimensionality reduction and feature extraction and selection in an integrated way. An efficient new method was developed for simulating the robot in its visual environment. This allowed controllers to be evaluated in a simulation connected to the FPTA. The controllers then transferred seamlessly to the real world. The circuit replication issue was also addressed in experiments where circuits were evolved to be able to function correctly in multiple areas of the FPTA. A methodology was developed to analyse the evolved circuits which provided insights into their operation. Comparative experiments demonstrated the superior evolvability of the transistor array medium.
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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
期刊最新文献
Genetic Programming for Automatically Evolving Multiple Features to Classification. A Tri-Objective Method for Bi-Objective Feature Selection in Classification. Preliminary Analysis of Simple Novelty Search. IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics. Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.
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