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首页> 外文期刊>Journal of robotic systems >An Affordable Modular Mobile Robotic Platform with Fuzzy Logic Control and Evolutionary Artificial Neural Networks
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An Affordable Modular Mobile Robotic Platform with Fuzzy Logic Control and Evolutionary Artificial Neural Networks

机译：具有模糊逻辑控制和进化人工神经网络的可负担的模块化移动机器人平台

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Autonomous robotics projects encompass the rich nature of integrated systems that includes mechanical, electrical, and computational software components. The availability of smaller and cheaper hardware components has helped make possible a new dimension in operational autonomy. This paper describes a mobile robotic platform consisting of several integrated modules including a laptop computer that serves as the main control module, microcontroller-based motion control module, a vision processing module, a sensor interface module, and a navigation module. The laptop computer module contains the main software development environment with a user interface to access and control all other modules. Programming language independence is achieved by using standard input/output computer interfaces including RS-232 serial port, USB, networking, audio input and output, and parallel port devices. However, with the same hardware technology available to all, the distinguishing factor in most cases for intelligent systems becomes the software design. The software for autonomous robots must intelligently control the hardware so that it functions in unstructured, dynamic, and uncertain environments while maintaining an autonomous adaptability. This paper describes how we introduced fuzzy logic control to one robot platform in order to solve the 2003 Intelligent Ground Vehicle Competition (IGVC) Autonomous Challenge problem. This paper also describes the introduction of hybrid software design that utilizes Fuzzy Evolutionary Artificial Neural Network techniques. In this design, rather than using a control program that is directly coded, the robot's artificial neural net is first trained with a training data set using evolutionary optimization techniques to adjust weight values between neurons. The trained neural network with a weight average defuzzification method was able to make correct decisions to unseen vision patterns for the IGVC Autonomous Challenge. A comparison of the Lawrence Technological University robot designs and the design of the other competing schools shows that our platforms were the most affordable robot systems to use as tools for computer science and engineering education.
机译：自主机器人项目涵盖了集成系统的丰富特性，其中包括机械，电气和计算软件组件。更小，更便宜的硬件组件的上市使操作自治有了新的可能。本文介绍了一个由多个集成模块组成的移动机器人平台，这些模块包括用作主控制模块的膝上型计算机，基于微控制器的运动控制模块，视觉处理模块，传感器接口模块和导航模块。便携式计算机模块包含主要的软件开发环境，以及用于访问和控制所有其他模块的用户界面。通过使用标准的输入/输出计算机接口，包括RS-232串行端口，USB，网络，音频输入和输出以及并行端口设备，可以实现编程语言的独立性。但是，如果所有人都拥有相同的硬件技术，则在大多数情况下，智能系统的区别因素将成为软件设计。自主机器人的软件必须智能地控制硬件，以使其在非结构化，动态和不确定的环境中运行，同时保持自主适应性。本文介绍了如何将模糊逻辑控制引入一个机器人平台，以解决2003年智能地面车辆竞赛（IGVC）自主挑战问题。本文还介绍了利用模糊进化人工神经网络技术的混合软件设计的介绍。在此设计中，不是使用直接编码的控制程序，而是首先使用训练数据集对机器人的人工神经网络进行训练，该训练数据集使用进化优化技术来调整神经元之间的权重值。训练有素的神经网络采用权重平均去模糊方法，能够为IGVC自主挑战赛的看不见的视觉模式做出正确的决策。劳伦斯理工大学的机器人设计与其他竞争学校的设计比较表明，我们的平台是最实惠的机器人系统，可用作计算机科学和工程教育的工具。

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