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首页> 外文会议>International Florida Artificial Intelligence Research Society Conference(FLAIRS 2006); 20060511-13; Melbourne Beach,FL(US) >Simulated Visual Perception-Based Control for Autonomous Mobile Agents
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Simulated Visual Perception-Based Control for Autonomous Mobile Agents

机译：基于视觉感知的自主移动代理控制

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摘要

Autonomous robots, such as automatic vacuum cleaners, toy robot dogs, and autonomous vehicles for the military, are rapidly becoming a part of everyday life. As a result the need for effective algorithms to control these agents is becoming increasingly important. Conventional path finding techniques rely on a representation of the world that can be analysed mathematically to find the best path. However, when an agent is placed into the real world in a place it has not seen before, conventional techniques frequently fail and a fundamentally different approach to path finding is required. The agent must rely on its senses, such as the input from a mounted camera, using this information to get around. We are especially interested in algorithms for use in highly interactive virtual environments such as computer games. In this paper we devise and analyse a technique which enables autonomous agents to navigate their way around a virtual city by using only what they see from their point of view. Since the scenes are computer generated we can use for the player's view and the agent's view representations with different visual complexity and hence improve the efficiency and effectiveness of the neural network. We show that by using neural networks agents can learn how to avoid obstacles, to follow the road, and we demonstrate that this method might even be useful for integration in path finding algorithm.
机译：自主机器人，例如自动吸尘器，玩具机器人狗和军用自主车辆，正迅速成为日常生活的一部分。结果，对控制这些代理的有效算法的需求变得越来越重要。传统的路径查找技术依赖于可以通过数学分析找到最佳路径的世界表示。但是，如果将代理程序放置在以前从未见过的真实世界中，则常规技术经常会失败，因此需要一种根本不同的路径查找方法。代理必须依靠其感觉（例如来自安装的摄像机的输入）来使用此信息来解决问题。我们对在高度互动的虚拟环境（例如计算机游戏）中使用的算法特别感兴趣。在本文中，我们设计并分析了一种技术，该技术使自治代理可以仅使用他们从其视角看到的内容在虚拟城市中导航。由于场景是计算机生成的，因此我们可以将玩家的视图和代理的视图表示用于不同的视觉复杂度，从而提高神经网络的效率和有效性。我们证明了通过使用神经网络代理可以学习如何避开障碍物，沿着道路前进，并且我们证明了该方法甚至可能对整合路径发现算法有用。

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