仿动物空间认知的多机器人协作目标搜索方法研究

项目名称： 仿动物空间认知的多机器人协作目标搜索方法研究

项目编号： No.61203365

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 倪建军

作者单位： 河海大学

项目金额： 26万元

中文摘要： 未知动态环境下多机器人目标搜索是机器人研究领域一个很重要又具挑战性的课题，其核心内容包括机器人同时定位与地图创建（SLAM）和多机器人协作。现有的SLAM算法大多假设环境是静态的，机器人有高精度传感器和高性能计算机，且很多SLAM和目标搜索问题的研究是割裂开的，无法充分利用SLAM研究的成果。动物具有很强的定位、导航和目标搜索能力，且不需要高精度传感器和复杂的计算，研究一种仿动物空间认知的、高效的、可用于实际的多机器人目标搜索方法具有重要意义。 本项目利用生物刺激神经网络，结合动物空间认知机制，进行机器人同时定位和地图创建，将地图表示、外界刺激、经验学习、目标搜索、导航等融为一体，主要研究内容包括基于生物刺激神经网络的SLAM算法、基于记忆网络的目标定位和机器人导航以及利用同伴经验强化的多机器人协作方法等。本项目的成功实施在未知环境探索、灾害救援、反恐、战场搜索等方面具有广阔的应用前景。

中文关键词： 动物空间认知；目标搜索；同时定位与地图创建；多机器人协作；生物刺激神经网络

英文摘要： The cooperative goal foraging of multi-robots in unknown and dynamic environments is a critical and challenging issue in the robotic field. The key contents include the Simultaneous Location and Mapping algorithm (SLAM) and the cooperation approach of multi-robots. Most of the SLAM algorithms are under varying assumptions, such as the environment is static and the robot is equipped with accurate sensors and a high performance computer. Moreover, much of the research on the SLAM algorithms and the goal foraging problems is independent, so it is difficult to use the results obtained from the study of SLAM algorithm, for the reality applications such as goal foraging. However, as we know, animals appear to have a great ability in mapping, goal foraging and navigation, without high precision sensors and complex computation. Thus, to study and create a biologically inspired and efficient multi-robot cooperative goal foraging approach with practical performance is very significant. This project attempts to use the bioinspired neural network to realize the simultaneous location and mapping, inspired from the animal spatial cognition. The proposed approach integrates the map expression, the external stimulation, the experience learning, the goal foraging and robot navigation together. The main research contents of this

英文关键词： Animal spatial cognition；Goal foraging；Simultaneous Location and Mapping；Multi-robot cooperation；Bioinspired neural network