未知环境中自主机器人环境探索与地图构建

研究了未知环境中自主机器人同时进行环境探索和拓扑地图构建问题．基于实时获得的激光传感器数据，提出了具有避障功能的环境探索策略．在环境探索的基础上，提出了以增长的神经气网络的网络节点作为拓扑网络节点的环境地图模型．该模型利用神经气网络的增长特性，通过不断增加新的拓扑网络节点来对机器人周围未知环境的整体性知识进行抽取与表达，构建出了易于机器人理解的环境地图．仿真试验验证了所提方法的有效性．

Environment Exploration and Map Building of Autonomous Robot in Unknown Environment

Simuhaneous environment exploration and map building of the autonomous robot in unknown environment were studied. Based on the real-time data acquired from the laser sensor, environment ex- ploration strategy with the function of obstacle avoidance was proposed. Furthermore, a topological map model was proposed which used the nodes of the GNG network as the topological network nodes. With the growth characteristics of the GNG network, new topological network nodes were added into the network to abstract and express the holistic knowledge of the surrounding environment and to construct the environ- ment map. Simulation results demonstrated its effectiveness.