中文摘要：

针对传感器数据分析中代数模型方法存在的简化、丢失环境细节信息的问题,提出了一种用于移动机器人避障多次膨胀分析的激光雷达数据分析方法.该方法通过对激光测距数据进行多次不同程度的障碍边界膨胀,分析各膨胀图特征参数并提取局部环境形状特征,强化机器人局部环境认知及避障控制能力;膨胀系数组合在线选取和切换的灵活性特点使机器人可适应不同形状、尺寸的环境;机器人运动控制中引入环境形状特征参与机器人运动决策,增加了避碰控制以确保机器人在狭小环境下的安全.仿真对比验证了该方法在不同形状环境下的运动规划能力;实物实验验证了该方法可克服电机控制信号精度低、响应延时及胶轮打滑等不利因素影响,具有实际可行性.

英文摘要：

Aiming at the problem that algebraic models used in sensor information analysis are prone to simplify or even ignore environmental details, we propose a novel laser data processing method based on multiple expan- sions for mobile robot obstacle avoidance. By expanding obstacle edges with several ratios and analyzing the characteristics of expansion maps, the proposed method extracts shape features of the local environment to en- hance robots＇ ability inlocal environmental recognition and obstacle avoidance. The flexibility of online selec- tion and the switch of the expansion ratios facilitates robots that are adaptableto environments of various shapes and sizes. In robot motion control, environmental shape features are also introduced for motion decision-mak- ing, while collision avoidance is added to ensure the security of robots in narrow environments. Simulations verify that the proposed method is competent for robots＇ motion planning in different environments, and a real platform experiment validates the method＇s feasibility for practical applications with low-precision control sig- nals, response delay, and tire slippage.