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随着经济水平和人们的需求的日益增长,移动机器人在未知环境的自主导航在各行各业成为了不可或缺的一部分。为了实现水面舰艇自主返航、自动泊船等功能,提出一种自主定位导航的移动机器人,并最终将其应用于水面舰艇自主定位导航。首先本文以模块化理念,构建了机器人自主定位导航的硬件系统。其系统由数据处理模块、环境感知模块、对外交互模块、运动执行模块和供电模块这五个部分构成。然后比较分析环境感知模块中的CCD、超声波、激光雷达等几种广泛应用的移动机器人传感器技术在不同情境下的优劣,并基于此比较得出水面舰艇的组合传感器环境感知方法。最后在数据处理模块中提出了扩展卡尔曼滤波,改进了卡尔曼滤波减轻定位产生的误差,通过非线性处理环境中当前特征的参量提取与预测量值之间的相互修正。本文特点在于精确有效的实现水上移动机器人的定位,移动。
With the increasing economic level and people’s demand, the autonomous navigation of mobile robot in unknown environment has become an indispensable part in all walks of life. In order to realize the autonomous return of naval ships and automatic mooring, a mobile robot with autonomous positioning and guidance is proposed and finally applied to autonomous navigation of surface ships. First of all, this paper constructs the hardware system of robot autonomous positioning and navigation based on the modular concept. The system is composed of five parts: data processing module, environment awareness module, external interaction module, exercise module and power supply module. Then the advantages and disadvantages of several widely used mobile robot sensor technologies, such as CCD, ultrasonic and lidar, are analyzed and compared in different situations. Based on this comparison, the sensing method of the combined sensor environment of surface ships is derived. Finally, an extended Kalman filter is proposed in the data processing module. Kalman filtering is used to reduce the errors caused by the positioning. Correlation between the extracted parameters and the predicted values of the current features in the non-linear processing environment is improved. This article is characterized by precise and effective realization of water mobile robot positioning and movement.