基于云平台地图的侦察机器人室外自主导航

针对未知室外大尺度范围环境中的侦察机器人全局自主导航问题,提出了一种基于云平台地图路径规划的全局导航控制方法。首先对侦察机器人进行运动建模,采用扩展卡尔曼滤波算法融合GPS、里程计、电子罗盘和惯性测量单元等传感器信息实现机器人的全局定位;其次利用开放软件开发包的云平台地图来自动规划或手动规划,生成机器人从当前位置至设定目标位置的关键路径点序列;最后将机器人与关键路径点的距离及偏航角作为输入,设计了全局导航控制器控制机器人调整其航向和速度依次通过关键路径点直至到达目标。在不同环境中开展了机器人全局自主导航仿真和实验,结果表明提出的方法能够有效地引导机器人经过多个分岔路口,可靠地完成室外环境下的全局自主导航任务。

Autonomous Outdoor Navigation for Reconnaissance Robot Based on Cloud Platform Map

To address the problem of autonomous navigation of the reconnaissance robot in unknown large-scale outdoor environments, a global navigation control method based on cloud map is proposed. Firstly, the kinematic model of the reconnaissance robot is built. The extended Kalman filter(EKF) algorithm is then employed to estimate the robot position by fusing the sensory information coming from GPS receiver, odometer, compass and inertial measurement unit(IMU). Secondly, by using the open SDK cloud platform maps, a sequence of key waypoints could be autonomously or manually planned. Finally, taking the distance and the drift angle to the next key waypoint as input, the global navigation controller is designed to navigate the robot with proper heading and velocity, aiming at achieving the destination through moving toward the key waypoints sequentially. Simulations and experiments of autonomous robot navigation are performed in different environments. The results show that the proposed approach can effectively pilot the robot moving through multiple road intersections, and make the robot complete the global autonomous outdoor navigation task reliably.