机器人周围环境的光照、阴影及物体遮挡等条件易导致环境信息采集出现误差,影响机器人建图精度。为优化障碍物的探测准确度和机器人的避障速度,提出基于激光雷达及模糊比例积分微分(PID)的机器人避障路径规划方法。利用Kinect 传感器采集机器人环境深度信息,并将其转化为3D 信息,构建全局地图。利用激光雷达,通过坐标转换、线段分割,构建局部地图,补充全局地图的不足。结合Cartographer 算法优化机器人控制参数,利用模糊PID控制器完成机器人避障路径 的规划。实验结果表明:该方法在普通避障环境和复杂避障环境下,均能够成功躲避障碍物,且避障路径均较短。

Conditions such as lighting, shadows, and object occlusion in the robot′s surrounding environment can easily lead to errors in environmental information collection, affecting the accuracy of robot mapping. In order to optimize the detection accuracy of obstacles and the obstacle avoidance speed of robots, a robot obstacle avoidance path planning method based on laser radar and fuzzy proportion integration differentiation (PID) is proposed. By using Kinect sensors the robot environment depth information is collected and converted into 3D information to build a global map. Using laser radar, local maps are constructed through coordinate conversion and line segment segmentation. Combining the Cartographer algorithm to optimize the robot control parameters, a fuzzy PID controller is used to complete the obstacle avoidance path planning of the robot. Experimental results show that the proposed method can successfully avoid obstacles in both ordinary and complex obstacle avoidance nvironments, and the obstacle avoidance path is shorter.

TP242;TN958.98

山西省教育科学规划课题资助项目(GH-19279)