克服V型障碍陷阱的激光雷达机器人分层避撞方法

激光雷达以其高的角度和距离分辨率被广泛地应用于移动机器人的障碍规避,但其固有的探测特性易使机器人陷入V型障碍陷阱,并增大路径代价.为此,通过深入分析激光雷达精度与距离的特性关系,提出按照探测精度将探测区域划分为模糊规避区、精确规避区和应急规避区,进而建立一种分层障碍规避方法.为模糊规避区设计神经网络障碍规避算法,同时在精确规避区采用边界点追踪法避撞.仿真实验表明,所提出方法相较于传统的激光雷达避撞方法,不仅能够使机器人避免误入V型陷阱,而且可以产生更小的路径代价.

Multilevel collision avoidance approach for lidar based robots to overcome trap of V-obstacle

Lidar is widely used in the obstacle avoidance field of the ground mobile robot because of its high resolution of angle and range, but its inherent characteristics of detection may induce robots to be caught in the trap of V-obstacle easily, and increase the cost of planning path. Thus, the detection zone of lidar is divided into the indistinct avoidance region, the precise avoidance region and the emergent avoidance region respectively based on deep analysis of relationship between its accuracy and detection range, furthermore, a multilevel obstacle avoidance method is proposed. An obstacle avoidance algorithm based on neural networks is designed for the indistinct avoidance region, while in the precise avoidance region, collision avoidance is realized by tracking the boundary laser points. Simulation experiments verify that compared to traditional collision avoidance methods based on lidar, the proposed approach can not only make the robot avoid being caught in the trap of V-obstacle, but also generate the avoidance path at a smaller cost.