改进egoTEB算法的机器人动态避障规划

Improved egoTEB algorithm for robot dynamic obstacle avoidance planning

对动态环境下自我时间弹性带(ego-timed-elastic-band,egoTEB)算法在避障时耗时较长且轨迹欠佳的问题,提出了改进egoTEB算法。首先对动态环境信息进行自我感知,以机器人自我为中心,由点、线段到圆形障碍物对环境信息进行提取,利用代价矩阵匹配圆形障碍物类型,采用集合卡尔曼滤波(ensemble Kalman filter,EnKF)模型跟踪动态圆形障碍物的运动轨迹。其次基于自我感知障碍物信息,构建由静态间隙和动态间隙组成的间隙规划图,设计加权间隙代价引导机器人局部轨迹安全穿过间隙,避开障碍物。最后利用机器人操作系统(robot operating system,ROS)进行仿真实验,验证了改进egoTEB算法能更快速地规划出安全轨迹,实现机器人适应性更强的避障效果。

Aiming at the problem that ego-timed-elastic-band(egoTEB)algorithm takes a long time and has a poor trajectory in obstacle avoidance in dynamic environment,an improved egoTEB algorithm is proposed.Firstly,the dynamic environment information was self-perceived,and the environmental information was extracted from points,line segments to the circular obstacles with the robot ego as the center.The cost matrix was used to match the types of circular obstacles,and the ensemble Kalman filter(EnKF)model was used to track the motion trajectory of the dynamic circular obstacles.Secondly,based on self-perceived obstacle information,a gap planning graph composed of static gap and dynamic gap is constructed,and a weighted gap cost is designed to guide the local trajectory of the robot to safely pass through the gap and avoid obstacles.Finally,the robot operating system(ROS)was used to conduct simulation experiments,which verified that the improved egoTEB algorithm can plan a safe trajectory more quickly and achieve a more adaptive obstacle avoidance effect of the robot.