Frenet坐标框架下的自适应避障算法

目的 解决包装车间无人运输车辆在沿全局路径行驶过程中，难以同时保证跟随轨迹平滑及规避障碍物的问题。方法 构建Frenet坐标框架，描述车辆位姿与全局路径的相对关系，利用五次多项式生成多条待选局部路径;采用障碍物势场法模糊处理包装车间障碍的外形轮廓，并设置膨胀区间;结合待选路径采样点经过障碍物膨胀区间得到的碰撞值，以及采样点与全局规划路径的偏差值，综合评估路径的避障能力，筛选出最优的避障路径。结果 该算法能根据障碍物的大小，动态解算出与全局路径偏移量小且运动连贯的局部避障路径。结论 所提算法使得车辆完成运输任务时的效果更好，且效率更高。

Adaptive Obstacle Avoidance Algorithm Based on Frenet Coordinate Framework

The work aims to address the difficulty of ensuring smooth following trajectories and obstacle avoidance at the same time when unmanned transport vehicles are travelling along a global path in the packaging workshop. The Frenet coordinate frame was constructed to describe the relative relationship between the vehicle pose and the global path. A quintic polynomial was adopted to generate multiple local paths to be selected. The obstacle profile of the packaging workshop was fuzzy processed by the obstacle potential field method, and the expansion interval was set. Combined with the collision values calculated for the sampling points of the path to be selected in the obstacle expansion interval, and the deviation values of the sampling points from the globally planned paths, the obstacle avoidance quality of the local paths was comprehensively evaluated to filter out the optimal obstacle avoidance path. According to the size of obstacles, the algorithm could dynamically calculate the local obstacle avoidance path with small offset from the global path and coherent motion. The proposed algorithm makes the vehicle complete the transport task with better effect and higher efficiency.