融合生物信息素的改进稀疏A^(*)探索航迹规划

Improved Sparse A*Trajectory Planning Exploration Incorporated with Pheromone

针对已知起点和终点、而环境信息未知情况下的探索航迹规划问题,提出了融合生物信息素的改进稀疏A^(*)无人机探索航迹规划算法。以激光雷达获取的局部地图信息为基础,通过引入生物信息素,对稀疏A^(*)算法中的代价函数进行优化,实现未知环境下自主规避障碍物,并避免环境重复探索。在此基础上,提出了基于机器人操作系统(ROS)的物理实施途径。通过“回”字形场景下的仿真实验对比,验证了所提算法可避免环境重复探索的有效性。此外,在“回”字形基础上,将所提算法推广应用于柱状障碍物场景。实验结果表明:所提出的融合生物信息素的改进稀疏A*探索航迹规划算法能有效实现未知环境探索航迹规划。

Aiming at the problem of trajectory planning exploration when the starting point and ending point are known and the environmental information is unknownan improved sparse A^(*) UAV trajectory planning exploration method incorporating pheromone is proposed.Based on the local map information obtained by lidar and by introducing the pheromonethe cost function in the sparse A^(*) algorithm is optimized to realize autonomous avoidance of obstacles in unknown environment without repeated exploration.On this basisa physical implementation approach based on Robot Operating System(ROS)is proposed.Under spiral-form obstacle sceneit is verified that the method can avoid repeated exploration of the environment.In additionthe method is extended to the columnar obstacle scene.The experimental results show that the method can effectively achieve the exploration of trajectory planning in the unknown environment.