未知环境下基于感知自适应的多AUV目标搜索算法

针对未知环境下目标搜索的复杂性与环境特征的随机性问题,提出了基于分区域的感知自适应目标搜索算法,来解决传统的梳子形搜索模式或预先离线设计的全局优化航迹中,自主水下航行器不能灵活适应环境的目标搜索问题。该方法的主要特点是根据AUV视域内有环境目标特征时,实时设计最优一步的航迹规划,并利用贝叶斯估计来完成目标定位,无目标时,利用分区域栅格值并锁定任务区域的航迹规划,两种模式交替进行来提高搜索的灵活性。仿真结果表明,相比于传统搜索模式或全局优化航迹,该方法增强了环境适应能力,提高了目标搜索效率。

Multi-AUV target search algorithm with cognitive based adaptive optimization in unknown environment#br#

To reduce the complexity of target search and the randomness of environmental features in unknown environment, this paper proposes a subarea based perceptual adaptive target search algorithm to solve the traditional comb search pattern or the problem that in global optimization track, autonomous underwater vehicles (AUVs) cannot flexibly adapt to the environment. The main feature of this method is to design the optimal trajectory planning in real time according to the characteristics of the environment target in the AUV vision and to use the Bayesian estimation to complete the target localization. When there is no target, use the sub regional grid values and lock the task regional trajectory planning, and the two modes alternate to improve the search flexibility. Simulation results show that compared with the traditional search mode or the global optimization track, this method enhances the environment adaptability and improves the target search efficiency.