基于内螺旋搜索的生物激励遍历路径规划算法

Path Planning Algorithm for Biological Incentive Traversal Based on Internal Spiral Search

针对传统生物激励神经网络遍历路径规划的重复覆盖率高和子区域间路径不是最优的问题,提出了基于内螺旋搜索的生物激励遍历路径规划方法。方法在未知水下环境信息的情况下通过生物激励神经网络算法完成水下地图环境建模与路径规划,在分割出子区域后通过内螺旋算法占主导完成子区域遍历,避免神经元活性值相同引起重复覆盖,子区域间通过A*算法实现最优路径规划。仿真结果表明,相较原方法,上述方法生成的路径分别在重复覆盖率、运行时间、路径长度指标上均有较大提升。

Aiming at the problems of high repetition coverage and sub-regional paths that are not optimal for traditional bio-stimulated neural network traversal path planning,a bio-encouraged traversal path planning method based on internal spiral search was proposed.Under the condition of unknown underwater environment information,the underwater map environment modeling and path planning were completed through the biological excitation neural network algorithm.After the sub-regions were segmented,the inner spiral algorithm was dominant to complete the traversal of the sub regions,so as to avoid repeated coverage caused by the same neuron activity value.The optimal path planning between the sub regions was realized by a*algorithm.The simulation results show that compared with the original method,the paths generated by the above method are greatly improved in repeated coverage,running time and path length.