School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China.
Sensors (Basel). 2022 Mar 22;22(7):2429. doi: 10.3390/s22072429.
To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.
为了解决无人巡航船在未知障碍物环境下遍历多目标路径规划的问题，本研究提出了一种混合多目标路径规划算法。所提出的算法可以分为两部分。首先，将多目标路径规划问题转化为旅行商问题，并使用改进的灰狼优化（GWO）算法计算多目标巡航序列。改进的 GWO 算法通过引入 Beta 函数来优化收敛因子，从而提高传统 GWO 算法的收敛速度。其次，基于规划的目标序列，使用改进的 D* Lite 算法在未知障碍物环境中实现每两个目标点之间的路径规划。改进了 D* Lite 算法中的启发式函数，以减少扩展节点的数量，从而提高搜索速度，并平滑规划路径。通过实验验证了所提出的算法，并与其他四种算法在普通和复杂环境下进行了比较。实验结果表明，所提出的方法具有很强的适用性和高效性。
