基于细菌觅食-改进蚁群优化算法的水面无人船路径规划

Path Planning for Unmanned Surface Vehicle Based on Bacterial Foraging-Improved Ant Colony Optimization Algorithm

为了解决水面无人船全局路径规划问题,提出了一种细菌觅食-改进蚁群优化算法(bacterial foraging-improved ant colony optimization algorithm,BF-IACOA)。相较于传统蚁群优化算法(ant colony optimization algorithm,ACOA),该算法在路径搜索策略上考虑水面无人船航行需要尽可能减少转向次数和完全规避过大转向角的约束,引入转向角启发因子,综合求解转移概率;同时引入细菌觅食算法的繁殖操作和趋化操作,改进信息素浓度的更新方式,解决传统ACOA容易陷入局部最优解和收敛速度较慢的问题。仿真结果表明,相较于传统ACOA,BF-IACOA的全局搜索能力得到较大幅度的提升,并且收敛迭代次数减少超过30%;在实际水域环境模型下,BF-IACOA可以通过14次迭代为无人船规划出全局可行路径。

To solve the problem of global path planning for unmanned surface vehicles,a bacterial foraging-improved ant colony optimization algorithm(BF-IACOA)is proposed.Compared with the conventional ant colony optimization algorithm(ACOA),the proposed algorithm takes into account constraints such as the need to minimize the number of turns and completely avoid excessive steering angle in the path search strategy for the navigation of unmanned surface vehicles,and the steering angle heuristic factor is introduced to solve the transfer probability comprehensively.At the same time,the reproduction operation and chemotaxis operation of bacterial foraging algorithm are introduced to improve the updating mode of pheromone concentration and solve the problems that the conventional ACOA is easy to fall into the local optimal solution and has slow convergence speed.The simulation results show that,compared with the conventional ACOA,the global search capability of BF-IACOA is greatly improved,and the number of convergence iterations is reduced by more than 30%.Under the actual water environment model,the BF-IACOA can plan a global feasible path at 14 iterations for the unmanned surface vehicle.