Cai Cuicui, Jia Chaochuan, Nie Yao, Zhang Jinhong, Li Ling
College of Electronics and Information Engineering, West Anhui University, Lu'an, China.
PeerJ Comput Sci. 2023 Jul 18;9:e1473. doi: 10.7717/peerj-cs.1473. eCollection 2023.
Path planning is a critical technology that could help mobile robots accomplish their tasks quickly. However, some path planning algorithms tend to fall into local optimum in complex environments. A path planning method using a modified Harris hawks optimization (MHHO) algorithm is proposed to address the problem and improve the path quality. The proposed method improves the performance of the algorithm through multiple strategies. A linear path strategy is employed in path planning, which could straighten the corner segments of the path, making the obtained path smooth and the path distance short. Then, to avoid getting into the local optimum, a local search update strategy is applied to the HHO algorithm. In addition, a nonlinear control strategy is also used to improve the convergence accuracy and convergence speed. The performance of the MHHO method was evaluated through multiple experiments in different environments. Experimental results show that the proposed algorithm is more efficient in path length and speed of convergence than the ant colony optimization (ACO) algorithm, improved sparrow search algorithm (ISSA), and HHO algorithms.
路径规划是一项关键技术，它可以帮助移动机器人快速完成任务。然而，一些路径规划算法在复杂环境中容易陷入局部最优。为了解决这个问题并提高路径质量，提出了一种使用改进的哈里斯鹰优化（MHHO）算法的路径规划方法。该方法通过多种策略提高了算法的性能。在路径规划中采用线性路径策略，该策略可以拉直路径的拐角段，使获得的路径平滑且路径距离短。然后，为了避免陷入局部最优，对哈里斯鹰优化算法应用局部搜索更新策略。此外，还使用非线性控制策略来提高收敛精度和收敛速度。通过在不同环境中的多次实验评估了MHHO方法的性能。实验结果表明，与蚁群优化（ACO）算法、改进的麻雀搜索算法（ISSA）和哈里斯鹰优化算法相比，该算法在路径长度和收敛速度方面更高效。
