Abstract
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.