Optimal Path Planning Algorithm of Indoor AGV Enhanced with HHO and Sector-Wide Field DWA

With an aim to solve the issue of dynamic obstacles and the global optimal path planning for indoor Automated Guided Vehicles (AGVs), this paper addressed an enhanced hybrid path planning method based on the improvement of Harris Hawk Optimization (HHO) and Dynamic Window Approach (DWA). The main contributions are given as follows. On the one hand, compared to the traditional HHO, the performances are improved in the following aspects. First, initializing the Harris Hawk population with a circle-tent chaotic map to enhance search capability. Second, balancing global and local searches with a segmented strategy to avoid local optima; Third, improving population survival with a hard-constraint mutation strategy. On the other hand, DWA is enhanced from the following three perspectives: a sector potential field is adopted for early obstacle avoidance and a smoother AGV trajectories; a dynamic weighting strategy is used to obtain a better escape local optima and enhance safety; a obstacle-free path attraction strategy is proposed to solve the problem of detours and accelerates the convergence speed. Finally, combined the enhanced HHO with DWA, Generalized Harris Hawks Optimization-Dynamic Window Approach (GHHO-DWA) algorithm addresses the issues of nonsmooth global paths and inadequate dynamic obstacle avoidance. Meanwhile, the effectiveness and practicality of GHHO-DWA algorithm are validated through MATLAB simulations and physical testing on the QBot2e platform.