To address the shortcomings of the traditional Aquila Optimizer (AO), such as its propensity to fall into local optima and its slow convergence in high-dimensional optimization and robot path planning, this paper proposes an improved algorithm named HATAO (Aquila Optimizer with Halton, Aerial search, and Triangular mutation). Firstly, the Halton low-discrepancy sequence is used to enhance the uniformity of the initial population distribution. Secondly, an aerial search mechanism from the Arctic Puffin Algorithm is incorporated into the contraction exploitation phase to improve the population′s cooperative evolution and search accuracy. Lastly, a triangular mutation operator is introduced to enhance the algorithm′s convergence performance in its later stages. The proposed HATAO is benchmarked against five other algorithms using the CEC2017 test suite, with statistical significance evaluated by the Wilcoxon rank-sum test. In robot path planning applications, experimental results demonstrate that HATAO achieves superior search accuracy, faster convergence, and greater stability. Specifically, compared to the original AO, HATAO reduces path lengths by approximately 4.96% in simple scenarios and 6.34% in complex scenarios, verifying its effectiveness and robustness for practical path-planning tasks.