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Humanoid robots, with their human-like form, can more easily integrate into human daily life and adapt to existing infrastructure environments. The study of their kinematics and dynamics theories, along with methods for disturbance rejection control, has been a focal point of research among numerous scientists and engineers around the world for nearly half a century. Due to disturbances from external uncertainties, the motion state of humanoid robots may undergo significant changes in a short period, often leading to difficulties in maintaining continuous walking and resulting in falls. Firstly, optimization adjustments were made to the relationship equation between the walking parameters and the target foot placement in classical gait planning methods based on the linear inverted pendulum, aiming to achieve a more coordinated walking gait. Secondly, a gait planning method based on optimizing the deviation of foot placement within one and two steps was proposed by generating walking patterns in two-step cycles and anticipating the subsequent two target foot placements. Substantial acceleration/deceleration walking simulations and experiments were conducted on a small humanoid robot. The experimental results showed that the improved gait planning method can significantly reduce the maximum deviation of the landing points, reducing the deviation of two consecutive steps during motion state transitions from 1.1cm and 0.8cm to 0.6cm and 0.7cm, respectively, compared with the classical method. Moreover, the improved gait planning method also mitigated the impact of inertial forces on trunk stability, decreasing the maximum change in trunk pitch angle caused by the classical method from 7.8 to 6.0°.