Trajectory generation for visually impaired scenarios requires smooth and temporally consistent state in structured, low-speed dynamic environments. However, traditional jerk-based heuristic trajectory sampling with independent segment generation and conventional smoothness penalties often lead to unstable terminal behavior and state discontinuities under frequent regenerating. This paper proposes a trajectory generation approach that integrates endpoint regulation to stabilize terminal states within each segment and momentum-aware dynamics to regularize the evolution of velocity and acceleration for segment consistency. Endpoint regulation is incorporated into trajectory sampling to stabilize terminal behavior, while a momentum-aware dynamics enforces consistent velocity and acceleration evolution across consecutive trajectory segments. Experimental results demonstrate reduced acceleration peaks and lower jerk levels with decreased dispersion, smoother velocity and acceleration profiles, more stable endpoint distributions, and fewer infeasible trajectory candidates compared with a baseline planner.

翻译：面向视障场景的轨迹生成需要在结构化、低速动态环境中产生平滑且时间一致的状态。然而，传统的基于加加速度的启发式轨迹采样方法采用独立分段生成和常规平滑度惩罚，在频繁重新生成的情况下常导致终端行为不稳定及状态不连续。本文提出一种轨迹生成方法，该方法集成端点调节以稳定各分段内的终端状态，并采用动量感知动力学来规范速度与加速度的演化以保持分段一致性。端点调节被纳入轨迹采样过程以稳定终端行为，而动量感知动力学则强制要求连续轨迹分段间速度与加速度演化的一致性。实验结果表明，与基线规划器相比，所提方法降低了加速度峰值与加加速度水平并减小了其离散程度，获得了更平滑的速度与加速度曲线、更稳定的端点分布以及更少的不可行轨迹候选方案。