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Trajectory Generation with Endpoint Regulation and Momentum-Aware Dynamics for Visually Impaired Scenarios

Yuting Zeng, Manping Fan, You Zhou, Yongbin Yu, Zhiwen Zheng, Jingtao Zhang, Liyong Ren, Zhenglin Yang

TL;DR

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.

Abstract

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.

Trajectory Generation with Endpoint Regulation and Momentum-Aware Dynamics for Visually Impaired Scenarios

TL;DR

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.

Abstract

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.
Paper Structure (15 sections, 16 equations, 7 figures, 1 table)

This paper contains 15 sections, 16 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Heuristic Trajectory Generation with Endpoint Regulation
  3. Momentum-aware Local Trajectory Optimization
  4. Momentum Consistency Across Trajectory Segments
  5. Momentum-Aware Formulation
  6. Momentum-Aware Lagrangian.
  7. External modulation abstraction.
  8. Uncertainty-aware regularization.
  9. Local Trajectory Optimization Procedure
  10. Experimental Evaluation
  11. Experimental Setup
  12. Experiment 1: Local Smoothness Analysis
  13. Experiment 2: Endpoint Density Regulation
  14. Experiment 3: Feasibility and Safety
  15. Conclusion

Figures (7)

  • Figure 1: Schematic illustration of endpoint regulation in trajectory sampling. (a) Unconstrained sampling yields dispersed terminal states within the trajectory cluster. (b) Endpoint regulation constrains terminal deviation with respect to a reference sample, producing well-conditioned boundary states for subsequent local optimization.
  • Figure 2: Illustration of momentum inconsistency in heuristic trajectory generation. (a) Spatial trajectory composed of independently optimized segments. (b) Spatial trajectory with momentum-aware regulation, showing similar geometric layout. (c) Velocity profile without momentum consistency, exhibiting a slope discontinuity at the segment boundary. (d) Velocity profile with enforced momentum consistency, yielding smooth temporal evolution.
  • Figure 3: Velocity, acceleration, and jerk profiles along the longitudinal displacement across three low-speed scenarios. The results compare the baseline and the proposed method, highlighting consistent trends in motion smoothness and jerk attenuation.
  • Figure 4: Boxplots of longitudinal and lateral jerk across three scenarios.
  • Figure 5: Comparison of trajectory cluster generation with and without endpoint regulation at a representative time step, where (a) corresponds to the baseline and (b) shows the result with endpoint regulation.
  • ...and 2 more figures