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

TL;DR

This paper introduces a trajectory generation method for visually impaired scenarios that stabilizes terminal states and enforces velocity and acceleration consistency, resulting in smoother and more stable trajectories in dynamic environments.

Contribution

It proposes endpoint regulation and momentum-aware dynamics to improve trajectory stability and smoothness, addressing issues of instability and discontinuity in prior methods.

Findings

Reduced acceleration peaks and jerk levels

More stable endpoint distributions

Fewer infeasible trajectory candidates

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.