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

2026-02-25

AI Summary

Existing trajectory generation methods often suffer from unstable terminal states and discontinuous trajectories in visually impaired navigation scenarios due to segment-wise independent sampling and heuristic smoothing penalties. To address this, this work proposes a trajectory generation approach that integrates terminal state constraints with momentum-aware dynamics. By explicitly constraining the endpoint of each trajectory segment and introducing jerk-based regularization to govern the evolution of velocity and acceleration, the method enhances inter-segment consistency and overall smoothness. Experimental results demonstrate that, compared to baseline planners, the proposed approach significantly reduces peak acceleration and jerk levels, decreases trajectory dispersion, yields smoother motion profiles, produces more stable terminal state distributions, and substantially lowers the incidence of infeasible trajectories.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• trajectory generation
• visually impaired scenarios
• endpoint stability
• state discontinuities
• temporal consistency

Innovation

• endpoint regulation
• momentum-aware dynamics
• trajectory generation
• jerk minimization
• state consistency