Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios

TL;DR

This paper introduces a hybrid trajectory optimization framework combining heuristic sampling, momentum constraints, residual-enhanced deep reinforcement learning, and dual-stage cost modeling to improve safety, efficiency, and interpretability for assistive navigation of visually impaired users.

Contribution

The paper presents a novel hybrid framework that integrates heuristic sampling, momentum constraints, residual DRL, and dual-stage cost modeling for improved assistive navigation.

Findings

Converges in nearly half the iterations of baseline methods.

Achieves lower and more stable costs in complex dynamic scenarios.

Demonstrates stable velocity and acceleration with reduced risk.

Abstract

Safe and efficient assistive planning for visually impaired scenarios remains challenging, since existing methods struggle with multi-objective optimization, generalization, and interpretability. In response, this paper proposes a Momentum-Constrained Hybrid Heuristic Trajectory Optimization Framework (MHHTOF). To balance multiple objectives of comfort and safety, the framework designs a Heuristic Trajectory Sampling Cluster (HTSC) with a Momentum-Constrained Trajectory Optimization (MTO), which suppresses abrupt velocity and acceleration changes. In addition, a novel residual-enhanced deep reinforcement learning (DRL) module refines candidate trajectories, advancing temporal modeling and policy generalization. Finally, a dual-stage cost modeling mechanism (DCMM) is introduced to regulate optimization, where costs in the Frenet space ensure consistency, and reward-driven adaptive weights in the Cartesian space integrate user preferences for interpretability and user-centric decision-making. Experimental results show that the proposed framework converges in nearly half the iterations of baselines and achieves lower and more stable costs. In complex dynamic scenarios, MHHTOF further demonstrates stable velocity and acceleration curves with reduced risk, confirming its advantages in robustness, safety, and efficiency.