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

arXiv cs.RO / 4/17/2026

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Key Points

The paper introduces the Momentum-Constrained Hybrid Heuristic Trajectory Optimization Framework (MHHTOF) to enable safer and more efficient assistive planning in visually impaired scenarios that are difficult for existing methods.
MHHTOF balances comfort and safety by combining a Heuristic Trajectory Sampling Cluster with momentum-constrained optimization that suppresses abrupt changes in velocity and acceleration.
It adds a residual-enhanced deep reinforcement learning module to refine candidate trajectories, improving temporal modeling and generalization of the resulting policy.
A dual-stage cost modeling mechanism regulates optimization using Frenet-space costs for consistency and adaptive, reward-driven weights in Cartesian space to incorporate user preferences for interpretability and user-centric decision-making.
Experiments indicate faster convergence (about half as many iterations as baselines) and lower, more stable costs, with improved robustness and reduced risk in complex dynamic environments.

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.