Learning to Initialize Trajectory Optimization for Vision-Based Autonomous Flight in Unknown Environments

• URL: http://arxiv.org/abs/2309.10683v2
• Date: Fri, 08 Aug 2025 12:33:07 GMT
• Title: Learning to Initialize Trajectory Optimization for Vision-Based Autonomous Flight in Unknown Environments
• Authors: Yicheng Chen, Jinjie Li, Wenyuan Qin, Yongzhao Hua, Xiwang Dong, Qingdong Li,
• Abstract summary: We present a Neural-Enhanced Tray Planner (NEO-Planner) for autonomous flight in unknown environments.<n>NEO-Planner learns to predict spatial and temporal parameters for trajectories directly from raw sensor observations.<n>It reduces optimization by 20%, leading to a 26% decrease in trajectory time compared with pure optimization-based methods.
• Score: 4.0543433786183485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous flight in unknown environments requires precise spatial and temporal trajectory planning, often involving computationally expensive nonconvex optimization prone to local optima. To overcome these challenges, we present the Neural-Enhanced Trajectory Planner (NEO-Planner), a novel approach that leverages a Neural Network (NN) Planner to provide informed initial values for trajectory optimization. The NN-Planner is trained on a dataset generated by an expert planner using batch sampling, capturing multimodal trajectory solutions. It learns to predict spatial and temporal parameters for trajectories directly from raw sensor observations. NEO-Planner starts optimization from these predictions, accelerating computation speed while maintaining explainability. Furthermore, we introduce a robust online replanning framework that accommodates planning latency for smooth trajectory tracking. Extensive simulations demonstrate that NEO-Planner reduces optimization iterations by 20%, leading to a 26% decrease in computation time compared with pure optimization-based methods. It maintains trajectory quality comparable to baseline approaches and generalizes well to unseen environments. Real-world experiments validate its effectiveness for autonomous drone navigation in cluttered, unknown environments.

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