An End-To-End Trajectory Planner for Safe and Efficient Navigation in Crowded Dynamic Environments

Abstract

This paper presents a novel end-to-end trajectory planning framework that integrates LiDAR-based perception with trajectory optimization, enabling safe and efficient navigation in dynamic environments without relying on semantic detection or explicit kinematic modeling. Learning-based dynamic collision avoidance methods often depend on reinforcement learning, which introduces challenges related to training efficiency, model generalization, and deployment safety. To address these limitations, we propose a lightweight map representation for temporally continuous dynamic obstacles, facilitating unsupervised network training with physically simulated data. Additionally, a repulsion-based adjustment method built upon motion primitives allows adaptive trajectory planning in highly crowded scenarios where no feasible trajectory exists, balancing target-reaching objectives with motion safety. Extensive simulations and real-world experiments demonstrate that the proposed framework achieves millisecond-level planning latency while ensuring high safety, trajectory smoothness, and flight efficiency. The demonstration video is available on the project website: https://swift520.github.io/Dynamic-Planner/.