用神经ESDF优化连续3D轨迹，提升无人机避障效率与平滑性。
C-3TO: Continuous 3D Trajectory Optimization on Neural Euclidean Signed Distance Fields
- 用五阶多项式参数化连续轨迹，结合神经ESDF实现梯度精确计算。
- 在复杂环境中生成无碰撞、动态可行的轨迹，支持实时重规划。
- 可灵活调整窗口大小和参数，适配不同飞行需求，通用性强。
本文提出一种新颖的连续3D轨迹优化框架C-3TO，用于复杂环境中的无人机路径规划。不同于以往依赖离散网格插值的ESDF方法，本工作直接在连续神经欧氏有符号距离场（ESDF）上优化由五阶多项式表示的平滑轨迹，确保全程梯度信息准确。框架采用两阶段非线性优化流程，在效率、安全性和平滑性之间取得平衡。实验表明，C-3TO能生成具有碰撞感知能力且满足动力学可行性的轨迹。其在局部窗口尺寸和优化参数上的灵活性，使得系统可轻松适应不同用户需求，而性能不降。通过将连续轨迹参数化与持续更新的神经ESDF结合，C-3TO为航空机器人提供了安全高效的局部重规划基础。
原文摘要 · Abstract (English)
This paper introduces a novel framework for continuous 3D trajectory optimization in cluttered environments, leveraging online neural Euclidean Signed Distance Fields (ESDFs). Unlike prior approaches that rely on discretized ESDF grids with interpolation, our method directly optimizes smooth trajectories represented by fifth-order polynomials over a continuous neural ESDF, ensuring precise gradient information throughout the entire trajectory. The framework integrates a two-stage nonlinear optimization pipeline that balances efficiency, safety and smoothness. Experimental results demonstrate that C-3TO produces collision-aware and dynamically feasible trajectories. Moreover, its flexibility in defining local window sizes and optimization parameters enables straightforward adaptation to diverse user's needs without compromising performance. By combining continuous trajectory parameterization with a continuously updated neural ESDF, C-3TO establishes a robust and generalizable foundation for safe and efficient local replanning in aerial robotics.
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