# Robust & Asymptotically Locally Optimal UAV-Trajectory Generation Based on Spline Subdivision

arXiv: https://arxiv.org/abs/2010.09904 · HTML: https://mlsanity.com/paper/2010.09904/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Constrained Stein Variational Trajectory Optimization](https://mlsanity.com/paper/2308.12110/) (2023)
- [Physics-informed Temporal Difference Metric Learning for Robot Motion Planning](https://mlsanity.com/paper/2505.05691/) (2025)
- [Neural Volumetric Mesh Generator](https://mlsanity.com/paper/2210.03158/) (2022)
- [PoseShield: Neural Collision Fields for Human Self-Collision Resolution](https://mlsanity.com/paper/2606.29686/) (2026)

## Abstract (arXiv)

Generating locally optimal UAV-trajectories is challenging due to the non-convex constraints of collision avoidance and actuation limits. We present the first local, optimization-based UAV-trajectory generator that simultaneously guarantees the validity and asymptotic optimality for known environments. textitValidity: Given a feasible initial guess, our algorithm guarantees the satisfaction of all constraints throughout the process of optimization. textitAsymptotic Optimality: We use an asymptotic exact piecewise approximation of the trajectory with an automatically adjustable resolution of its discretization. The trajectory converges under refinement to the first-order stationary point of the exact non-convex programming problem. Our method has additional practical advantages including joint optimality in terms of trajectory and time-allocation, and robustness to challenging environments as demonstrated in our experiments.