# Real-Time Sampling-based Online Planning for Drone Interception

arXiv: https://arxiv.org/abs/2502.14231 · HTML: https://mlsanity.com/paper/2502.14231/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight](https://mlsanity.com/paper/2007.03465/) (2020)
- [Shortest Paths in Graphs of Convex Sets](https://mlsanity.com/paper/2101.11565/) (2021)
- [Learning Minimum-Time Flight in Cluttered Environments](https://mlsanity.com/paper/2203.15052/) (2022)
- [Learning Speed Adaptation for Flight in Clutter](https://mlsanity.com/paper/2403.04586/) (2024)
- [Accurate Tracking of Aggressive Quadrotor Trajectories using Incremental Nonlinear Dynamic Inversion and Differential Flatness](https://mlsanity.com/paper/1809.04048/) (2018)
- [Search-based Motion Planning for Quadrotors using Linear Quadratic Minimum Time Control](https://mlsanity.com/paper/1709.05401/) (2017)
- [Minimum-Time Quadrotor Waypoint Flight in Cluttered Environments](https://mlsanity.com/paper/2202.03947/) (2022)
- [Multi-Fidelity Reinforcement Learning for Time-Optimal Quadrotor Re-planning](https://mlsanity.com/paper/2403.08152/) (2024)
- [Multi-Sample Long Range Path Planning under Sensing Uncertainty for Off-Road Autonomous Driving](https://mlsanity.com/paper/2403.11298/) (2024)
- [Topological Trajectory Prediction with Homotopy Classes](https://mlsanity.com/paper/2301.09821/) (2023)

## Abstract (arXiv)

This paper studies high-speed online planning in dynamic environments. The problem requires finding time-optimal trajectories that conform to system dynamics, meeting computational constraints for real-time adaptation, and accounting for uncertainty from environmental changes. To address these challenges, we propose a sampling-based online planning algorithm that leverages neural network inference to replace time-consuming nonlinear trajectory optimization, enabling rapid exploration of multiple trajectory options under uncertainty. The proposed method is applied to the drone interception problem, where a defense drone must intercept a target while avoiding collisions and handling imperfect target predictions. The algorithm efficiently generates trajectories toward multiple potential target drone positions in parallel. It then assesses trajectory reachability by comparing traversal times with the target drone's predicted arrival time, ultimately selecting the minimum-time reachable trajectory. Through extensive validation in both simulated and real-world environments, we demonstrate our method's capability for high-rate online planning and its adaptability to unpredictable movements in unstructured settings.