# Non-Equilibrium MAV-Capture-MAV via Time-Optimal Planning and Reinforcement Learning

arXiv: https://arxiv.org/abs/2503.06578 · HTML: https://mlsanity.com/paper/2503.06578/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Autonomous Drone Racing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.08624/) (2021)
- [Time-Optimal Planning for Quadrotor Waypoint Flight](https://mlsanity.com/paper/2108.04537/) (2021)
- [Model Predictive Contouring Control for Time-Optimal Quadrotor Flight](https://mlsanity.com/paper/2108.13205/) (2021)
- [Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles](https://mlsanity.com/paper/1611.09240/) (2016)
- [Learning High-Level Policies for Model Predictive Control](https://mlsanity.com/paper/2007.10284/) (2020)
- [Global-Local MAV Detection under Challenging Conditions based on Appearance and Motion](https://mlsanity.com/paper/2312.11008/) (2023)
- [Fast-Tracker: A Robust Aerial System for Tracking Agile Target in Cluttered Environments](https://mlsanity.com/paper/2011.03968/) (2020)
- [Towards Safe Mid-Air Drone Interception: Strategies for Tracking & Capture](https://mlsanity.com/paper/2405.13542/) (2024)
- [Optimal Spatial-Temporal Triangulation for Bearing-Only Cooperative Motion Estimation](https://mlsanity.com/paper/2310.15846/) (2023)
- [Generating Minimum-Snap Quadrotor Trajectories Really Fast](https://mlsanity.com/paper/2008.00595/) (2020)
- [A Nonlinear Model Predictive Control Strategy for Autonomous Racing of Scale Vehicles](https://mlsanity.com/paper/2302.04722/) (2023)
- [A Bearing-Angle Approach for Unknown Target Motion Analysis Based on Visual Measurements](https://mlsanity.com/paper/2401.17117/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learning Agile Intruder Interception using Differentiable Quadrotor Dynamics](https://mlsanity.com/paper/2607.02472/) (2026)

## Abstract (arXiv)

The capture of flying MAVs (micro aerial vehicles) has garnered increasing research attention due to its intriguing challenges and promising applications. Despite recent advancements, a key limitation of existing work is that capture strategies are often relatively simple and constrained by platform performance. This paper addresses control strategies capable of capturing high-maneuverability targets. The unique challenge of achieving target capture under unstable conditions distinguishes this task from traditional pursuit-evasion and guidance problems. In this study, we transition from larger MAV platforms to a specially designed, compact capture MAV equipped with a custom launching device while maintaining high maneuverability. We explore both time-optimal planning (TOP) and reinforcement learning (RL) methods. Simulations demonstrate that TOP offers highly maneuverable and shorter trajectories, while RL excels in real-time adaptability and stability. Moreover, the RL method has been tested in real-world scenarios, successfully achieving target capture even in unstable states.