# Learning-Accelerated Optimization-based Trajectory Planning for Cooperative Aerial-Ground Handover Missions

arXiv: https://arxiv.org/abs/2605.19562 · HTML: https://mlsanity.com/paper/2605.19562/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 6 of 6)

- [MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts](https://mlsanity.com/paper/2403.06041/) (2024)
- [Decentralized Multi-Agents by Imitation of a Centralized Controller](https://mlsanity.com/paper/1902.02311/) (2019)
- [Deep Learning Warm Starts for Trajectory Optimization on the International Space Station](https://mlsanity.com/paper/2505.05588/) (2025)
- [An Online Optimization-Based Trajectory Planning Approach for Cooperative Landing Tasks](https://mlsanity.com/paper/2502.13823/) (2025)
- [End-to-End Multi-Task Policy Learning from NMPC for Quadruped Locomotion](https://mlsanity.com/paper/2505.08574/) (2025)
- [Decentralized Real-Time Planning for Multi-UAV Cooperative Manipulation via Imitation Learning](https://mlsanity.com/paper/2510.17143/) (2025)

## Abstract (arXiv)

This paper presents a learning-augmented trajectory planning framework for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) handover missions. While centralized trajectory optimization ensures dynamic feasibility and task optimality, its high computational cost limits real-time applicability. We propose a neural surrogate planner utilizing decoupled encoder-decoder long short-term memory (LSTM) networks to generate coordinated handover trajectory predictions from the task specifications. These predictions serve as informed warm starts for the downstream centralized optimizer, thereby accelerating convergence to dynamically feasible solutions. Benchmark evaluations demonstrate that the learning-augmented planning framework achieves more than a threefold speedup and 100% optimization success rate compared to cold start optimization. The results indicate that combining data-driven inference with model-based refinement enables fast and reliable trajectory generation for heterogeneous multi-robot systems.