# Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control

arXiv: https://arxiv.org/abs/1809.04230 · HTML: https://mlsanity.com/paper/1809.04230/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 6 of 6)

- [Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARL](https://mlsanity.com/paper/2504.15425/) (2025)
- [SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography](https://mlsanity.com/paper/2412.08428/) (2024)
- [Distributed Swarm Collision Avoidance Based on Angular Calculations](https://mlsanity.com/paper/2108.12934/) (2021)
- [Collision Avoidance for Multiple UAVs in Unknown Scenarios with Causal Representation Disentanglement](https://mlsanity.com/paper/2407.04064/) (2024)
- [Guiding Reinforcement Learning with Incomplete System Dynamics](https://mlsanity.com/paper/2410.16821/) (2024)
- [D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations](https://mlsanity.com/paper/2503.12204/) (2025)

## Abstract (arXiv)

This paper introduces a novel algorithm for multiagent offline trajectory generation based on distributed model predictive control. Central to the algorithm's scalability and success is the development of an on-demand collision avoidance strategy. By predicting future states and sharing this information with their neighbors, the agents are able to detect and avoid collisions while moving toward their goals. The proposed algorithm can be implemented in a distributed fashion and reduces the computation time by more than 85% compared to previous optimization approaches based on sequential convex programming, while only having a small impact on the optimality of the plans. The approach was validated both through extensive simulations and experimentally with teams of up to 25 quadrotors flying in confined indoor spaces.