# Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning

arXiv: https://arxiv.org/abs/1909.05150 · HTML: https://mlsanity.com/paper/1909.05150/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 14 of 14)

- [Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments](https://mlsanity.com/paper/2102.05382/) (2021)
- [Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning](https://mlsanity.com/paper/2309.13285/) (2023)
- [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)
- [Distributing Collaborative Multi-Robot Planning with Gaussian Belief Propagation](https://mlsanity.com/paper/2203.11618/) (2022)
- [Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning](https://mlsanity.com/paper/2307.16062/) (2023)
- [Learning the References of Online Model Predictive Control for Urban Self-Driving](https://mlsanity.com/paper/2308.15808/) (2023)
- [Decentralized traffic management of autonomous drones](https://mlsanity.com/paper/2312.11207/) (2023)
- [Distributed Model Predictive Control with Adaptive Safety Zones for Multi-Fleet Drone Operations](https://mlsanity.com/paper/2606.20651/) (2026)
- [PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner](https://mlsanity.com/paper/2406.10060/) (2024)
- [Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization](https://mlsanity.com/paper/2510.09204/) (2025)
- [LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation](https://mlsanity.com/paper/2511.17765/) (2025)
- [High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning](https://mlsanity.com/paper/2607.03512/) (2026)
- [Conflict-Predictive Variable Horizons in Multi-Drone Distributed Model Predictive Control](https://mlsanity.com/paper/2609.13270/) (2026)

## Abstract (arXiv)

We present a distributed model predictive control (DMPC) algorithm to generate trajectories in real-time for multiple robots. We adopted the textiton-demand collision avoidance method presented in previous work to efficiently compute non-colliding trajectories in transition tasks. An event-triggered replanning strategy is proposed to account for disturbances. Our simulation results show that the proposed collision avoidance method can reduce, on average, around 50% of the travel time required to complete a multi-agent point-to-point transition when compared to the well-studied Buffered Voronoi Cells (BVC) approach. Additionally, it shows a higher success rate in transition tasks with a high density of agents, with more than 90% success rate with 30 palm-sized quadrotor agents in a 18 m3 arena. The approach was experimentally validated with a swarm of up to 20 drones flying in close proximity.