Robust MADER: Decentralized and Asynchronous Multiagent Trajectory Planner Robust to Communication Delay
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Abstract (arXiv)
Although communication delays can disrupt multiagent systems, most of the existing multiagent trajectory planners lack a strategy to address this issue. State-of-the-art approaches typically assume perfect communication environments, which is hardly realistic in real-world experiments. This paper presents Robust MADER (RMADER), a decentralized and asynchronous multiagent trajectory planner that can handle communication delays among agents. By broadcasting both the newly optimized trajectory and the committed trajectory, and by performing a delay check step, RMADER is able to guarantee safety even under communication delay. RMADER was validated through extensive simulation and hardware flight experiments and achieved a 100% success rate of collision-free trajectory generation, outperforming state-of-the-art approaches.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Sep 2022.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 1.
4 papers.
- 2023Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learningcited by 6
- 2024Scalable Interactive Machine Learning for Future Command and Controlcited by 2
- 2024PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Plannercited by 0
- 2025LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigationcited by 0
- 2025LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation21 Nov 2025
- 2024PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner14 Jun 2024
- 2024Scalable Interactive Machine Learning for Future Command and Control9 Feb 2024
- 2023Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning23 Sep 2023