Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

Hai Zhu, Francisco Martinez Claramunt, Bruno Brito, Javier Alonso-Mora

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)
69 Downloads (Pure)


This paper presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each robot needs accurate motion predictions of neighboring robots to achieve predictive collision avoidance. These motion predictions can be obtained among robots by sharing their future planned trajectories with each other via communication. However, such communication may not be available nor reliable in practice. In this paper, we introduce a novel trajectory prediction model based on recurrent neural networks (RNN) that can learn multi-robot motion behaviors from demonstrated trajectories generated using a centralized sequential planner. The learned model can run efficiently online for each robot and provide interaction-aware trajectory predictions of its neighbors based on observations of their history states. We then incorporate the trajectory prediction model into a decentralized model predictive control (MPC) framework for multi-robot collision avoidance. Simulation results show that our decentralized approach can achieve a comparable level of performance to a centralized planner while being communication-free and scalable to a large number of robots. We also validate our approach with a team of quadrotors in real-world experiments.

Original languageEnglish
Pages (from-to)2256-2263
JournalIEEE Robotics and Automation Letters
Issue number2
Publication statusPublished - 2021

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Collision avoidance
  • Deep Learning Methods
  • Dynamics
  • Motion and Path Planning
  • Path Planning for Multiple Mobile Robots or Agents
  • Planning
  • Predictive models
  • Recurrent neural networks
  • Robots
  • Trajectory


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