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

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

Zhu, H. (TU Delft Learning & Autonomous Control)
Martinez Claramunt, Francisco (Student TU Delft)
Ferreira de Brito, B.F. (TU Delft Learning & Autonomous Control)
Alonso-Mora, J. (TU Delft Learning & Autonomous Control)

2021

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.

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

http://resolver.tudelft.nl/uuid:1e2805bc-6834-4d27-aa4c-20ca19b87815

https://doi.org/10.1109/LRA.2021.3061073

2021-08-22

2377-3766

IEEE Robotics and Automation Letters, 6 (2), 2256-2263

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.

Institutional Repository

journal article

© 2021 H. Zhu, Francisco Martinez Claramunt, B.F. Ferreira de Brito, J. Alonso-Mora