Paper Info
Title
Learning Interaction-Aware Trajectory Predictions For Decentralized Multi-Robot Motion Planning In Dynamic Environments
Abstract
This letter 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 letter, 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.
Year
DOI
Venue
2021
10.1109/LRA.2021.3061073
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Deep learning methods, motion and path planning, path planning for multiple mobile robots or agents
Journal
6
Issue
ISSN
Citations 
2
2377-3766
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Hai Zhu18722.69
Francisco Martinez Claramunt200.34
Bruno Brito312.39
Javier Alonso-Mora437534.15