Prescient Collision-Free Navigation of Mobile Robots with Iterative Multimodal Motion Prediction of Dynamic Obstacles
Journal article, 2023

To explore safe interactions between a mobile robot and dynamic obstacles, this paper presents a comprehensive approach to collision-free navigation in dynamic indoor environments. The approach integrates multimodal motion predictions of dynamic obstacles with predictive control for obstacle avoidance. Multimodal Motion Prediction (MMP) is achieved by a deep-learning method that predicts multiple plausible future positions. By repeating the MMP for each time offset in the future, multi-time-step MMPs are obtained. A nonlinear Model Predictive Control (MPC) solver uses the prediction outcomes to achieve collision-free trajectory tracking for the mobile robot. The proposed integration of multimodal motion prediction and trajectory tracking outperforms other non-deep-learning methods in complex scenarios. The approach enables safe interaction between the mobile robot and stochastic dynamic obstacles.

Collision avoidance

deep learning methods

Collision avoidance

Uncertainty

Vehicle dynamics

Trajectory

Robots

Mobile robots

Dynamics

autonomous agents

Author

Ze Zhang

Chalmers, Electrical Engineering, Systems and control

Hadi Hajieghrary

Chalmers, Electrical Engineering, Systems and control

Emmanuel Dean

Chalmers, Electrical Engineering, Systems and control

Knut Åkesson

Chalmers, Electrical Engineering, Systems and control

IEEE Robotics and Automation Letters

23773766 (eISSN)

Vol. 8 9 5488-5495

AIHURO-Intelligent human-robot collaboration

VINNOVA (2022-03012), 2023-02-01 -- 2026-01-31.

Subject Categories

Robotics

DOI

10.1109/LRA.2023.3296333

More information

Latest update

9/11/2024