Motion Prediction Based on Multiple Futures for Dynamic Obstacle Avoidance of Mobile Robots
Paper in proceeding, 2021

The ability to decide and adjust actions according to motion prediction of dynamic obstacles offers a flexible planning scheme and ampler reaction time to avoid potential impact. Prediction-based collision avoidance implies a two-stage decision-making process from motion prediction to action planning. One of the challenges in motion prediction is the movements of objects are usually non-deterministic and governed by multimodal models. Many studies have been made on motion prediction of dynamic obstacles and action planning for mobile robots separately. The objective of this work is to explore their coherence in terms of multiple future predictions by combining a data-driven motion prediction approach with a model-based control strategy. More specifically, we integrate motion prediction from deep learning models, Mixture Density Networks (MDNs) with a Non-linear Model Predictive Control (NMPC) framework. The deep learning models produce the multimodal probability distribution of future positions of dynamic obstacles, which is utilized by the MPC controller as a constraint. We show via simulation that the selected model provides valid predictions of motion in a dynamic environment. The prediction result endows the controller with the capability to avoid dynamic obstacles in advance.

Author

Ze Zhang

Chalmers, Electrical Engineering, Systems and control, Automation

Emmanuel Dean

Chalmers, Electrical Engineering, Systems and control, Automation

Yiannis Karayiannidis

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Knut Åkesson

Chalmers, Electrical Engineering, Systems and control, Automation

IEEE International Conference on Automation Science and Engineering

21618070 (ISSN) 21618089 (eISSN)

Vol. 2021-August 475-481

17th IEEE International Conference on Automation Science and Engineering, CASE 2021
Lyon, France,

Subject Categories

Bioinformatics (Computational Biology)

Robotics

Control Engineering

DOI

10.1109/CASE49439.2021.9551463

More information

Latest update

11/4/2021