Predicting the Future Motion Trajectory of Scuba Divers for Human-Robot Interaction

Thumbnail Image

Persistent link to this item

View Statistics

Journal Title

Journal ISSN

Volume Title


Predicting the Future Motion Trajectory of Scuba Divers for Human-Robot Interaction

Published Date






Autonomous Underwater Vehicles (AUVs) can be effective collaborators to human scuba divers in many applications, such as environmental surveying, mapping, or infrastructure repair. However, for these applications to be realized in the real world, it is essential that robots are able to both lead and follow their human collaborators. Current algorithms for diver following are not robust to non-uniform changes in the motion of the diver, and no framework currently exists for robots to lead divers. One method to improve the robustness of diver following and enable the capability of diver leading is to predict the future motion of a diver. In this paper, we present a vision-based approach for AUVs to predict the future motion trajectory of divers, utilizing the Vanilla-LSTM and Social-LSTM temporal deep neural networks. We also present a dense optical flow-based method to stabilize the input annotations from the dataset and reduce the effects of camera ego-motion. We analyze the results of these models on scenarios ranging from swimming pools to the open ocean and present the model's accuracy at varying prediction lengths. We find that our LSTM models can generate predictions with significant accuracy 1.5 seconds into the future and that stabilizing LSTM models significantly improves trajectory prediction performance.



Related to



Series/Report Number

Funding information

This research was supported by the Undergraduate Research Opportunities Program (UROP), the US National Science Foundation awards IIS-#1845364 & #00074041 and the Minnesota Robotics Institute Seed Grant

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Agarwal, Tanmay; Fulton, Michael; Sattar, Junaed. (2021). Predicting the Future Motion Trajectory of Scuba Divers for Human-Robot Interaction. Retrieved from the University Digital Conservancy,

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.