Predicting the Future Motion of Divers for Enhanced Underwater Human-Robot Collaboration

Tanmay Agarwal, Michael Fulton, Junaed Sattar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5379-5386
Number of pages8
ISBN (Electronic)9781665417143
DOIs
StatePublished - 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 - Prague, Czech Republic
Duration: Sep 27 2021Oct 1 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Country/TerritoryCzech Republic
CityPrague
Period9/27/2110/1/21

Bibliographical note

Funding Information:
The authors are with the Department of Computer Science and Engineering and the Minnesota Robotics Institute, University of Minnesota Twin Cities, Minneapolis, MN, USA. {1agarw139,2fulto081,3junaed}@umn.edu *This work was supported by the US National Science Foundation awards IIS-#1845364 & #00074041, the Minnesota Robotics Institute Seed Grant, and the Undergraduate Research Opportunities Program (UROP) by the Office of Undergraduate Research at the University of Minnesota.

Publisher Copyright:
© 2021 IEEE.

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