Omnibot: A Small Versatile Robotic Platform Capable of Air, Ground, and Underwater Operation

Dario Canelon-Suarez, Youbing Wang, Nikos Papanikolopoulos

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

Abstract

Autonomous systems are increasingly sought after to carry out complex tasks yet most available systems are designed to operate in a limited environment. By combining different mechanisms into a single design, a new kind of universal robotic platform called the Omnibot is proposed, which can fly, move on the ground, move on the surface, and dive into the water at the same time. To achieve this goal, a quadrotor configuration, screwdrive drivetrain, and a bladder-based buoyancy unit are proposed as a robotic design and a proof-of-concept platform is developed. Preliminary results demonstrate that the system is lightweight, cost-effective, versatile, and able to operate in the four kinds of environments and switch between them seamlessly without changing its hardware. The proposed design would enable a robotic system to switch among its methods for locomotion and choose the best mode according to the current goal, environment, and the internal power supply conditions, thus making it especially suited to carry out complex missions in changing and unpredictable environments.

Original languageEnglish (US)
Title of host publication2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1742-1747
Number of pages6
ISBN (Electronic)9781728142777
DOIs
StatePublished - Sep 2020
Externally publishedYes
Event2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020 - Athens, Greece
Duration: Sep 1 2020Sep 4 2020

Publication series

Name2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020

Conference

Conference2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020
CountryGreece
CityAthens
Period9/1/209/4/20

Bibliographical note

Funding Information:
VI. ACKNOWLEDGEMENTS This material is based upon work partially supported by the Corn Growers Association of MN, the Minnesota Robotics Institute (MnRI), Honeywell, and the National Science Foundation through grants #CNS-1432957, #CNS-1544887, #CNS-1531330, and #CNS-1939033. USDA/NIFA has also supported this work through grant 2020-67021-30755.

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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