Back to results

Massachusetts Institute of Technology

Perception and Motion Planning for Autonomous Surface Vehicles in Aquaculture

Abstract

dc:description.abstract

The "Oystermaran" USV was designed and developed by students at MIT SeaGrant to resolve the oyster basket flipping bottleneck that slows down oyster farming at Ward Aquafarms. The state of the USV requires remote operation within close distance of the vessel. In this thesis, we present an automated solution that will enable the Oystermaran to autonomously depart from its parked location, navigate to its destination, execute the flipping tasks, and return to a designated location with little to no human intervention. The details explored in this project and discussed in the thesis focus on the perception and motion planning aspects of the proposed autonomous system. Our results show a capable basket detection algorithm based on our collected dataset. The system’s path planning approach is also proven sufficient in simulation. Additional data collection with further testing may be required to fully realize the system on board the Oystermaran.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Zhang, Jerry
Advisors dc:contributor.advisor
  • Leonard, John J.
  • Bennett, Andrew

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright MIT

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/144936
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/144936

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Zhang, Jerry. Perception and Motion Planning for Autonomous Surface Vehicles in Aquaculture. Massachusetts Institute of Technology, 2022. https://hdl.handle.net/1721.1/144936