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Massachusetts Institute of Technology

Design of Mobile Robot for use as a Teaching Platform and Autonomous Navigation and Object Avoidance

Abstract

dc:description.abstract

There is no end to the depth of learning possible In the field of robotics. However, we are many times limited by the methods we choose to teach ourselves and others. As a result, I looked at the current teaching platforms used, along with others available in the market, and found there was room for something more suitable for my coursework taken so far. This thesis covers the design and implementation of a new autonomous mobile robot teaching platform, which has now been adopted by MIT’s Introduction to Robotics class. This involves the design requirements gathered from looking at what both students and instructors needed to improve their current platform for teaching many of the principles of robotics. The end result has been a successful, open-source mobile robot that is capable of a plethora of autonomous tasks and a high level of modularity—useful for applications in whatever designs are produced by students for the ever-changing term projects.

Degree

thesis:*
Name thesis:degree_name
Bachelor
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2023

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Thompson, Kyle
Advisor dc:contributor.advisor
  • Chin, Harrison

Rights

dc:rights
Statement dc:rights
  • Attribution 4.0 International (CC BY 4.0)
  • Copyright retained by author(s)

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/151834
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/151834

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

Thompson, Kyle. Design of Mobile Robot for use as a Teaching Platform and Autonomous Navigation and Object Avoidance. Massachusetts Institute of Technology, 2023. https://hdl.handle.net/1721.1/151834