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University of Ontario Institute of Technology

Manipulator’s configuration design, excavation path generation, and underground object detection for an autonomous electric excavator

Abstract

dc:description.abstract

Construction is an industrial sector that requires high labor costs and is exposed to harsh and hazardous environmental conditions. As a solution to these problems, an autonomous excavator is expected in high demand. To increase the energy efficiency in autonomous excavators, and increase the safety of operation for them, the objectives of this research are threefolds. The first one is to design and fabricate an excavator with parallel electrical linear actuators. The second one is to develop and test the PSO-based and PFM-Based path generation algorithms for this excavator in order to save energy, maintain the digging efficiency, and avoid colliding with underground objects. In addition, the third one is to detect the metallic pipes and electricity carrying wires underground, using two inexpensive magnetometer sensors attached to the bucket of the autonomous excavator, and computer vision for verification of digging and motion accuracy.

Degree

thesis:*
Name thesis:degree_name
Master of Applied Science (MASc)
Discipline thesis:degree_discipline
Mechanical Engineering
Grantor
University of Ontario Institute of Technology
Year dc:date.issued
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Ahmadi Khiyavi, Omid
Advisors dc:contributor.advisor
  • Seo, Jaho
  • Lin, Xianke

Subjects

dc:subject × 5

Rights

Language dc:language.iso
en

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/10155/1562
OAI identifier oai:identifier
oai:ontariotechu.scholaris.ca:10155/1562

Chain of custody

source
Harvested from
Ontario Institute of Technology
Base URL
ontariotechu.scholaris.ca/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Ahmadi Khiyavi, Omid. Manipulator’s configuration design, excavation path generation, and underground object detection for an autonomous electric excavator. University of Ontario Institute of Technology, 2022. https://hdl.handle.net/10155/1562