Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Robotic Grasping of Large Objects for Collaborative Manipulation
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In near future, robots are envisioned to work alongside humans in professional anddomestic environments without significant restructuring of workspace. Roboticsystems in such setups must be adept at observation, analysis and rational de-cision making. To coexist in an environment, humans and robots will need tointeract and cooperate for multiple tasks. A fundamental such task is the manip-ulation of large objects in work environments which requires cooperation betweenmultiple manipulating agents for load sharing. Collaborative manipulation hasbeen studied in the literature with the focus on multi-agent planning and controlstrategies. However, for a collaborative manipulation task, grasp planning alsoplays a pivotal role in cooperation and task completion.In this work, a novel approach is proposed for collaborative grasping and manipu-lation of large unknown objects. The manipulation task was defined as a sequenceof poses and expected external wrench acting on the target object. In a two-agentmanipulation task, the proposed approach selects a grasp for the second agentafter observing the grasp location of the first agent. The solution is computed ina way that it minimizes the grasp wrenches by load sharing between both agents.To verify the proposed methodology, an online system for human-robot manipu-lation of unknown objects was developed. The system utilized depth informationfrom a fixed Kinect sensor for perception and decision making for a human-robotcollaborative lift-up. Experiments with multiple objects substantiated that theproposed method results in an optimal load sharing despite limited informationand partial observability.

Place, publisher, year, edition, pages
2017. , 69 p.
Keyword [en]
Grasp planning, Multi-agent grasping, Collaborative manipulation, Load sharing
National Category
Robotics
Identifiers
URN: urn:nbn:se:ltu:diva-65866OAI: oai:DiVA.org:ltu-65866DiVA: diva2:1145271
Subject / course
Student thesis, at least 30 credits
Educational program
Space Engineering, master's level
Examiners
Available from: 2017-09-28 Created: 2017-09-28 Last updated: 2017-09-28Bibliographically approved

Open Access in DiVA

fulltext(7086 kB)54 downloads
File information
File name FULLTEXT01.pdfFile size 7086 kBChecksum SHA-512
be6828bd30f55cddb9a664a6a4ac035c07769f7b24b7acf8cf56f4a7a1afca4841a4eccbd4a93c2c1ba00cd23dd9e340b259413e97348ed34df04472fdec53ac
Type fulltextMimetype application/pdf

By organisation
Space Technology
Robotics

Search outside of DiVA

GoogleGoogle Scholar
Total: 54 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 26 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf