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
Object Placement Planning and Optimization for Robot Manipulators
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-5821-7435
Department of Mechanical Engineering and Material Science, Yale University.ORCID iD: 0000-0003-4132-1217
Center for Applied Autonomous Sensor Systems (AASS), Örebro University.ORCID iD: 0000-0003-3958-6179
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0003-2965-2953
2019 (English)In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), 2019Conference paper, Published paper (Refereed)
Abstract [en]

We address the problem of planning the placement of a rigid object with a dual-arm robot in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot and c) optimizes a user-given placement objective. In addition, we need to select which robot arm to perform the placement with. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning with a novel hierarchical search for suitable placement poses. Our algorithm incrementally produces approach motions to stable placement poses, reaching placements with better objective as runtime progresses. We evaluate our approach for two different placement objectives, and observe its effectiveness even in challenging scenarios.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Motion planning, Object placing
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-264015OAI: oai:DiVA.org:kth-264015DiVA, id: diva2:1371694
Conference
International Conference on Intelligent Robots and Systems (IROS), Macau, China, November 4-8, 2019
Funder
Swedish Foundation for Strategic Research Knut and Alice Wallenberg Foundation
Note

QC 20191210

Available from: 2019-11-20 Created: 2019-11-20 Last updated: 2020-01-31Bibliographically approved
In thesis
1. Robot Manipulation Planning Among Obstacles: Grasping, Placing and Rearranging
Open this publication in new window or tab >>Robot Manipulation Planning Among Obstacles: Grasping, Placing and Rearranging
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents planning algorithms for three different robot manipulation tasks: fingertip grasping, object placing and rearranging. Herein, we place special attention on addressing these tasks in the presence of obstacles. Obstacles are frequently encountered in human-centered environments and constrain a robot's motion and ability to manipulate objects. In narrow shelves, for example, even the common task of pick-and-place becomes challenging. A shelf is difficult to navigate and many potential grasps and placements are inaccessible. Hence, to solve such tasks, specialized manipulation planning algorithms are required that can cope with the presence of obstacles.

For fingertip grasping, we first present a framework to learn models that encode which grasps a given dexterous robot hand can reach. These models are then used to facilitate planning and optimization of fingertip grasps. Next, we address the presence of obstacles and integrate fingertip grasp and motion planning to generate grasps that are reachable by a robot in complex scenes.

For object placing, we first present an algorithm that plans the placement of a grasped object among obstacles so that a user-given placement objective is maximized. We then extend this algorithm, and incorporate planning in-hand manipulation to increase the set of placements a robot can reach.

Lastly, we go beyond pure collision avoidance and study object rearrangement planning. Specifically, we consider the special case of non-prehensile rearrangement, where a robot rearranges multiple objects through pushing. First, we present how a kinodynamic motion planning algorithm can be augmented with learned models to rearrange a few target objects among movable and static obstacles. We then present how we can use Monte Carlo tree search to solve a large-scale rearrangement problem, where a robot is tasked to spatially sort many objects according to a user-assigned class membership.

Abstract [sv]

Den här avhandlingen presenterar planeringsalgoritmer för tre olika ma-nipulationsuppgifter för robotar i närheten av hinder: att greppa med hjälpav fingertopparna, att placera objekt och att arrangera om flertalet objekt iolika konfigurationer. Hinder finns oftast i människors miljöer och begränsaren robots rörelse och förmåga att manipulera objekt. Till exempel är den tillsynes enkla uppgiften att hämta och placera objekt i smala hyllor mycketsvår för robotar. Planering av manipulering i detta fall blir svårt därför attmånga rörelser och grepp kommer att kollidera med hinder. För att lösa dessauppgifter behöver man speciella algoritmer som kan hantera dessa hinder imiljön.

För grepp med fingertoppar presenterar vi ett ramverk för inlärning avmodeller som representerar vilka grepp en robothand kan utföra. Dessa mo-deller används sedan för att planera och optimera grepp som sker med hjälpav fingertopparna. Därefter integreras planeringsalgoritmen med rörelsepla-nering för att kunna planera fingertoppsgrepp där också hinder existerar iobjektets närhet.

För objektsplaceringar presenterar vi ett ramverk för att planera hur enrobot kan transportera ett greppat objekt till en placering som optimerar engiven kostnadsfunktion. Därefter utvecklar vi detta ramverk för att visa hurså kallad in-hand manipulation (att byta grepp utan att släppa objektet) kanöka antalet möjliga placeringar som roboten kan utföra.

Till slut utökar vi våra metoder bortom endast undvikande av kollisionoch studerar planering för omarrangering av objekt. Vi studerar det speciellafallet non-prehensile rearrangement där en robot måste ordna om flertaletobjekt genom att skjuta dem framför sig. Vi presenterar först hur en kino-dynamisk rörelseplaneringsalgoritm kan förbättras genom inlärda modeller.Detta görs i syftet att hitta rörelsesekvenser som kan ordna flertalet objekt tillgivna konfigurationer. Sedan presenterar vi hur vi kan använda Monte Carlo-trädsök för att lösa ett stort sådant problem där objekt ska om-arrangeras. Idetta problem ska roboten sortera objekt enligt kategorier som en användarehar specifierat.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2020. p. 52
Series
TRITA-EECS-AVL ; 2020:6
Keywords
robot manipulation planning, sampling-based planning, fingertip grasp planning, placement planning, rearrangement planning
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-266792 (URN)978-91-7873-411-5 (ISBN)
Public defence
2020-02-17, F3, Lindstedtsvägen 26, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20200124

Available from: 2020-01-24 Created: 2020-01-22 Last updated: 2020-01-24Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Conference webpage

Authority records BETA

Hang, KaiyuStork, Johannes A.Kragic, Danica

Search in DiVA

By author/editor
Haustein, Joshua AlexanderHang, KaiyuStork, Johannes A.Kragic, Danica
By organisation
Robotics, Perception and Learning, RPL
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 15 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