liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
Multi-robot Information Fusion: Considering spatial uncertainty models
Linköping University, Department of Management and Engineering, Fluid and Mechanical Engineering Systems . Linköping University, The Institute of Technology.
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The work presented in this thesis covers the topic of deployment for mobile robot teams. By connecting robots in teams they can perform a better job than each individual is capable of. It also gives redundancy, increases robustness, provides scalability, and increases efficiency. Multi-robot Information Fusion also results in a broader perspective for decision making. This thesis focuses on methods for estimating formation and trajectories and how these can be used for deployment of a robot team. The problems covered discuss what impact trajectories and formation have on the total uncertainty when exploring unknown areas. The deployment problem is approached using a centralized Kalman filter, for investigation of how team formation affects error propagation. Trajectory estimation is done using a smoother, where all information is used not only to estimate the trajectory of each robot, but also to align trajectories from different robots. Both simulation and experimental results are presented in the appended papers. It is shown that sensor placements can substantially affect uncertainty during deployment. When deploying a robot team the formation can be used as a tool for balancing error propagation among the robot states. A robust algorithm for associating rendezvous observations to align robot trajectories is also presented. Trajectory alignment is used as an efficient and cost-effective method for joining mapping information within robot teams. When working with robot teams, sensor placement and formation should be considered to obtain the maximum from the system. It is also of great value to mix robots with different characteristics since it is shown that using sensor fusion the robots can inherit each other’s characteristics if sensors are used correctly. Information sharing requires modularity and general models, which consumecomputational resources. Over time computer resources will become cheaper, allowing for distribution, and each robot will become more self-contained. Together with increased wireless bandwidth this will enable larger numbers of robots to cooperate.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press , 2008. , 82 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1209
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-15327ISBN: 978-91-7393-813-6 (print)OAI: oai:DiVA.org:liu-15327DiVA: diva2:113903
Public defence
2008-09-19, A35, Hus A, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2008-11-05 Created: 2008-10-31 Last updated: 2009-04-22Bibliographically approved
List of papers
1. On Sensor Fusion Between a Pair Of Heterogeneous Robots
Open this publication in new window or tab >>On Sensor Fusion Between a Pair Of Heterogeneous Robots
2003 (English)In: Proceedings of the 6th International Conference on Information Fusion, Cairns, Australia, 8th–11th July, 2003, 2003, 1287-1294 p.Chapter in book (Other (popular science, discussion, etc.))
Abstract [en]

This paper present work in progress, aiming to find models that can be used as guidelines in how to best deploy a team of heterogeneous robots to solve a given task. That is, this research is focusing on how much each robot can increase the certainty about its own position and orientation in a global perspective as well as relative to other team members. It also, in some extent investigate what happens too the uncertainty and correlation within the group. The motive is to provide an insight into the utility of fusing position information communicated within the team, in aspect of increased accuracy. It also investigates in what way different usage of sensors will effect the results. Since the goal of the paper is insight, the minimum configuration of two robots and two landmarks is studied although the results are scalable to larger robot teams. The results show that by placing a camera on the moving robot measuring the bearing of a stationary robot gives better estimation of the moving robots orientation, which can be crucial for the solution of the association problem.

Keyword
Cooperative, Robot, Navigation, Distributed, Fusion, SLAM, CLAM
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-15106 (URN)0-9721844-4-9 (ISBN)
Available from: 2008-10-16 Created: 2008-10-16 Last updated: 2013-11-04Bibliographically approved
2. On utilizing geometric formations for minimizing uncertainty in 3 robot teams
Open this publication in new window or tab >>On utilizing geometric formations for minimizing uncertainty in 3 robot teams
2004 (English)In: Proceedings of The 8th Conference on Intelligent Autonomous Systems, IAS-8, Amsterdam, Netherlands, 10th–12th March, 2004, , 100-110 p.100-110 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents work in progress, aiming to find models that can be used as guidelines in how to best deploy a team of robots to solve given tasks. The motive is to provide an insight into the utility in fusing position information communicated within the team, in aspect of increased accuracy in position and orientation estimate. The minimum configuration of three robots is studied. The robots are equipped with camera-like sensors that make omni directional bearing measurements to eachother. To reduce the free variables the formations are restricted so that two subordinate robots are located at the same distance to a master robot. For this configuration the information matrix is explored. A few formations are chosen and studied further, through simulations. The simulations show that there exists a breakpoint in traveled distance, where the formation minimizing orientation uncertainty for the master robot, changes from a column-formation to a line formation. The simulations also show that a line or column formations are not a good choice when balansed position uncertainty is required. In the case of balansing position uncertainty, a triangular formation is better.

Publisher
100-110 p.
Keyword
Geometric formations, minimize uncertainty
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-15319 (URN)9781586034146 (ISBN)
Available from: 2008-10-31 Created: 2008-10-31 Last updated: 2013-11-04Bibliographically approved
3. Effects on Uncertainty Utilizing Formation-Planning in Robot Teams
Open this publication in new window or tab >>Effects on Uncertainty Utilizing Formation-Planning in Robot Teams
2004 (English)In: Proceedings of The 5th Symposium on Intelligent Autonomous Vehicles, IAV 2004, Lisbon, Portugal, 5th–7th July 2004, 2004Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents work in progress, aiming to find models that can be used as guidelines in how to best deploy a team of robots to solve given tasks. The motive is to provide an insight into the utility in fusing position information communicated within the team, in aspect of increased accuracy in position and orientation estimate. The minimum configuration of three robots moving over a flat surface is studied. The robots are equipped with omni-directional cameralike sensors that make bearing measurements to each other. To reduce the free variables the formations are restricted so that two subordinate robots are located at the same distance to a master robot. For this configuration the information matrix is derived and studied. There are numerical results presented that indicate how the uncertainty of the master robot changes depending of the bearing to the subordinate robots and the distance traveled.

Keyword
Multi-Robot, Cooperative, Formation, Localization
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-15320 (URN)
Available from: 2008-10-31 Created: 2008-10-31 Last updated: 2013-11-04Bibliographically approved
4. C-SAM: Multi-Robot SLAM using Square Root Information Smoothing
Open this publication in new window or tab >>C-SAM: Multi-Robot SLAM using Square Root Information Smoothing
2008 (English)In: Proceedings of International Conference on Robotics and Automation, ICRA 2008, Pasadena, CA, USA, 19th–23rd May, IEEE Xplore , 2008, 2798-2805 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents collaborative smoothing and mapping (C-SAM) as a viable approach to the multi-robot map- alignment problem. This method enables a team of robots to build joint maps with or without initial knowledge of their relative poses. To accomplish the simultaneous localization and mapping this method uses square root information smoothing (SRIS). In contrast to traditional extended Kalman filter (EKF) methods the smoothing does not exclude any information and is therefore also better equipped to deal with non-linear process and measurement models. The method proposed does not require the collaborative robots to have initial correspondence. The key contribution of this work is an optimal smoothing algorithm for merging maps that are created by different robots independently or in groups. The method not only joins maps from different robots, it also recovers the complete robot trajectory for each robot involved in the map joining. It is also shown how data association between duplicate features is done and how this reduces uncertainty in the complete map. Two simulated scenarios are presented where the C-SAM algorithm is applied on two individually created maps. One basically joins two maps resulting in a large map while the other shows a scenario where sensor extension is carried out.

Place, publisher, year, edition, pages
IEEE Xplore, 2008
Keyword
C-SAM, SLAM, fusion, multi, robot
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-15322 (URN)10.1109/ROBOT.2008.4543634 (DOI)978-1-4244-1646-2 (ISBN)
Available from: 2008-10-31 Created: 2008-10-31 Last updated: 2009-04-22Bibliographically approved
5. Sensor Data Fusion for Terrain Exploration by Collaborating Unmanned Ground Vehicles
Open this publication in new window or tab >>Sensor Data Fusion for Terrain Exploration by Collaborating Unmanned Ground Vehicles
2008 (English)In: Proceedings of the 11th International Conference on Information Fusion, FUSION 2008, Cologne, Germany, 30th June–3rd July, IEEE Xplore , 2008, 1214-1221 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents work in progress for the pre-Runners project. The goal is to experimentally demonstrate the value of unmanned ground vehicles (UGVs) in collaboration with a main vehicle in an outdoor setting. With uneven terrain and unexpected obstacles the main vehicle benefits from a priori information of the terrain ahead. This information can be gathered by a smaller, more agile, and risks tolerant autonomous “prerunner”. The results presented, represent the first steps toward the important task of determining the traversable surfaces and communicating the results within the team. The information sharing between vehicles is based on Collaborative Smoothing and Mapping (C-SAM). The horizontal position is also estimated within the C-SAM. In parallel the vertical component and orientation is estimated by a filter fusing data from odometry, an imu and two lasers to allow computation of traversability maps to be shared within the team.

Place, publisher, year, edition, pages
IEEE Xplore, 2008
Keyword
Collaboration, SLAM, SAM, sensor data fusion, data association, Kalman filtering, traversability estimation
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-15323 (URN)10.1109/ICIF.2008.4632348 (DOI)978-3-8007-3092-6 (ISBN)
Conference
11th International Conference on Information Fusion, FUSION 2008, Cologne, Germany, 30th June–3rd July
Available from: 2008-10-31 Created: 2008-10-31 Last updated: 2012-12-03Bibliographically approved
6. On Multi-robot Map Fusion by Inter-robot Observations
Open this publication in new window or tab >>On Multi-robot Map Fusion by Inter-robot Observations
2009 (English)In: In proceedings of 12th International Conference on Information Fusion, 2009Conference paper, Published paper (Other academic)
Abstract [en]

This paper addresses the problem of aligning and fusing maps built by multiple robots. The proposed method for solving the multi-robot map alignment problem relies on inter-robot observations to seed the alignment processing and find a transformation between the map reference frames. The method enables one to join maps from robots with or without initial correspondence. However, the poses of each robot during an inter-robot observation need to be synchronized. In this work the method is applied onto Collaborative Smoothing and Mapping (C-SAM), a smoothing approach for merging maps that are created by different robots independently or in teams. In contrast to traditional Extended Kalman Filter (EKF) or Particle Filtering (PF) methods the smoothing does not exclude any information and is therefore better equipped to deal with non-linear process and measurement models. The algorithm is also proven to be useful in two different experiments showing the robustness of the algorithm. The experiments show that alignment can be conducted using only inter-robot observation in both unguided terrain as well as in terrain with many false observations. The key contribution of this work is a robust algorithm for solving the association problem and eliminating false observations when doing multi-robot map alignment using inter-robot observations during a rendezvous.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-15324 (URN)000273560001076 ()
Conference
12th International Conference on Information Fusion
Available from: 2008-10-31 Created: 2008-10-31 Last updated: 2010-08-12Bibliographically approved

Open Access in DiVA

fulltext(2493 kB)1643 downloads
File information
File name FULLTEXT01.pdfFile size 2493 kBChecksum SHA-512
10841905f8d6397ddc2f8300f3bdf4aa1afdb08561c1d14eba6a54d0f2b2341cfe626880898c8c80ce44cf6dafab2de3cdcb015b137130650582e73ea00f2aea
Type fulltextMimetype application/pdf
cover(507 kB)79 downloads
File information
File name COVER01.pdfFile size 507 kBChecksum SHA-512
12cffae34f5417100b394177c24d5faa1978f1b6718d99bdf766ac13a23101620734242b267d647296d512d52a60b2b7c4ddf4b3700ff08688efbf97d9ed5d0e
Type coverMimetype application/pdf

Authority records BETA

Andersson, Lars

Search in DiVA

By author/editor
Andersson, Lars
By organisation
Fluid and Mechanical Engineering Systems The Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 1643 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

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 2322 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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