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On utilizing geometric formations for minimizing uncertainty in 3 robot teams
Linköping University, Department of Management and Engineering, Fluid and Mechanical Engineering Systems . Linköping University, The Institute of Technology.
Linköping University, Department of Management and Engineering, Fluid and Mechanical Engineering Systems . Linköping University, The Institute of Technology.
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.

Place, publisher, year, edition, pages
2004. , 100-110 p.100-110 p.
Keyword [en]
Geometric formations, minimize uncertainty
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-15319ISBN: 9781586034146 (print)OAI: oai:DiVA.org:liu-15319DiVA: diva2:113896
Available from: 2008-10-31 Created: 2008-10-31 Last updated: 2013-11-04Bibliographically approved
In thesis
1. Multi-robot Information Fusion: Considering spatial uncertainty models
Open this publication in new window or tab >>Multi-robot Information Fusion: Considering spatial uncertainty models
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:nbn:se:liu:diva-15327 (URN)978-91-7393-813-6 (ISBN)
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
2. Information sharing in mobile robot teams: deployment with uncertainty consideration
Open this publication in new window or tab >>Information sharing in mobile robot teams: deployment with uncertainty consideration
2004 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The work presented in this thesis deals with information sharing within mobile robot teams. Main focus is on information related to team positioning and localization. The goal of this research is to find models for how to best deploy a group of robots.

The motion platforms covered are well adapted. although very important for the results. Motion models are crucial when calculating the dead reckoning of each platform. Results in the appended work show that the motion model has heavy impact on the possibility to affect the uncertainty of a robot team as new ground is deployed.

The sensor models discussed may not necessarily be what is commonly used in the research field. Instead good approximations are used, that describe the main features of the sensor type. The focus is to present different sensor types and describe how the characteristics affect the uncertainty of the robot team, not to create new sensor models. A general model for sensors with polar measurement coordinates is derived. This can be used with different sensors with the same characteristics.

A framework for sharing information within the robot team is derived. This is based on earlier work presented as "The Stochastic Map". However, the framework is tailored to suite the purpose of this work. Different estimation methods are also discussed, with main focus is on Kalman filtering techniques.

The resulting part of this work present different ideas of what should be considered when deploying a group of robots. Results show how different dist ribution of sensors will affect the localization uncertainty of the group. It is also shown how different deployment formations affect the uncertainty within the team.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2004. 52 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1105
Keyword
Information sharing, robot teams
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-23265 (URN)2689 (Local ID)91-7373-993-6 (ISBN)2689 (Archive number)2689 (OAI)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2013-11-04

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Andersson, LarsNygårds, Jonas

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