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Sensor Data Fusion for Terrain Exploration by Collaborating Unmanned Ground Vehicles
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.
Linköping University, Department of Management and Engineering, Fluid and Mechanical Engineering Systems. Linköping University, The Institute of Technology.
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. 1214-1221 p.
Keyword [en]
Collaboration, SLAM, SAM, sensor data fusion, data association, Kalman filtering, traversability estimation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-15323DOI: 10.1109/ICIF.2008.4632348ISBN: 978-3-8007-3092-6 (print)OAI: oai:DiVA.org:liu-15323DiVA: diva2:113899
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
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. Mobile Robot Traversability Mapping: For Outdoor Navigation
Open this publication in new window or tab >>Mobile Robot Traversability Mapping: For Outdoor Navigation
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

To avoid getting stuck or causing damage to a vehicle or its surroundings a driver must be able to identify obstacles and adapt speed to ground conditions. An automatically controlled vehicle must be able to handle these identifications and adjustments by itself using sensors, actuators and control software. By storing properties of the surroundings in a map, a vehicle revisiting an area can benefit from prior information.

Rough ground may cause oscillations in the vehicle chassis. These can be measured by on-board motion sensors. For obstacle detection, a representation of the geometry of the surroundings can be created using range sensors. Information on where it is suitable to drive, called traversability, can be generated based on these kinds of sensor measurements.

In this work, real semi-autonomous mobile robots have been used to create traverasbility maps in both simulated and real outdoor environments. Seeking out problems through experiments and implementing algorithms in an attempt to solve them has been the core of the work.

Finding large obstacles in the vicinity of a vehicle is seldom a problem; accurately identifying small near-ground obstacles is much more difficult, however. The work additionally includes both high-level path planning, where no obstacle details are considered, and more detailed planning for finding an obstacle free path. How prior maps can be matched and merged in preparation for path planning operations is also shown. To prevent collisions with unforeseen objects, up-to-date traversability information is used in local-area navigation and obstacle avoidance.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 122 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1564
Keyword
Traversability, Laser, Roughness, Mapping, Planning, Mobile robot, Navigation, Implementation
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-85937 (URN)LIU-TEK-LIC-2012:49 (Local ID)978-91-7519-726-5 (ISBN)LIU-TEK-LIC-2012:49 (Archive number)LIU-TEK-LIC-2012:49 (OAI)
Presentation
2012-12-14, A39, A-huset, Campus Valla, Linköpings universitet, Linköping, 14:15 (English)
Opponent
Supervisors
Available from: 2012-12-03 Created: 2012-12-03 Last updated: 2012-12-06Bibliographically approved

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Nordin, PeterAndersson, LarsNygårds, Jonas

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