smooth path planning and localisation
DESCRIPTION
University of Kent School of Engineering and Digital Arts. Smooth Path Planning and Localisation. Michael Gillham University of Kent SYSIASS Meeting ISEN Lille 24.06.11. Current assisted wheelchair navigation technologies. Simple collision avoidance using proximity sensors - PowerPoint PPT PresentationTRANSCRIPT
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Smooth Path Planning and Localisation
University of Kent School of Engineering and Digital Arts
Michael GillhamUniversity of Kent
SYSIASS Meeting ISEN Lille 24.06.11
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Current assisted wheelchair navigation technologies
• Simple collision avoidance using proximity sensors
• Traction control for unknown surfaces
• Course smoothing using gyro and compass
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Future technologies
• Complex dynamic and static real time hazard detection, collision and avoidance
• Assisted waypoint/door traversing• Course/trajectory smoothing
improvements• Path planning for autonomous navigation• Course/trajectory optimization
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Potential fields
• Fast real time processing
• Simple representation• Well understood• Obstacle repulsion• Target or goal attraction
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Potential field problems
LocalisationLocal MinimaSmoothness
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Local minima
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Localisation
Occupancy grid based mapping offers the possibility of localisation through room classification, both locally within that room and globally on higher level mapping.Fusing other sensor data improves the certainty.
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SmoothnessSmaller tick mark period = 10 cm Larger tick mark period = 100 cm Green dots are obstacles. Blue dot is the target. Agent starts in upper right corner with heading = 0 degrees (facing +x axis)
White path is traversed with potential field method. Cyan path is traversed with human model.
“Comparison of the Human Model and Potential Field Method for Navigation”Selim Temizer [email protected]
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Weightless Neural Networks
• Pattern recognition from one shot learning• Network performs simple operations
avoiding inefficient floating point arithmetic• Fast real time processing• No null output
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Pattern recognitionObstacle
Obstacle
Robot
Sonar
Right corner
Corridor
ClassesClass certainty improved through data fusion techniques
Local minima
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Manipulating potential fields
Local minima
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Smoothness solution
One problem is the angle of approach to waypoints
such as corners and doors.
The solution is to use WNN pattern recognition to determine the class of
waypoint and use pre-determined potential
fields to manipulate the trajectory.
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Localisation solution
Localisation obtained from fused sensor data for room occupancy pattern recognition and way point pattern recognition using layered WNNs.
ADABOOST BASED DOOR DETECTION FOR MOBILE ROBOTSJens Hensler, Michael Blaich, Oliver Bittel
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Path planning solution
1
45
6
32
7
611
5
9
7
3
4
13
4
3
9
Waypoints and goals can be mapped as a digraph, look up tables are used for classification and spanning tree patterns generated
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Thank you.
Any Questions?
University of Kent School of Engineering and Digital Arts