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Smooth Path Planning and Localisation

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

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Smooth Path Planning and Localisation

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  1. 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

  2. Current assisted wheelchair navigation technologies • Simple collision avoidance using proximity sensors • Traction control for unknown surfaces • Course smoothing using gyro and compass

  3. 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

  4. Potential fields • Fast real time processing • Simple representation • Well understood • Obstacle repulsion • Target or goal attraction

  5. Potential field problems Localisation Local Minima Smoothness

  6. Local minima

  7. 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.

  8. Smoothness Smaller 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” SelimTemizertemizer@ai.mit.edu

  9. 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

  10. Pattern recognition Right corner Corridor Classes Class certainty improved through data fusion techniques Local minima

  11. Manipulating potential fields Local minima

  12. 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.

  13. 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 ROBOTS Jens Hensler, Michael Blaich, Oliver Bittel

  14. Path planning solution Waypoints and goals can be mapped as a digraph, look up tables are used for classification and spanning tree patterns generated

  15. University of Kent School of Engineering and Digital Arts Thank you. Any Questions?

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