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

Current assisted wheelchair navigation technologies

• Simple collision avoidance using proximity sensors

• Traction control for unknown surfaces

• Course smoothing using gyro and compass

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

Potential fields

• Fast real time processing

• Simple representation• Well understood• Obstacle repulsion• Target or goal attraction

Potential field problems

LocalisationLocal MinimaSmoothness

Local minima

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.

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 temizer@ai.mit.edu

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

Pattern recognitionObstacle

Obstacle

Robot

Sonar

Right corner

Corridor

ClassesClass certainty improved through data fusion techniques

Local minima

Manipulating potential fields

Local minima

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.

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

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

Thank you.

Any Questions?

University of Kent School of Engineering and Digital Arts