iqn talk 17 09 04

Slide1: 

An Extension to the Dynamic Window Approach for arbitrarily shaped Robots Christian Mandel A1[RoboMap], 09/17/04

Slide2: 

A1[RoboMap] - 0 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. Overview Motivation Basic principles of the Dynamic Window Approach DWA & the problem with non circular shaped robots Implementation issues: Curve Segments Table, Collision Table Computing the Trajectory Computing the Velocity Profile Rolland: new hardware platform What remains to do Preliminary Experiments

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- 1 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. Rolland: hardware platform A1[RoboMap]

Slide4: 

A1[RoboMap] - 2 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. Motivation Wheelchair in its environment Metrical & topological representation. How to navigate between decision points while taking care of dynamic obstacles? decision points voronoi edges

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- 3 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. A1[RoboMap] Basic principles of the Dynamic Window Approach1 Local navigation combined with reactive collision avoidance. 1 [Fox, Burgard, Thrun] „The Dynamic Window Approach To Collision Avoidance“ DWA computes optimal circular arc in every time step. DWA assumes: Robot velocity is a piecewise constant function in time. DWA considers: Robot has initial velocity and limited accelerations. DWA looks one curve ahead.

Slide6: 

- 4 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. A1[RoboMap] DWA & the problem with non circular shaped robots

Slide7: 

- 5 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. Curve Segments Table A1[RoboMap]

Slide8: 

- 6 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. Collision Table A1[RoboMap]

Slide9: 

- 7 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. A1[RoboMap] Algorithmic Refinements 1 Goal: reduce Size of

Slide10: 

startPose headedPose goalPose path - 8 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. A1[RoboMap] Score Function

Slide11: 

- 9 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. Computing the Optimal Path Input A1[RoboMap]

Slide12: 

- 10 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. A1[RoboMap] Algorithmic Refinements 2 Goal: reduce complexity of computation Computing the optimal path w.r.t. objective function

Slide13: 

- 11 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. A1[RoboMap] Algorithmic Refinements 3 Algorithm (witch constant arc2.length) minimise path.score

Slide14: 

- 12 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. A1[RoboMap] Computing the Velocity Profile Input Solution Path Velocities in Start & Goal Lateral Acceleration Limit Longitudinal Acceleration Limit Rotational Velocity Limit Longitudinal Velocity Limit

Slide15: 

- 13 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. A1[RoboMap] What remains to do current implementation considers only binary information from the evidence grid while doing the collision test

Slide16: 

- 14 - SFB/IQN-Kolloquium 09/17/04 An Extension to the Dynamic Window Approach for arbitrarily shaped Robots. A1[RoboMap] Preliminary Experiments