AUTONOMOUS MOTION PLANNING USING NEURAL NETWORK Presented by: Debasish Sahu University of Hamburg, Department of Informatics 27-10-2014

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Overview

¨  Introduction ¨  Related Works ¨  Why RBF ¨  System Architecture ¨  Digital Map Generation ¨  RBF Network ¨  RBF Function ¨  Further Issues ¨  Regularization ¨  Design of Learning Algorithm ¨  Simulations ¨  Comparison ¨  Conclusions ¨  References ¨  Questions

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Introduction

Objective- ¨  Generate a smooth feasible

path to reach goal

Challenges- ¨  Unstructured Environment ¨  Limited Sensing Capability ¨  Non holonomic constraint of

vehicle

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Related Works

¨  Heuristic Approaches - Dijkstra, A*, D*, ARAs

¨  Probabilistic Approaches

¨  Single query planners

¨  EST, RRT, Path directed subdivision trees

¨  Clearance based shortest path planner

¨  Voronoi diagram based

¨  SVM

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Why RBF

¨  Universal Approximator

¨  Can approximate any non linear function

¨  Generated smoothness can be executed easily

¨  Fast learning rate

¨  Insensitive to environment

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System Architecture 6

Digital Map Generation 7

RBF Network 8

RBF Function

•  Radial symmetry •  Smoothness •  Simple representation for

multi input

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Further issues!

¨  Unstable solution of matrix inverse function

¨  Oversampling due to noise

Solution:

¨  Use regularization network

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Regularization 11

Design of Learning Algorithm

¨ Computing the width

¨ Adjusting centers

¨ Adjusting weights

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Design of Learning Algorithm Contd.. 13

Simulations 14

Comparison of Results 15

Conclusion

¨ Smooth and stable collision free path

¨ Satisfy vehicle kinematic constraints

¨ Easy computations

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References

1.  Ferguson, D.; Likhachev M.; Stentz A. A Guide to Heuristic-based Path Planning 2.  Touretzky D.; Thompson E. Cognitive Robotics CMU 3.  Wilfong, G.T. Motion planning for an autonomous vehicle. In Autonomous Robot Vehicles;

Springer: New York, NY, USA, 1990; pp. 391–395. 4.  Bruce, J.; Veloso, M. Real-Time randomized path planning for robot navigation. Intell. Robot.

Syst. IEEE RSJ Int. Conf. 2002, 3, 2383–2388. 5.  Gindele, T.; Jagszent, D.; Pitzer, B.; Dillmann, R. Design of the planner of Team AnnieWAY’s

autonomous vehicle used in the DARPA Urban Challenge 2007. In Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, Holland, 4–6 June 2008; pp. 1131–1136.

6.  Garrido,S.;Moreno,L.;Blanco,D.Voronoidiagramandfastmarchingappliedtopathplanning. In Proceedings of the 2006 IEEE International Conference on Robotics and Automation (ICRA 2006), Orlando, FL, USA, 15–19 May 2006; pp. 3049–3054.

7.  Pomerleau, D.A. Alvinn: An Autonomous Land Vehicle in a Neural Network. In Advances in Neural Information Processing Systems 1; Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA, 1989; pp. 305–313.

8.  Yang, S.X.; Meng, M. An efficient neural network approach to dynamic robot motion planning. Neural Netw. 2000, 13, 143–148.

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Questions and Discussion

Thank You.

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