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Extended Potential Field Method. Adam A. Gonthier MEAM 620 Final Project 3/19/2006. Can use continuous configuration space Therefore, avoids complexities of discrete planning Field is modeled by potential functions . Benefits: Local method Does not require global map

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Extended potential field method

Extended Potential Field Method

Adam A. Gonthier

MEAM 620 Final Project

3/19/2006


Potential field approach

Can use continuous configuration space

Therefore, avoids complexities of discrete planning

Field is modeled by potential functions

Benefits:

Local method

Does not require global map

No prior knowledge of obstacles required

Drawbacks:

Get caught in local minima

Does not find optimal path

Obstacles, robot modeled as points; in real world, they are not points

Potential Field Approach


Potential field approach1
Potential Field Approach

  • Goal is modeled as attractive force

  • Obstacles modeled as repulsive forces

  • Robot modeled as point

  • Robot follows gradient of force field, stopping at goal point

2-D Symmetric Gaussian Obstacle

Parabolic Goal Well

Total Potential Field


Potential field algorithm
Potential Field Algorithm

  • Very simple algorithm

    • Start at initial point

    • Find path opposite to gradient

    • Set new point at distance d along path from old point

    • Repeat until goal point is reached

-Prof. John Spletzer

Lehigh University


Potential field extension
Potential Field Extension

M. Khatib and R. Chatila

  • In addition to the goal and obstacle potentials, include rotation and task potentials:

    • Rotation Potential:

      • A potential function for the robot’s orientation relative to the goal is considered, thus forcing the robot along a ‘straighter’ path

    • Task Potential:

      • Filter out obstacles that should not influence the robot’s motion; the obstacles that are not in the robot’s path.

  • These additions should help create a superior path for the robot:

    • The rotation field helps avoid overturning from an obstacle

    • The task field helps avoid unnecessary turning.


Goal and references

Goal

To produce a Matlab implementation of the extended potential fields method for several configurations

References

Prof. John Spletzer:

http://www.cse.lehigh.edu/%7Espletzer/cse397_Fall05/lec014_MotionPlanning.pdf

H. Choset et al (2005). Principles of Robot Motion: Theory, Algorithms and Implementations. Cambridge, MA. MIT Press

M. Khatib and R. Chatila. An extended potential field approach for mobile robot sensor-based motions. In Proc. Int. Conf. on Intelligent Autonomous Systems (IAS'4), 1995.

R. W. Beard and T.W. McLain, Motion Planning Using Potential Fields, January 2003

Goal and References


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