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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