Implementation of Artificial Potential Field (Reactive Method of Motion Planing)
For basics and working of Potential Field Motion Planning one can refer to http://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf
This is basic implementation of potential field motion planning. Here we condsider our bot as positively charged body and goal as a negatively charged body and all obstacles as positively charge bodies. This way goal will attracts bot but obstacles will repel it from itself. Hence bot will reach to goal avoiding obstacles in these different potential fields.
Attractive force =
- tau(q(current) -q(goal)), if d(q(current), d(q(goal), q(current)) <= d*
- d*(tau*(q(current)-q(goal)))/d(q(current), q(goal)), if d(q(goal), q(current)) > d*
Repulsive Force =
- n(1/Q* - 1/D(q))*(1/D(q))^2* d'(q), if D(q) < Q*
- 0, if D(q) >= Q*
Prerequisites
- Python
- OpenCV
- Numpy
- Matplotlib
Input Images It will take all images in root folder as input images.
Sample Input Images:
Output Images:
