Python implementation of Artificial Potential Field

Check demo_1.gif to see the output with 4 obstacles b/w robot and goal
Check demo_2.gif to see the output with 6 obstacles b/w robot and goal

Running instruction:

Run the main.py file from directory.
User can defined objects like Robot, Goal, Obstacles in this file.

Requirements

python
numpy

Features

Supports multiple obstacle objects
Single robot and goal object
image saving capability added
positional class encorporated to remove duplicate code in all objects
Public and private functions defined in classes

Fixed issues:

bug fixed in take_next_move function of Robot class
vague name replaced with meaningful names

Example for usage:

You can define objects, robot, goal and obstacle's in main function like:

obstacle1 = Object(position(x=9.0, y=5.0), sigma=1.0)
obstacle2 = Object(position(x=9.0, y=8.0), sigma=1.0)
obstacle3 = Object(position(x=14.0, y=15.0), sigma=1.0)
obstacle4 = Object(position(x=14.0, y=18.0), sigma=1.0)
goal = Object(position(x=18.0, y=12.0), sigma=2.0)
robot = Robot(position(x=5.0, y=5.0), sensor_range=2.0, npts=60)

List of Obstacles:

obstacles = [obstacle1, obstacle2, obstacle3, obstacle4]

Objective:

The aim is to move mobile robot from start point to goal point while avoiding obstacle in path

Flow Chart Artificial Potential Field: