PSO AND ROBOT PATH PLANNING PROBLEM
As a particle move through the search space, gaining one new position for iteration, a
conditional statement checks to see if the position of the particle will fall within the boundaries of
the obstacle. If this condition is true, the obstacle avoidance section of the algorithm is initiated.
Movement of a robot position is realized by the Particle Swarm Optimization algorithm. PSO
convergence to the best solution by adjusting the trail of each individual particle toward its best
location based on the best of itself and global best on the neighbor particles. The modification of
a robot position is realized by position and velocity information. The position of a robot is
represented by Cartesian coordinate, such as x, y axis position and the velocity is modification by
PSO.
The initial position for the robot is initialized with random coordinates. Robot is defined
within the context of topological obstacles comprising itself in the population. Neighbors of robot
can be carried out based on the distance of robot position. In this study, global path is considered
for robot movement path planning. Global path is carried out based on the fitness function. Fitness
value is evaluated for each particle over its obstacles based on the coverage of the target area.
When a particle discovers a pattern that is better than any it has found is stores the coordinate as
new best position. Robot rotates in order to reduce the possible collisions with obstacles.
SOLVING THE CONSTRAINT PATH PLANNING PROBLEM
ROBOT USING PSO
In this section we propose a solution to path planning problem using PSO. The proposed scheme
presumes current position of robot and their speeds, and determines the next position of robot by
optimizing the given constrained objective function. We propse an algorithm for trajectory of robot
using PSO.
1. Add current position of robot to the trajectory.
2. If the distance between the current position of the robot and the goal is less than or equal to a
predefined threshold, go to f.
3. Relocate the obstacles and the goal according to their corresponding relocation probability.
4. Initialize PSO swarm around current position of the robot. Evolve PSO swarm according to
Algorithm 1.
5. Choose global best position the swarm to be the current position of the robot and go back to a.
6. Add the goal position to the trajectory and stop.
