Figure 6. Goal force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Since the motion trajectory of UAV is divided into several median points that the UAV
should reach them one by one in a sequence the output obtained after the execution of AI will be a
set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through
the obstacles to reach the destination. The routine used for this purpose is the potential field method
(also an alternative new method is in progress which models the UAV motion through opponents
same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical
charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field
of this system of charges a path will be suggested for the UAV. At a higher level, predictions can be
used to anticipate the position of the obstacles and make better decisions in order to reach the
desired vector. In our path- planning algorithm, an articial potential field is set up in the space; that
is, each point in the space is assigned a scalar value. The value at the goal point is set to be 0 and the
value of the potential at all other points is positive.