  Motion planning in an autonomous agent is responsible for providing smooth,
safe and efficient navigation. Many solutions for dealing this problem have
been offered, one of which is, Artificial Potential Fields (APF). APF is a
simple and computationally low cost method which keeps the robot away from the
obstacles in environment. However, this approach suffers from trapping in local
minima of potential function and then fails to produce motion plans.
Furthermore, Oscillation in presence of obstacles or in narrow passages is
another disadvantage of the method which makes it unqualified for many planning
problems. In this paper we aim to resolve these deficiencies by a novel
approach which employs a prior path between origin and goal configuration of
the robot. Therefore, the planner guarantees to lead the robot to goal area
while the inherent advantages of potential fields remain. For path planning
stage, we intend to use randomized sampling methods such as Rapidly-exploring
Random Trees (RRT) or its derivatives, however, any path planning approach can
be utilized. We have also designed an optimization procedure for evolving the
motion plans towards optimal solution. Then genetic algorithm is applied to
find smoother, safer and shorter plans. In our experiments, we apply a
simulated vehicle in Webots simulator to test and evaluate the motion planner.
Our experiments showed our method to enjoy improving the performance and speed
in comparison to basic approaches.
