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Welcome to the Hybrid-Artificial-Potential-Field-A-star-Planning wiki!

Here we are trying to mix two known method of motion planning , A-star and Artificial-Potential Field method.

We are implementing these two methods using following two mixtures(Environment is considered to be static):

1)This method is not actually hybrid one rather it uses results of A-star to boost up potential field. Now we know that A-star is offline algorithm, which calculates path to goal directly without any feedback from next frame. So we calculate path from source to goal using this A-star method. Now we can divide our planned path into local goals and use Artificial-potential method to get to those. This way it will be faster and cheaper.

2)This method ,up to some extent, is hybrid. Here we use A-star and Potential field simultaneously depending on some parameters. Currently in this code we are considering that parameter as distance from nearest obstacle. So algorithm goes like this: If distance from nearest obstacle > k(some parameter): next position = A-star planner else: next position = Artificial-Potential-field planner