A Framework for Robotic Path Planning Based on Enhanced Fluid Potential Dynamical Models

Path planning is one of the main sectors of robotics that deals with path calculations from a starting point to a goal in a defined field. Physical algorithms that rely on artificial potential fields and fluid fields have been developed to solve the path planning task. The main problem for the former is the creation of local minima in the field which requires extra algorithms to solve with increased path cost and added inefficiencies, while for the latter, the problem is the computational cost. This paper proposes an enhanced computational-physical path planning algorithm based on fluid stream equations and mechanics – named the Stream Field Navigation (SFN) – with modifications on how the residuals are calculated to improve the computational efficiency by introducing the Directional Residuals. SFN also introduces the Stream Reversal approach to represent a navigation field with no local minima. A comparison between the SFN algorithm and the Artificial Potential Field method is carried out to show the fundamental differences between the two methods and the results include empirical comparisons between the execution times of multiple algorithms which show that the SFN algorithm is at least 75% faster than PRM while A* is the fastest approach.