To address the challenge of optimizing paths amidst multiple influencing factors within a constrained timeframe, a path planning algorithm rooted in the minimal processing unit is introduced. This algorithm encompasses various factors affecting path quality by formulating a comprehensive objective function that includes path distance and steering cost. Adjustable parameters are incorporated to cater to the operational requirements of diverse robotic systems. To circumvent redundant computations of global path data and enhance the efficiency of variant search, a framework comprising a minimal solution unit and a distributed storage structure is devised for local path data computation and variant updates. Finally, comparative experiments are conducted across different environmental models. The results show that the average iteration times of the algorithm are reduced by 52.4%, and the solving time is reduced by 89.2%.