Aiming at the problems of unsolved mobile robot path planning in multi-obstacle scenarios, large change of steering speed state and too close distance between path and obstacles, a vehicle constrained path planning algorithm based on the improvement of cost function is proposed. The method takes the vehicle constrained path planning algorithm Hybrid A* as the base algorithm and its cost function as the original cost function. A dual start and dual end search process is established to form a two-way search and reduce the path search time. Introduce steering constraints to reduce the steering speed state change of the traditional Hybrid A* algorithm. Introduce distance constraints to reduce the priority of dangerous edge nodes. The experiments demonstrate that compared to the traditional vehicle constraint Hybrid A* algorithm, the improved Hybrid A* algorithm reduces the average path search time by 12.043%, the steering speed state change by 16.623%, and the priority of nodes close to the danger edge by 25%. This series of experimental results successfully improves the traditional Hybrid A* algorithm in planning path algorithm and provides an effective solution for mobile robot path planning in multi-obstacle scenarios..