In the real environment, most intelligent wheelchairs work in complex scenarios, and their autonomous navigation requires high requirements for path safety. The asymptotic optimal random search tree RRT star algorithm basically meets the optimal path planning of mobile robots. However, due to the large size of the intelligent wheelchair itself, it is easy to come into close contact with the environment. Therefore, the environmental model can be expanded and different search steps can be defined to keep the planned path away from obstacles. Secondly, to ensure that users can achieve higher comfort and more efficient destination when using intelligent wheelchair navigation, the heuristic constraint sampling idea and the gravitational field idea in the artificial potential field are used to prune the redundant nodes in the planning process of this algorithm. Therefore, the operating memory of the system is reduced. Subsequently, combined with the minimum turning radius of the wheelchair, a minimum path curvature constraint strategy and a cubic Bspline curve algorithm are proposed to smooth the path, which make it more suitable for wheelchair driving. Finally, a comparative experiment is conducted on the improved algorithms on MATLAB and Gazebo simulation platforms, and the proposed algorithm is applied to intelligent wheelchair entities. The experimental results show that the algorithm can effectively solve the global path planning problem of intelligent wheelchairs, significantly improve the efficiency of global path planning, and have a certain degree of security. It provides an effective reference for the mobile robot field.