We propose HRA*, a new randomized path planner for complex 3D environments. The method is a modified A* algorithm that uses a hybrid node expansion technique that combines a random exploration of the action space meeting vehicle kinematic constraints with a cost to goal metric that considers only kinematically feasible paths to the goal. The method includes also a series of heuristics to accelerate the search time. These include a cost penalty near obstacles, and a filter to prevent revisiting configurations. The performance of the method is compared against A*, RRT and RRT* in a series of challenging 3D outdoor datasets. HRA* is shown to outperform all of them in computation time, and delivering shorter paths than A* and RR