Robotic path planning is a critical problem in autonomous robotics. Two com- mon approaches to robotic path planning are sampling-based motion planners and continuous optimization methods. Sampling-based motion planners explore the search space effectively, but either return low quality paths or take a long time to ini- tially find a path. Continuous optimization methods quickly find high-quality paths, but often return paths in collision with obstacles. This thesis combines sampling- based and continuous optimization techniques in order to improve the performance of these planning approaches. This thesis shows that the advantages and disad- vantages of these approaches are complementary and proposes combining them into a pipeline. The proposed pipeline results in better path quality than either ap- proach alone, providing a robust, efficient, and high-quality general path planning solution. The use of collision checking techniques introduced by continuous opti- mization methods in sampling-based planners is also analyzed and approximation error rates and timing results are provided.