Trajectory planning is critical to autonomous navigation systems, working in conjunction with perception, localization, and obstacle avoidance. Traditional path planning algorithms often struggle in large or complex environments due to extensive memory usage and long computation times. In this paper, we propose a hierarchical planning, a multi-level approach where a high-level planner sets general goals for a low-level planner to execute, framework that combines the reasoning capabilities of a large language model (LLM) with the efficiency of a machine learning-based local planner. The LLM acts as a high-level planner by suggesting intermediate waypoints that guide the robot toward its goal. A machine learning-based trajectory planner then uses these waypoints to compute feasible and efficient paths at the local level. This approach significantly reduces the number of states explored during planning and accelerates decision-making. To validate our method, we tested it in 100 simulated environments of varying difficulty levels (easy and hard). The results show that our approach reduces the explored space by 73.2%, 96.9%, 91.6%, and 77.4%, and the length of trajectory required to reach the goal by 5.9%, 5.7%, 2.69%, and 21.1%, respectively, when compared to methods such as A*, Dijkstra, as well as other advanced methods such as an LLM-assisted A* and an improved A* algorithm.
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