Accelerating Classical Path Planning via Learned Search Space Reduction

Efficient path planning is critical for autonomous navigation in complex environments. This paper presents a comparative analysis of two classical heuristic algorithms, A* and Dijkstra, which are widely used to compute optimal paths on grid-based maps. While Dijkstra guarantees path optimality through an exhaustive search, A* improves computational efficiency by leveraging heuristics, sometimes at the expense of suboptimal solutions. Recognizing the complementary strengths and limitations of these methods, we propose a hybrid approach that integrates their outputs using a convolutional autoencoder-based neural network. Trained on path distributions from both algorithms, the model learns to predict high-probability path regions and expected path lengths, effectively reducing the search space for downstream planners. This integration allows the system to balance optimality and speed, providing a data-driven mechanism to guide classical search algorithms more efficiently. Experimental results demonstrate that our hybrid planner significantly improves planning time without sacrificing path quality, illustrating the benefits of combining heuristic search with learned priors for scalable and adaptive robotic navigation.