Zhejiang University Introduces iLLM-A* for Faster Path Planning in Large Grid Maps
Researchers at Zhejiang University have introduced iLLM-A, a novel algorithm for efficient path planning in large-scale grid maps. Published on arXiv, this method combines a language model with an optimized A algorithm, reducing search time significantly.
The algorithm, iLLM-A, tackles the computational challenges posed by traditional pathfinding methods like A and Dijkstra in large, grid-based environments. It employs a delayed update strategy for heuristic values in the open list, avoiding costly re-computations.
Key to iLLM-A*'s efficiency is its use of a hash-based data structure for the open list, reducing search complexity from O(N) to average O(1). This, coupled with a dynamic learning process using an expandable few-shot example database, improves the quality of waypoints generated by the language model (LLM).
The algorithm addresses three major bottlenecks of the current state-of-the-art approach, LLM-A: high time complexity for search and insertion operations, high memory usage, and inefficient waypoints generated by LLMs. In tests, iLLM-A significantly outperformed comparison methods (A, Opt-A, LLM-A*) in search time, memory usage, and path quality.
iLLM-A is poised to benefit applications such as autonomous robotics, logistics planning, and AI control in complex simulations or video games. By combining a language model with an optimized A algorithm, iLLM-A* reduces search time by over a thousand times compared to existing methods. The algorithm, published by researchers at Zhejiang University, offers a promising solution for fast pathfinding in large grid maps.
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