MMP-A*: Multimodal Perception Enhanced Incremental Heuristic Search on Path Planning

TL;DR

Experimental results show that MMP-A*, a multimodal framework that integrates the spatial grounding capabilities of vision-language models with a novel adaptive decay mechanism achieves near-optimal trajectories with significantly reduced operational costs, demonstrating its potential as a perception-grounded and computationally efficient paradigm for autonomous navigation.

Abstract

Autonomous path planning requires a synergy between global reasoning and geometric precision, especially in complex or cluttered environments. While classical A* is valued for its optimality, it incurs prohibitive computational and memory costs in large-scale scenarios. Recent attempts to mitigate these limitations by using Large Language Models for waypoint guidance remain insufficient, as they rely only on text-based reasoning without spatial grounding. As a result, such models often produce incorrect waypoints in topologically complex environments with dead ends, and lack the perceptual capacity to interpret ambiguous physical boundaries. These inconsistencies lead to costly corrective expansions and undermine the intended computational efficiency. We introduce MMP-A*, a multimodal framework that integrates the spatial grounding capabilities of vision-language models with a novel adaptive decay mechanism. By anchoring high-level reasoning in physical geometry, the framework produces coherent waypoint guidance that addresses the limitations of text-only planners. The adaptive decay mechanism dynamically regulates the influence of uncertain waypoints within the heuristic, ensuring geometric validity while substantially reducing memory overhead. To evaluate robustness, we test the framework in challenging environments characterized by severe clutter and topological complexity. Experimental results show that MMP-A* achieves near-optimal trajectories with significantly reduced operational costs, demonstrating its potential as a perception-grounded and computationally efficient paradigm for autonomous navigation.

Figures (12)

• Figure 1: Overall framework of MMP-A*: The proposed planner operates in three stages: (1) the LLM analyzes the map and generates coarse waypoint suggestions; (2) the VLM refines these by visually filtering redundant or invalid checkpoints; and (3) the refined waypoints guide the A* search through an adaptive fading-checkpoint heuristic, producing valid and efficient paths in complex environments.
• Figure 2: Alpha-Decay Sensitivity Analysis. Operation ratio (bars) and relative path length (lines) of LLM-A* and MMP-A* under varying decay coefficients $\alpha$. Each subplot corresponds to a different LLM–VLM pairing.
• Figure 3: Visualization of the experimental setup. The bottom row depicts the core dataset featuring dense, maze-like topologies. The top row illustrates the generality assessment subset, where irregular, amorphous barriers (shaded red) are superimposed to rigorously evaluate the framework's visual generalization capabilities against non-geometric obstacles.
• Figure 4: Impact of Adaptive Decay on Efficiency and Path Quality. Comparison between LLM-A* and MMP-A* with and without the adaptive decay mechanism. The plots show changes in operation, storage, and relative path length ratios (lower is better).
• Figure 5: Overall visualization of key experiments.(a) Complex Environment Experiment: Operation and storage ratios across increasing map complexity levels. (b) Scale Robustness Experiment: Growth factors of operations and storages across varying map sizes. (c) Comparison of Prompt Engineering Strategy