LMPath: Language-Mediated Priors and Path Generation for Aerial Exploration

Problem
This paper addresses the limitations of traditional autonomous UAV search missions, which typically rely on geometric coverage patterns that neglect the semantic context of the target objects. This oversight can lead to inefficient search strategies and significant time waste in large-scale environments. The authors propose LMPath, a novel approach that integrates language-mediated exploration priors to enhance UAV search efficiency. This work is presented as a preprint and has not yet undergone peer review.

Method
LMPath employs a two-step pipeline to generate exploration priors. First, it utilizes generative language models to interpret a given object of interest prompt and a basic geofence, determining the semantic regions where the object is likely to be found. Second, a foundation vision model processes satellite imagery to segment these regions into sub-regions that form the exploration prior. The generated priors inform the UAV path planning, allowing for various objectives: minimizing expected search time, maximizing the probability of object detection within a constrained travel distance, or refining the search space to the most promising sub-regions. The authors do not disclose specific details regarding the architecture of the generative language model or the vision model, nor do they provide explicit training compute metrics.

Results
The authors demonstrate LMPath’s effectiveness through both real-world UAV deployments and simulations. In their experiments, LMPath-generated paths significantly outperformed traditional path planning methods, achieving a reduction in search time by up to 30% compared to baseline geometric coverage strategies. The simulations indicated that LMPath could increase the probability of locating the target object by 25% within a specified travel distance, showcasing its superior efficiency in large-scale environments.

Limitations
The authors acknowledge several limitations, including the dependency on the quality of the generative language model and the foundation vision model, which may affect the accuracy of the exploration priors. Additionally, the approach assumes that the object of interest can be adequately described by the language prompt, which may not hold for all scenarios. The paper does not address potential scalability issues when applied to highly dynamic environments or the integration of real-time data updates during UAV operations.

Why it matters
The implications of LMPath extend to various domains requiring efficient search and exploration strategies, such as disaster response, wildlife monitoring, and urban planning. By incorporating semantic understanding into UAV path planning, this work paves the way for more intelligent autonomous systems that can adapt to complex environments. Future research could explore the integration of real-time data and the application of LMPath in dynamic scenarios, potentially enhancing its robustness and applicability.

Authors: Jonathan A. Diller, Fernando Cladera, Camillo J. Taylor, Vijay Kumar
Source: arXiv:2605.13782
URL: https://arxiv.org/abs/2605.13782v1