Text-guided Generation of Efficient Personalized Inspection Plans
Journal:
arXiv
Published Date:
Jun 3, 2025
Abstract
We propose a training-free, Vision-Language Model (VLM)-guided approach for
efficiently generating trajectories to facilitate target inspection planning
based on text descriptions. Unlike existing Vision-and-Language Navigation
(VLN) methods designed for general agents in unknown environments, our approach
specifically targets the efficient inspection of known scenes, with widespread
applications in fields such as medical, marine, and civil engineering.
Leveraging VLMs, our method first extracts points of interest (POIs) from the
text description, then identifies a set of waypoints from which POIs are both
salient and align with the spatial constraints defined in the prompt. Next, we
interact with the VLM to iteratively refine the trajectory, preserving the
visibility and prominence of the POIs. Further, we solve a Traveling Salesman
Problem (TSP) to find the most efficient visitation order that satisfies the
order constraint implied in the text description. Finally, we apply trajectory
optimization to generate smooth, executable inspection paths for aerial and
underwater vehicles. We have evaluated our method across a series of both
handcrafted and real-world scanned environments. The results demonstrate that
our approach effectively generates inspection planning trajectories that adhere
to user instructions.
