Semantics-Aware Predictive Inspection Path Planning
Mihir Rahul Dharmadhikari, Kostas Alexis
AI summary
Problem
Current semantics-aware path planning methods treat objects individually or only use seen relations, ignoring the highly structured, repeating spatial patterns common in industrial environments like ship ballast tanks.
Approach
The authors propose Semantics-aware Predictive Planning (SPP), which detects repeating semantic patterns in a Semantic Scene Graph, predicts unseen structures, and uses these predictions to guide two tailored inspection path planning strategies.
Key results
- Algorithm for detecting exact and inexact repeating semantic patterns in Semantic Scene Graphs
- Graph prediction strategy to extend semantic maps into unknown environments
- Two predictive inspection path planning strategies tailored for industrial settings
- 25–60% inspection time reduction in simulation and up to 23% in real-world ship tank deployments
Why it matters
Significantly boosts the efficiency of autonomous robotic inspections in structured industrial environments, addressing critical time constraints for aerial robots operating in confined spaces.
Abstract
This paper presents a novel semantics-aware inspection path planning paradigm called “Semantics-aware Predictive Planning” (SPP). Industrial environments that require the inspection of specific objects or structures (called “semantics”), such as ballast water tanks inside ships, often present structured and repetitive spatial arrangements of the semantics of interest. Motivated by this, we first contribute an algorithm that identifies spatially repeating patterns of semantics - exact or inexact - in a semantic scene graph representation and makes predictions about the evolution of the graph in the unseen parts of the environment using these patterns. Furthermore, two inspection path planning strategies, tailored to ballast water tank inspection, that exploit these predictions are proposed. To assess the performance of the novel predictive planning paradigm, both simulation and experimental evaluations are performed. First, we conduct a simulation study comparing the method against relevant state-of-the-art techniques and further present tests showing its ability to handle imperfect patterns. Second, we deploy our method onboard a collision-tolerant aerial robot operating inside the ballast tanks of two real ships. The results, both in simulation and field experiments, demonstrate significant improvement over the state-of-the-art in terms of inspection time while maintaining equal or better semantic surface coverage. A set of videos describing the different parts of the method and the field deployments are available at https://tinyurl.com/spp-videos. The code for this work is made available at https://github.com/ntnu-arl/predictive planning ros.