Near-Optimal Path Planning for Complex Robotic Inspection Tasks
This addresses the challenge of scalable and robust path planning for robotic inspection tasks, offering a more general solution compared to existing methods.
The paper tackles the problem of generating robot inspection paths that maximize measurement quality, showing it corresponds to the submodular orienteering problem, and proposes a method that achieves near-optimal solutions, outperforming traditional methods in various real-world tests.
In this paper, we consider the problem of generating inspection paths for robots. These paths should allow an attached measurement device to perform high-quality measurements. We formally show that generating robot paths, while maximizing the inspection quality, naturally corresponds to the submodular orienteering problem. Traditional methods that are able to generate solutions with mathematical guarantees do not scale to real-world problems. In this work, we propose a method that is able to generate near-optimal solutions for real-world complex problems. We experimentally test this method in a wide variety of inspection problems and show that it nearly always outperforms traditional methods. We furthermore show that the near-optimality of our approach makes it more robust to changing the inspection problem, and is thus more general.