An Adaptive Inspection Planning Approach Towards Routine Monitoring in Uncertain Environments

TL;DR

This paper introduces a hierarchical, adaptive inspection planning framework for robots operating in uncertain environments, combining global route planning with local replanning to ensure robust, comprehensive inspections in dynamic, real-world settings.

Contribution

It presents a novel hierarchical approach that integrates global route planning with local surface adaptation to improve inspection robustness under environmental uncertainties.

Findings

Successfully deployed in subterranean mines with quadrupedal robots.

Demonstrated robustness against environment changes and obstructions.

Enhanced coverage and safety in uncertain inspection environments.

Abstract

In this work, we present a hierarchical framework designed to support robotic inspection under environment uncertainty. By leveraging a known environment model, existing methods plan and safely track inspection routes to visit points of interest. However, discrepancies between the model and actual site conditions, caused by either natural or human activities, can alter the surface morphology or introduce path obstructions. To address this challenge, the proposed framework divides the inspection task into: (a) generating the initial global view-plan for region of interests based on a historical map and (b) local view replanning to adapt to the current morphology of the inspection scene. The proposed hierarchy preserves global coverage objectives while enabling reactive adaptation to the local surface morphology. This enables the local autonomy to remain robust against environment uncertainty and complete the inspection tasks. We validate the approach through deployments in real-world subterranean mines using quadrupedal robot. A supplementary media highlighting the proposed method can be found here https://youtu.be/6TxK8S_83Lw.