3D Coverage Path Planning for Efficient Construction Progress Monitoring

TL;DR

This paper introduces a novel 3D coverage path planning method for autonomous robots to efficiently monitor large, multi-level construction sites, ensuring comprehensive surface coverage for progress assessment.

Contribution

The paper presents a new 3D path planning approach utilizing existing building models to improve coverage efficiency over traditional exploration methods.

Findings

Shorter paths achieved compared to exploration planners.

Better surface coverage demonstrated on real and artificial models.

Effective monitoring of multi-level construction sites.

Abstract

On construction sites, progress must be monitored continuously to ensure that the current state corresponds to the planned state in order to increase efficiency, safety and detect construction defects at an early stage. Autonomous mobile robots can document the state of construction with high data quality and consistency. However, finding a path that fully covers the construction site is a challenging task as it can be large, slowly changing over time, and contain dynamic objects. Existing approaches are either exploration approaches that require a long time to explore the entire building, object scanning approaches that are not suitable for large and complex buildings, or planning approaches that only consider 2D coverage. In this paper, we present a novel approach for planning an efficient 3D path for progress monitoring on large construction sites with multiple levels. By making use of an existing 3D model we ensure that all surfaces of the building are covered by the sensor payload such as a 360-degree camera or a lidar. This enables the consistent and reliable monitoring of construction site progress with an autonomous ground robot. We demonstrate the effectiveness of the proposed planner on an artificial and a real building model, showing that much shorter paths and better coverage are achieved than with a traditional exploration planner.