UAV-assisted autonomous mobile robot navigation for as-is 3D data collection and registration in cluttered environments

Abstract

Abstract This paper discusses enabling autonomous mobile robots to operate in unstructured terrain and environments and gather data in a time-efficient manner. In rugged environments such as construction sites and disaster sites, spatiotemporal data is difficult to acquire since these environments are potentially hazardous to humans. Autonomous robot systems represent a reasonable solution to provide topographic and surveillance data that can assist human activity in these environments, whether for exploration, mapping, or search and rescue. However, automatically operating the mobile laser scanning robots at cluttered environments is challenging because as-is geometrical conditions of the site are difficult to comprehend from the ground level due to the blocked line-of-site views. To address the issue, this paper introduces a new framework for operating mobile robots equipped with a laser scanning system in cluttered outdoor environments with the aid of an unmanned aerial vehicle (UAV). To obtain an initial map from the current field, this method first deploys UAV to collect photographic images of a site and builds a point cloud of a3D terrain of the site including obstacle information. A voxel grid is then created from the UAV-generated point cloud, and simulation for laser scan planning is conducted to determine the stationary laser scan positions at which a mobile robot can collect data with less occluded views while capturing crucial geometric information as much as it can. Finally, optimal paths for the mobile robot to navigate among the estimated scan positions are generated. Promising test results were obtained from a real-world outdoor structural material test yard as an example of a cluttered environment. It is expected that the proposed UAV-assisted robotic approach can significantly reduce human intervention and time for data collection and processing and provide technologies to enable cluttered environments to be frequently monitored, updated, and analyzed to support timely decision-making.