UAV Mission Planning Using Swarm Intelligence and 4D BIMs in Support of Vision-Based Construction Progress Monitoring and As-Built Modeling

Abstract

Inspection planning is a primary element of computer vision-and unoccupied aerial vehicle (UAV)-enabled construction monitoring. Prior to the on-site deployment of camera-mounted UAVs, the inspection objectives need to be identified, and optimal inspection plans must be developed; Such plans should ensure complete data acquisition and minimize the use of UAV's limited flight time. The image capture configuration must be taken into account since it directly affects the downstream applications of the captured data such as progress detection and as-built modeling. This paper proposes a framework and a novel technique which utilizes four-dimensional (4D) building information models (BIM) and swarm intelligence to automatically generate the UAV inspection mission plans. It computationally supports both static and dynamic site layouts. The inspection objectives, their geometry, and their semantics are automatically extracted from BIM, and the corresponding elements are identified. An optimal inspection plan is developed using artificial intelligence, ensuring complete coverage of inspection targets while minimizing flight duration. The method has been tested in UAV-enabled data acquisition scenarios. It is based on the industry foundation classes (IFC), facilitating OpenBIM and reducing the costs associated with the lack of interoperability, a core challenge in information modeling. Due to the target extraction at element and sub-element levels, it supports computer vision-based construction progress monitoring and automated as-built and as-is BIM development.

Lean and BIM based flight planning for automated data acquisition of bridge structures with LiDAR UAV during construction phase

“…In addition, the simulation of the acquired LiDAR-based point clouds and an analytical evaluation and completeness check of the acquired data are performed to fulfill quality criteria and to optimize the flight planning before the actual flight. The approaches of Hamledari et al (Hamledari et al, 2018), (Hamledari et al, 2021) build on the approach of Lin et al (Lin et al, 2013) and use a 4D-BIM in IFC as a basis already. The activities and associated components for which data acquisition is required are manually filtered in the IFC using a simple interface, e.g., in terms of completion date or responsible sub-contractor.…”

Building a Next Generation AI Platform for AEC: A Review and Research Challenges

“…However, it is still a challenge to achieve autonomous path planning and navigation for UAVs (Ham et al 2016). Some studies have explored the potentials of applying AI in autonomous path planning (Aggarwal et al 2019;Hamledari et al 2018). For instance, according to Hamledari et al (2018), swarm intelligence was employed in making the UAVs inspection plan, which reduces the flight duration and thus improves the efficiency of site inspection.…”

“…Some studies have explored the potentials of applying AI in autonomous path planning (Aggarwal et al 2019;Hamledari et al 2018). For instance, according to Hamledari et al (2018), swarm intelligence was employed in making the UAVs inspection plan, which reduces the flight duration and thus improves the efficiency of site inspection. Besides, some studies integrated 4D BIM with UAVsenabled 3D reconstruction to achieve autonomous path planning of UAVs (Shang and Shen 2017).…”