Development of Integrative Methodologies for Effective Excavation Progress Monitoring

Abstract

Excavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity that are the ongoing issues in this area. This paper covers three aspects of effective excavation progress monitoring that include excavation volume estimation, occlusion area detection, and 5D mapping. The excavation volume estimation component enables estimating the bucket volume and ground excavation volume. To achieve mapping of the hidden or occluded ground areas, integration of proprioceptive and exteroceptive sensing data was adopted. Finally, we proposed the idea of 5D mapping that provides the info of the excavated ground in terms of geometric space and material type/properties using a 3D ground map with LiDAR intensity and a ground resistive index. Through experimental validations with a mini excavator, the accuracy of the two different volume estimation methods was compared. Finally, a reconstructed map for occlusion areas and a 5D map were created using the bucket tip’s trajectory and multiple sensory data with convolutional neural network techniques, respectively. The created 5D map would allow for the provision of extended ground information beyond a normal 3D ground map, which is indispensable to progress monitoring and control of autonomous excavation.

Automatic Estimation of Excavator’s Actual Productivity in Trenching and Grading Operations Using Building Information Modeling (BIM)

“…Most construction projects, including the construction of industrial and residential buildings, highways, and airports, require different sorts of excavation operations [8]. An excavator is a multi-purpose machine that can be used in many earth-moving operations since it is capable of performing a variety of tasks, such as digging, loading, trenching, cutting, and grading soil [7]. Human operators exploit their senses and reasoning-based knowledge to control and monitor operations.…”

“…Also, another challenge is that in the grading and trenching tasks, the sheer amount of moved material does not matter, but it is important that the work is carried out accurately within required tolerances [5]. Therefore, an automated procedure is required to estimate the productivity of HDMMs in grading and trenching operations, which are dependent on target models [6,7].…”

Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey

“…Results obtained are useful for validating numerical models of the soil cutting process leading to increase task efficiency. By merging force sensing (1D) with LiDAR intensity information (1D) and other proprioceptive and exteroceptive sensors for 3D ground map, the 5D map generated can be used to estimate the bucket-load volume and effectively monitor the excavation progress [59].…”

A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

“…Bugler et al (2017) used photogrammetry to measure the volume of excavated soil. To control the construction progress, the bucket volume and the excavation volume were estimated (Rasul et al, 2021). Bi et al (2019) estimated the amount of ore loaded by trucks based on deep convolutional neural networks and achieved good accuracy.…”

“…(2017) used photogrammetry to measure the volume of excavated soil. To control the construction progress, the bucket volume and the excavation volume were estimated (Rasul et al., 2021). Bi et al.…”