Real-time volume estimation of a dragline payload

Remote Sensing, 2013

Roads play an indispensable role as part of the infrastructure of society. In recent years, society has witnessed the rapid development of laser mobile mapping systems (LMMS) which, at high measurement rates, acquire dense and accurate point cloud data. This paper presents a way to automatically estimate the required excavation volume when widening a road from point cloud data acquired by an LMMS. Firstly, the input point cloud is down-sampled to a uniform grid and outliers are removed. For each of the resulting grid points, both on and off the road, the local surface normal and 2D slope are estimated. Normals and slopes are consecutively used to separate road from off-road points which enables the estimation of the road centerline and road boundaries. In the final step, the left and right side of the road points are sliced in 1-m slices up to a distance of 4 m, perpendicular to the roadside. Determining and summing each sliced volume enables the estimation of the required excavation for a widening of the road on the left or on the right side. The procedure, including a quality analysis, is demonstrated on a stretch of a mountain road that is approximately 132 m long as sampled by a Lynx LMMS. The results in this particular case show that the required excavation volume on the left side is 8% more than that on the right side. In addition, the error in the results is assessed in two ways. First, by adding up estimated local errors, and second, by comparing results from two different OPEN ACCESS Remote Sens. 2013, 5 4630 datasets sampling the same piece of road both acquired by the Lynx LMMS. Results of both approaches indicate that the error in the estimated volume is below 4%. The proposed method is relatively easy to implement and runs smoothly on a desktop PC. The whole workflow of the LMMS data acquisition and subsequent volume computation can be completed in one or two days and provides road engineers with much more detail than traditional single-point surveying methods such as Total Station or GPS profiling. A drawback is that an LMMS system can only sample what is within the view of the system from the road.

E3S Web of Conferences

The bucket positioning of the excavator in three-dimensions (3-D) is the precondition of the robotic excavator starting automatized works. The electric excavator is one of the most widely used machinery in the mining industry, mainly due to its versatility and portability. Among the tasks performed by the excavator, there is a significant number of repetitive movements associated with moving the bucket to the unloading point and back to the face. Using automated functions to perform such repetitive tasks will not only significantly increase overall productivity, but also reduce energy consumption. This research is carried out to create a method of coordinate control of electric drives of the boom, dipper stick, and bucket of an electric excavator to perform accurate and efficient work. On the basis of the kinematic analysis of the excavator’s attachment system, the trajectory of the end of the working body can be determined from the point of view of the coordinated movement of the e...

Hauling trucks are important part of equipment fleets for large earthmoving operations such as those encountered in dams and highway construction projects. This paper presents an automated methodology for tracking and estimating productivity of hauling trucks fleet operations in near-real-time. Recent advancement in automated site data acquisition technologies made their use in tracking and monitoring of construction operations feasible. However, these technologies fail to track hauling truck fleets due to the change in the cut and fill locations from one cycle to another; making tracking and progress reporting difficult and inaccurate. In addition, there is very little work done utilizing data sensed directly from equipment, for example sensing when truck dumping bed is raised during the dumping process. The technologies deployed in the developed method are Radio Frequency Identification (RFID) and equipment control sensors. Low cost passive RFID tags are attached to hauling trucks and fixed RFID readers are attached to loaders or excavators. The read range of the used RFID tag is centimeters, to be activated only when a loader with an attached RFID reader is loading a truck. On the other hand, control sensor is connected to the truck control system and operated by the motion of its movable bed. The function of control sensors is to record the signal time when the truck operator gives order to the truck control system to raise or lower truck bed. The captured data is then transferred wirelessly from the RFID reader and control sensor to a computer housed in one of the temporary offices onsite and subsequently to the main server in the contractor's head office. Fusing the data captured from RFID reader and control sensor is used to identify loading, travel, dumping and return time that constitute the hauling truck cycle time. The collected data is analyzed and processed automatically, without human intervention, to calculate the productivity of the hauling truck and to report it directly to onsite personnel. Relational database is developed to support the implementation of the proposed method. The developed database is used to process the data captured by the RFID and the control sensor to calculate the productivity achieved in cutfill operations in near-real-time. The developed methodology is expected to facilitate early detection of discrepancies between actual and planned performances.

2019

Since wood represents an important proportion of the delivered cost, it is important to embrace and implement correct measurement procedures and technologies that provide better wood volume estimates of logs on trucks. Poor measurements not only impact the revenue obtained by haulage contractors and forest companies but also might affect their contractual business relationship. Although laser scanning has become a mature and more affordable technology in the forestry domain, it remains expensive to adopt and implement in real-life operating conditions. In this study, multi-view Structure from Motion (SfM) photogrammetry and commercial 3D image processing software were tested as an innovative and alternative method for automated volumetric measurement of truckloads. The images were collected with a small UAV, which was flown around logging trucks transporting Eucalyptus nitens pulplogs. Photogrammetric commercial software was used to process the images and generate 3D models of each ...

SME Annual Conference, 2019

Recent studies towards dragline excavation efficiency have focused on incrementally achieving automation of the entire excavation cycle. Initial efforts resulted in the development of an automated dragline swing system, which optimizes the swing phase time. However, the system still requires human operation for collision avoidance. For full dragline autonomy, a machine vision system is needed for collision prevention and big rock handling during the \u27swinging\u27 and \u27digging\u27 phases of the excavation operation. Previous attempts in this area focused on collision avoidance vision models which estimated the location of the bucket in space in real-time. However, these previous models use image segmentation methods that are neither scalable nor multi-purpose. In this study, a scalable and multi-purpose vision model has been developed for draglines using Convolutional Neural Networks. This vision system averages 82.6% classification accuracy and 91% detection in collision avoidance. It also achieves an 87.32% detection rate in bucket pose estimation tasks. In addition, it averages 80.9% precision and 91.3% recall performance across terrain recognition and oversized rock detection tasks. With minimal modification, the proposed vision system can be adjusted for other automated excavators

Proceedings of the Australian Conference on Robotics …, 2000

This paper describes technology (hardware and software) that has been developed to estimate the volume of material contained within the tray of various haul trucks. Such information is critical to truck utilization and thus can provide information that could lead to significant gains in productivity.

Construction Robotics

This work presents a method for automated excavation speed and progress estimation. First, a measure for the progress speed of an excavation pit is taken from the literature and evaluated regarding the possibility of automation. For each possible parameter, an automated extraction algorithm is presented. The used system is an autonomous excavator arm of a backhoe loader where the used hardware and software system is described. An experimental evaluation of the presented approach has been done with the autonomous system for a small trench, including multiple digging cycles. The resulting measurements seem to include some systematic errors which could be identified and suitable sanity checks could be implemented, removing the erroneous measurements. The remaining measurements were used to determine the excavation speed of the autonomous excavator arm and compared to the values of experienced and amateur operators. The resulting speed and the relevant parameters are then sent to a web ...

Archaeological Prospection, 2012

This paper describes a new method for the automatic detection of pit structures in airborne laser scanning data collected with at least five emitted pulses per square metre. Oppland County, Norway, has a large number of ancient iron production sites and hunting systems. Today, these are manifested as pits in the ground. The iron production sites were used 1400-700 yr ago, and consist of charcoal burning pits, often located in groups of four around a central oven, which also occasionally can be located as a small mound. The hunting systems were used 2000-500 yr ago, and consisted of concealed pitfall traps and wooden fences, located on deer trekking routes. The fences are gone, but the pits remain. Many of the iron production sites and hunting systems are located in forested areas. The current, traditional mapping of these is relatively poor, with many individual pits missing, and poor positional accuracy of the ones that are mapped. This means that overlays of cultural heritage sites cannot be trusted when planning new highways, residential areas, cabins, etc. Experience suggests that automatic detection is clearly superior to visual inspection of the lidar data in finding small pits or pits that are not easily visible. However, visual inspection is better at finding additional information as human interpretation based on archaeological knowledge can be applied. Combining these two methods secures a very good basis for subsequent field survey. In conclusion, we demonstrate that automatic detection of hunting systems and iron extraction sites in lidar data is a valuable tool in combination with visual inspection of the lidar data, prior to field survey. Provided that the point density of the lidar data is high enough, the field survey can be accomplished ten times faster compared with the traditional approach without lidar data.

2015

This report presents the activities and results associated with the development of a laser scanning prototype device, referred to as the Sampling and Laser Targeting System (SLT). The SLT provides the potential means to rapidly classify aggregates used in highway construction. This classification process can be employed to: quantify specific engineering properties; identify and/or quantify the presence and amounts of deleterious materials present; determine whether aggregate types or quality are changing during production; and determine the source material or sources of blended production materials. This project involved the design and fabrication of a prototype SLT, the development of software capable of operating the system and processing, through chemometric modeling, the large quantity of data generated during the scanning process to provide near real-time data turnaround. It was concluded from these findings that laser scanning in near real-time of aggregate materials is a tech...