Autonomous Loading Research Articles

On-Demand Transport Bubbles Adhering to Noncontiguous Patterned Superhydrophobic Surfaces Using a Superhydrophobic Tweezer.

Bubble transportation and related flotation are ubiquitous phenomena in nature and industry. Various surfaces with distinct morphologies and specific wettability properties have been engineered by organisms in nature and by humans to facilitate the targeted movement of bubbles. However, existing methods predominantly rely on continuous surfaces, limiting the ability of bubbles to deviate from their path before reaching their intended destination. Therefore, directional transportation of bubbles using noncontiguous surfaces still remains a significant challenge. Inspired by water spiders' ability to capture bubbles underwater using their hydrophobic surface for survival, we propose a novel transport strategy that utilizes patterned superhydrophobic surfaces (PSHSs) and a superhydrophobic tweezer. This strategy is implemented by switching between the hood mode and puncture mode of the moving three-phase contact lines to load and unload the bubble. To quantitatively evaluate the loss ratio of the bubble during transportation, a simple and exquisite bubble-weighing apparatus is devised. Our findings indicate that circular PSHSs demonstrate superior bubble adhesion and achieve the highest bubble transport ratio of 95.1%. In order to validate the promising application of this novel method, we employ the computer numerical control (CNC) technology to facilitate the autonomous loading and precise transportation of underwater bubbles, as well as the blending and ionization of combustible gas bubbles with air bubbles at different volume ratios.

Robust Localization for Underground Mining Vehicles: An Application in a Room and Pillar Mine.

Most autonomous navigation systems used in underground mining vehicles such as load-haul-dump (LHD) vehicles and trucks use 2D light detection and ranging (LIDAR) sensors and 2D representations/maps of the environment. In this article, we propose the use of 3D LIDARs and existing 3D simultaneous localization and mapping (SLAM) jointly with 2D mapping methods to produce or update 2D grid maps of underground tunnels that may have significant elevation changes. Existing mapping methods that only use 2D LIDARs are shown to fail to produce accurate 2D grid maps of the environment. These maps can be used for robust localization and navigation in different mine types (e.g., sublevel stoping, block/panel caving, room and pillar), using only 2D LIDAR sensors. The proposed methodology was tested in the Werra Potash Mine located at Philippsthal, Germany, under real operational conditions. The obtained results show that the enhanced 2D map-building method produces a superior mapping performance compared with a 2D map generated without the use of the 3D LIDAR-based mapping solution. The 2D map generated enables robust 2D localization, which was tested during the operation of an autonomous LHD, performing autonomous navigation and autonomous loading over extended periods of time.

Autonomous detection and loading of ore piles with load–haul–dump machines in Room & Pillar mines

AbstractAutomation of machines in underground mines is a topic with increasing interest, both for research and industrial applications. Autonomous load–haul–dump (LHD) machines need to load material successfully before dumping it into a crusher or an ore pass. The autonomous loading method must be robust to enable reliable operation of the LHD during long periods of time. In this work, a method to perform autonomous loading in Room & Pillar mines is presented. It is based on detecting all ore piles in real‐time, and then computing attack poses in each pile. Then, a positioning process is performed to get the machine in front of the selected ore pile, and an excavation algorithm is executed for loading until the bucket is filled. The proposed method is able to detect multiple ore piles, with different slopes and sizes, and to consider different possible trajectories for attacking and loading the most feasible pile. The method was tested in the Werra Potash Mine, under real operational conditions. The results show that the method can load about 80% of the material that an experienced operator can load. Also, the success rate of the autonomous loading process is very high, being able to load enough material in all trials, and performing successfully the full procedure in 88% of the cases. Thus, the proposed autonomous loading method is a suitable alternative to be used in Room & Pillar mines.

An autonomous crawler excavator for hazardous environments

Abstract As part of ROBDEKON, a 24-ton crawler excavator was equipped with sensors and a digital actuation interface as a technology demonstrator which features autonomy capabilities. The system architecture includes algorithms for localization, perception, mapping, planning, and control. The system is capable of tasks like autonomous driving to a target location, excavation of a predefined area to a given depth, and autonomous loading of an autonomously approaching transport vehicle. To ensure safety, collision avoidance based on 360° perception is always active during autonomous operation. This article presents the concept and implementation of the excavator’s autonomy functionality.

A Parametric Study on a Paper-Based Bi-Material Cantilever Valve.

The novel paper-based Bi-Material Cantilever (B-MaC) valve allows the autonomous loading and control of multiple fluid reagents which contributes to the accurate operation of paper-based microfluidic devices utilized for biological and chemical sensing applications. In this paper, an extensive parametric study is presented to evaluate the effects of key geometric parameters of the valve, such as paper direction, cantilever width, paper type, tape type, and sample volume, in addition to the effects of relative humidity and temperature on the functionality of the B-MaC and to provide a better understanding of the rate of fluid flow and resulting deflection of the cantilever. Machine direction, cantilever width, paper type, and tape type were found to be important parameters that affect the B-MAC’s activation time. It was also observed that the rate of fluid imbibition in the B-MaC is considerably affected by change in humidity for high (55 °C) and low (25 °C) temperatures, while humidity levels have no significant effect during imbibition in the B-MaC at an ambient temperature of 45 °C. It was also found that a minimum distance of 4 mm is required between the B-MaC and the stationary component to prevent accidental activation of the B-MaC prior to sample insertion when relative humidity is higher than 90% and temperature is lower than 35 °C. The rate of fluid imbibition that determines the wetted length of the B-MaC and the final deflection of the cantilever are critical in designing and fabricating point-of-care microfluidic paper-based devices. The B-MaC valve can be utilized in a fluidic circuit to sequentially load several reagents, in addition to the sample to the detection area.

Visual Rewards From Observation for Sequential Tasks: Autonomous Pile Loading.

One of the key challenges in implementing reinforcement learning methods for real-world robotic applications is the design of a suitable reward function. In field robotics, the absence of abundant datasets, limited training time, and high variation of environmental conditions complicate the task further. In this paper, we review reward learning techniques together with visual representations commonly used in current state-of-the-art works in robotics. We investigate a practical approach proposed in prior work to associate the reward with the stage of the progress in task completion based on visual observation. This approach was demonstrated in controlled laboratory conditions. We study its potential for a real-scale field application, autonomous pile loading, tested outdoors in three seasons: summer, autumn, and winter. In our framework, the cumulative reward combines the predictions about the process stage and the task completion (terminal stage). We use supervised classification methods to train prediction models and investigate the most common state-of-the-art visual representations. We use task-specific contrastive features for terminal stage prediction.

Особенности идентификации оборудования релоадинга по следам на переснаряженных гильзах

Introduction. In connection with legalization of autonomous cartridges loading to the rifled firearm, the matter of forensic examination of reloaded cases is growing more urgent. Aims and tasks. To examine the regularities of trace formation made by the reloading equipment on reloaded cases in order to identify the method of making cartridges, whose parts they have originally been, and to establish the fact of reloading by means of the same production mechanisms. Experimental part. Examination of the traces formed in the course of a shot and reloading was carried out with hunter’s .300 win mag caliber cartridge cases ejected from .300 win mag caliber Sauer 202 carbine. The following reloading equipment was used: manual threaded (single-stage and turret) presses produced by Neck, threaded molding matrices produced by Redding. Discussion of results. Reloading equipment is considered as a production mechanism depositing traces on the surface of the reloaded cases. In the case of identification (selecting a group of objects from a multitude, determining their group belonging) the structural features of their cutting surfaces, their traces being imprinted on the processed parts, are studied. As a whole, they are suitable for identifying the instrument that formed them. Defects in trace-forming objects (matrices, shellhorder, etc.) can also be seen in the traces on the cases as subclass features. These features can be taken into account in the comparative study of cases to establish the fact of reloading using the same production mechanisms.

Rocky-CenterNet: Detecting rocks using convolutional neural networks and ellipses

<h2>Abstract</h2> Rocky-CenterNet is an algorithm based on convolutional networks, aimed at detecting rocks and other ellipse-shaped objects. Instead of representing rocks as bounding boxes or segmentation masks, Rocky-CenterNet models rocks as ellipses. Rocky-CenterNet provides a rich description of the shape of the rocks, while running as fast as object detectors based on bounding boxes. The original implementation of Rocky-CenterNet is made available online. It is built on the original implementation of CenterNet, which is itself implemented in Pytorch. Rocky-CenterNet is useful for automation of heavy-duty machinery in mining related tasks, like autonomous loading of material with LHDs and autonomous rock-breaking hammers.

Autonomous Loading System for Load-Haul-Dump (LHD) Machines Used in Underground Mining

This paper describes an autonomous loading system for load-haul-dump (LHD) machines used in underground mining. The loading of fragmented rocks from draw points is a complex task due to many factors including: bucket-rock interaction forces that are difficult to model, humidity that increases cohesion forces, and the possible presence of boulders. The proposed system is designed to integrate all the relevant tasks required for ore loading: rock pile identification, LHD positioning in front of the ore pile, charging and excavating into the ore pile, pull back and payload weighing. The system follows the shared autonomy paradigm: given that the loading process may not be completed autonomously in some cases, it takes into account that the machine/agent can detect this situation and ask a human operator for assistance. The most novel component of the proposed autonomous loading system is the excavation algorithm, and the disclosure of the results obtained from its application in a real underground production environment. The excavation method is based on the way that human operators excavate: while excavating, the bucket is tilted intermittently in order to penetrate the material, and the boom of the LHD is lifted on demand to prevent or correct wheel skidding. Wheel skidding is detected with a patented method that uses LIDAR-based odometry and internal measurements of the LHD. While a complete loading system was designed, the validation had to be divided in two stages. One stage included the rock pile identification and positioning, and the other included the charging, excavation, pull back, and weighting processes. The stage concerning the excavation algorithm was validated using full-scale experiments with a real-size LHD in an underground copper mine in the north of Chile, while the stage concerning the rock pile identification was later validated using real data. The tests showed that the excavation algorithm is able to load the material with an average of 90% bucket fill factor using between three and four attempts (professional human operators required between two and three loading attempts in this mine).

ПІДХІД ДО УНІФІКАЦІЇ РУХОМИХ ЗАСОБІВ ТЕХНІЧНОГО ОБСЛУГОВУВАННЯ ТА РЕМОНТУ РАКЕТНО-АРТИЛЕРІЙСЬКОГО ОЗБРОЄННЯ

The article analyzes approaches to the unification of mobile means of maintenance and repair, and in particular, on the nomenclature of missile and artillery weapons. The classification of mobile means of maintenance and repair has been carried out. A list of the main malfunctions of missile and artillery weapons, which are eliminated through military repair, is given. The analysis of the main problems of maintenance and repair at the initial stage of the Anti-terrorist operation is carried out, and on the basis of trends in the development of mobile maintenance and repair facilities in the leading countries of the world, recommendations are formulated for the modernization of existing and design of mobile maintenance and repair facilities of a new type. The main direction is: 1) use for the base chassis of a family of cars of a domestic manufacturer. 2) modernization of equipment and special equipment of mobile means of maintenance and repair. It is necessary to focus on modular equipment, which can be implemented from the ABS to the troops to order for specific states. 3) reduction of the number of types of mobile means of maintenance and repair rocket and artillery weapon due to unification of equipment, expansion of production capabilities due to repair, maintenance and diagnostics of several groups of weapons and ammunition. 4) consideration of the possibility of application of existing and tested world standards of logistics for the transportation of goods and equipment in standardized packaging. 5) introduction of a means of repair and evacuation "two in one" through the use of platforms with a system of autonomous loading and unloading of the body-container. Keywords: bile vehicles, repairs in the troops, maintenance, unification of mobile repair and maintenance vehicles, missile and artillery weapons.

Handling of large and heavy objects using a single mobile manipulator in combination with a roller board

This paper presents a method for autonomous loading, transportation, and unloading of large objects using a nonholonomic mobile manipulator. Here, the size of the transported object is considerably larger than the size of the mobile platform, which is made possible through the use of a roller board. In this way, the mobile manipulator can handle objects that exceed the manipulator’s payload. The robot can load and unload the object onto its platform using the differential kinematics of the system for a null space motion to maintain the object’s position in space. In order to localise the object, we apply 3D-perception using a depth-camera. While transporting the object to its destination, the robot is considered a tractor-trailer-wheeled system and can navigate using SLAM. Kinematic modelling and practical evaluation prove that the system can potentially take over arduous transportation tasks.

What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning

The ability of robotic excavators to acquire meaningful knowledge about materials during digging can augment their autonomous functionality, as well as optimize downstream operations in construction and mining. Some material properties, such as rock sizes, can be determined visually, but these methods cannot see what lies beneath. In this work, a classification methodology that utilizes only proprioceptive force data acquired from an autonomous digging system and machine learning algorithms is proposed for excavation material identification. The consistent performance synonymous with autonomous digging systems allows for the use of basic features extracted from the force data for classification. A proof of concept of this novel approach to excavation material classification is demonstrated through a binary classification of rock and gravel materials. Force data were obtained from full-scale autonomous loading trials with a 14-tonne capacity load-haul-dump machine at a mining and construction test facility. Preliminary results achieved a classification accuracy of 90%.

A Visual Detection and Location Method for Soft Fabrics

The soft fabric is prone to burr, fold, enfold and blur, which poses a great challenge to the accuracy of visual edge detection and localization of the autonomous loading and unloading material system of garment robot. In order to overcome the above problems, it proposes an adaptive edge localization method based on double filtering in this paper, which can be used to guide the garment robot to achieve grasping of soft fabric automatically. Firstly, the Gauss filtering and morphological filtering are used to smooth the image, and then it calculates the canny adaptive threshold based on the edge gradient information and detect the edge, finally it locates the coordinates of edge feature extracted. The experiment results show that the method is effective accuracy greater than 96%and robust tested on the real data sets composed of soft fabric. The method has good application value for the autonomous loading and unloading operation of the garment robot and has positive significance for the construction of the intelligent garment factory.

Safe and efficient delivery of logistics solutions to coal seam gas well sites, utilising our 'Well in a Box' methodology

Queensland coal seam gas (CSG) company Arrow Energy (Arrow) has implemented a safer, more efficient and cost effective approach to the transportation and supply of well materials to site. This paper examines Arrow’s approach to mitigating the transportation risks. It looks at current transport health, safety and environment (HSE) challenges, approach to address these risks, outcomes and future opportunities. The movement of well materials has been a concern for Arrow; it is a repetitive activity because wells are drilled, completed and worked over on a regular basis. Arrow’s challenge was to determine a better and safer way to load, unload and transport these materials. To address this material handling risk, an Arrow team developed a specially modified 40-foot flat rack (FR) capable of safely and securely holding a variety of materials. The FR can be picked up by a side-loader truck (swing-lifter), thereby enabling autonomous and safe loading and unloading. Warehouse staff can pre-pack all materials, thereby saving significant time for loading and unloading. Time and resources are also saved at the rig site where the FR can be unloaded autonomously. This unique design reduces human interaction and improves HSE personal safety risk significantly. A continuous improvement approach to further enhance the FR design to achieve better efficiency and safety for staff, as well as expanding its use to other business areas, is a key priority to Arrow.

Admittance Control for Robotic Loading: Design and Experiments with a 1‐Tonne Loader and a 14‐Tonne Load‐Haul‐Dump Machine

This paper describes the design, tuning, and extensive field testing of an admittance‐based autonomous loading controller (ALC) for robotic excavation. Several iterations of the ALC were tuned and tested in fragmented rock piles—similar to those found in operating mines—by using both a robotic 1‐tonne capacity Kubota R520S diesel‐hydraulic surface loader and a 14‐tonne capacity Atlas Copco ST14 underground load‐haul‐dump (LHD) machine. On the R520S loader, the ALC increased payload by 18% with greater consistency, although with more energy expended and longer dig times when compared with digging at maximum actuator velocity. On the ST14 LHD, the ALC took 61% less time to load 39% more payload when compared to a single manual operator. The manual operator made 28 dig attempts by using three different digging strategies, and had one failed dig. The tuned ALC made 26 dig attempts at 10 and 11 MN target force levels. All 10 11 MN digs succeeded while 6 of the 16 10 MN digs failed. The results presented in this paper suggest that the admittance‐based ALC is more productive and consistent than manual operators, but that care should be taken when detecting entry into the rock pile.

Technical and operational aspects of autonomous LHD application in metal mines

Due to safety concerns, automation of load-haul-dump (LHD) machines receives considerable attention from the mining industry. This paper analyses and discusses the issues and problems related to implementation and use of autonomous LHDs in underground metal mines. It presents the need for safety measures, infrastructure and discusses technical problems encountered. The paper looks also into technical and operational issues (reliability, maintainability, utilisation, production rate, etc.) as compared to conventional manually operated machines. Conclusions focus mostly on the aspects requiring attention before and after the implementation of autonomous loading systems in order to maximise the chances that they deliver expected benefits.

Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation

We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading.

네트워크 기반 무인지게차를 위한 팔레트 자율적재기술의 개발

Unmanned autonomous forklifts have a great potential to enhance the productivity of material handling in various applications because these forklifts can pick up and deliver loads without an operator and any fixed guide. Especially, automation of pallet loading and unloading technique is useful for enhancing performance of logistics and reducing cost for automation system. There are, however, many technical difficulties in developing such forklifts including localization, map building, sensor fusion, control, and so on. This is because the system requires numerous sensors, actuators, and controllers that need to be connected with each other, and the number of connections grows very rapidly as the number of devices grows. This paper presents a vision sensorbased autonomous loading and unloading for network-based unmanned forklift where system components are connected to a shared CAN network. Functions such as image processing and control algorithm are divided into small tasks that are distributed over a number of microcontrollers with a limited computing capacity. And the experimental results show that proposed architecture can be an appropriate choice for autonomous loading in the unmanned forklift.

Development and design of a novel loading device for the investigation of bone adaptation around immediately loaded dental implants using the reindeer antler as implant bed

The assessment of the behavior of immediately loaded dental implants using biomechanical methods is of particular importance. The primary goal of this investigation is to optimize the function of the implants to serve for immediate loading. Animal experiments on reindeer antlers as a novel animal model will serve for investigation of the bone remodeling processes in the implant bed. The main interest is directed towards the time and loading-dependant behavior of the antler tissue around the implants. The aim and scope of this work was to design an autonomous loading device that has the ability to load an inserted implant in the antler with predefined occlusal forces for predetermined time protocols. The mechanical part of the device can be attached to the antler and is capable of cyclically loading the implant with forces of up to 100N. For the calibration and testing of the loading device a biomechanical measuring system has been used. The calibration curve shows a logarithmic relationship between force and motor current and is used to control the force on the implant. A first test on a cast reindeer antler was performed successfully.

Autonomous Loading, Transport, and Unloading of Specified Cargoes by Using DNA Hybridization and Biological Motor‐Based Motility

Autonomous Loading, Transport, and Unloading of Specified Cargoes by Using DNA Hybridization and Biological Motor‐Based Motility