A Design Framework for Off-road Equipment Automation

Design frameworks can be helpful in the development of complex systems needed to automate machines. Designing autonomous off-road machinery requires having the means for managing the complexity of multiple interacting systems. A design framework, consisting of four technical layers, is presented. These layers are (1) machine architecture, (2) machine awareness, (3) machine control, and (4) machine behavior. Examples of technology advanced in development efforts of autonomous, robotic platforms for agricultural applications are provided. Linkages were made to applications in the construction machinery sector. Similarities between agricultural and construction automation exist in each of the technical layers

Earthmoving construction automation with military applications: Past, present and future

© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Amongst increasing innovations in frontier engineering sciences, the advancements in Robotic and Autonomous Systems (RAS) has brought about a new horizon in construction applications. There is evidence of the increasing interest in RAS technologies in the civil construction sector being reflected in construction efforts of many military forces. In particular, Army or ground-based forces are frequently called upon to conduct construction tasks as part of military operations, tasks which could be partially or fully aided by the employment of RAS technologies. Along with recent advances in the Internet of Things (IoT) and cyber-physical system infrastructure, it is essential to examine the current maturity, technical feasibility, and affordability, as well as the challenges and future directions of the adoption and application of RAS to military construction. This paper presents a comprehensive survey and provides a contemporary and industry-independent overview on the state-of-the-art of earthmoving construction automation used in defence, spanning current world’s best practice through to that which is predicted over the coming years

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

Abstract There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.</jats:p

Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery

This thesis develops a shared control design framework for improving operator efficiency and performance on hydraulic excavation tasks. The framework is based on blended shared control (BSC), a technique whereby the operator’s command input is continually augmented by an assistive controller. Designing a BSC control scheme is subdivided here into four key components. Task learning utilizes nonparametric inverse reinforcement learning to identify the underlying goal structure of a task as a sequence of subgoals directly from the demonstration data of an experienced operator. These subgoals may be distinct points in the actuator space or distributions overthe space, from which the operator draws a subgoal location during the task. The remaining three steps are executed on-line during each update of the BSC controller. In real-time, the subgoal prediction step involves utilizing the subgoal decomposition from the learning process in order to predict the current subgoal of the operator. Novel deterministic and probabilistic prediction methods are developed and evaluated for their ease of implementation and performance against manually labeled trial data. The control generation component involves computing polynomial trajectories to the predicted subgoal location or mean of the subgoal distribution, and computing a control input which tracks those trajectories. Finally, the blending law synthesizes both inputs through a weighted averaging of the human and control input, using a blending parameter which can be static or dynamic. In the latter case, mapping probabilistic quantities such as the maximum a posteriori probability or statistical entropy to the value of the dynamic blending parameter may yield a more intelligent control assistance, scaling the intervention according to the confidence of the prediction. A reduced-scale (1/12) fully hydraulic excavator model was instrumented for BSC experimentation, equipped with absolute position feedback of each hydraulic actuator. Experiments were conducted using a standard operator control interface and a common earthmoving task: loading a truck from a pile. Under BSC, operators experienced an 18% improvement in mean digging efficiency, defined as mass of material moved per cycle time. Effects of BSC vary with regard to pure cycle time, although most operators experienced a reduced mean cycle time

Towards Semi-Autonomous Control of Heavy-Duty Tracked Earth-Moving Mobile Manipulators : Use Case: The Bulldozer

A mobile manipulator (MM) comprises a manipulator attached to a mobile base, making it capable of manipulation tasks in large workspaces. In the field of construction, heavy-duty MMs are extensively used for soil excavation at construction sites. One such machine is the bulldozer, which is widely used because of its robustness and maneuverability. With its onboard blade, the bulldozer shapes terrain and transports soil material by pushing it. However, operating the blade with joysticks to accurately shape the terrain surface and moving material productively are difficult tasks that require extensive training and experience. Automating the motion of the blade, therefore, has the potential to reduce skill requirements, improve productivity, and reduce operators’ workloads. This thesis studies and develops methods for the semi-autonomous control of a bulldozer to increase surface quality and earthmoving productivity. These goals were reflected in the main research problems (RPs). Furthermore, as bulldozers drive over the terrain shape generated by the blade, the RPs are coupled because earthmoving productivity is partially dependent on surface quality. The RPs and their coupling were addressed in four publications by coordinating the mobile base and manipulator control and by using the surrounding terrain shape in automatic blade motion reference computations. Challenges to automatic control emerge from the tracked mobile platform driving on rough terrain while the manipulator tool interacts with the soil. It is shown in the first two publications that coordinating the control of the MM mobile base and blade manipulator subsystems can improve surface quality and productivity by temporarily slowing down the machine when the required manipulator joint rates increase or when the tractive performance reduces. The third publication showed that feedforward–feedback control of the blade manipulator can be used on a real-world bulldozer for accurate terrain shaping. The thesis work culminates in the final publication with an experimental implementation of a semi-autonomous blade control system that continuously maps the worksite terrain and uses it to compute the required blade motion

An in-depth investigation of digital construction technologies from a building economics perspective

While initial costs in building economics cover a small portion of the costs incurred during its life-cycle, most occur in construction, operation, and subsequent processes. Despite its numerous contributions to building economics, the construction industry is slowly adapting to digital technologies.&nbsp;To overcome the barriers and crown the assets with their proper management, dynamic applications of digital tools and techniques of Industry 4.0 need to emerge in the construction industry.&nbsp;Therefore, this study aims to present an integrative approach that combines quantitative and qualitative analysis techniques to critically review the available literature on the potential contributions of digital construction technologies to building economics through the post-design phases of the life cycle. The primary focus of the investigation is how digital technologies can overcome prevalent problems and how they can impact building economics. The study contributes to the field by providing an awareness that will inform researchers and practitioners of the trends, gaps, and more profound exchange of ideas in future research efforts

The effect of overloading on reliability of wheel loader structural components

This research attempts to provide a fundamental understanding into the relationship between the productivity of material handling equipment, specifically wheel loaders, and their ability to operate reliably when subjected to high overload conditions. The overall aim is to determine the effect of overloading the bucket on wheel loader reliability. The specific objectives of the research are to: 1) evaluate the effect of overloading the bucket on wheel loader productivity; 2) examine the effect of overloading the bucket on hydraulic pressures in the hoist cylinders (used as a proxy for forces on a wheel loader); and 3) investigate the effect of overloading the bucket on the reliability of structural components of a wheel loader. To achieve these objectives, the research used data from on-board equipment monitors from the global fleet of ultra-class wheel loaders for a specific original equipment manufacturer to test the various research hypotheses. The data included production data, failure and repair data, and hydraulic cylinder pressures, which were used as a proxy for stresses on structural components. ANOVA and Pearson and Spearman correlations tests were performed on data samples to test the hypotheses. Duty-cycle relationships were established using linear life stress relationships ratios for the wheel loaders structural components. The research showed that, while higher bucket loads increase productivity, there is evidence that they slow down the loading cycle, may be detrimental to productivity. The hoist cylinder pressure increased with increasing payload weight. The reliability of the structural components was similar in both the standard and duty-cycle cases; although, the accuracy of the reliability models increased when the models accounted for duty-cycles --Abstract, page iii