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

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

Novel Methods For Human-robot Shared Control In Collaborative Robotics

Blended shared control is a method to continuously combine control inputs from traditional automatic control systems and human operators for control of machines. An automatic control system generates control input based on feedback of measured signals, whereas a human operator generates control input based on experience, task knowledge, and awareness and sensing of the environment in which the machine is operating. Such active blending of inputs from the automatic control agent and the human agent to jointly control machines is expected to provide benefits in terms of utilizing the unique features of both agents, i.e., better task execution performance of automatic control systems based on sensed signals and maintaining situation awareness by having the human in the loop to handle safety concerns and environmental uncertainties. The shared control approach in this sense provides an alternative to full autonomy. Many existing and future applications of such an approach include automobiles, underwater vehicles, ships, airplanes, construction machines, space manipulators, surgery robots, and power wheelchairs, where machines are still mostly operated by human operators for safety concerns. Developing machines for full autonomy requires not only advances in machines but also the ability to sense the environment by placing sensors in it; the latter could be a very difficult task for many such applications due to perceived uncertainties and changing conditions. The notion of blended shared control, as a more practical alternative to full autonomy, enables keeping the human operator in the loop to initiate machine actions with real-time intelligent assistance provided by automatic control. The problem of how to blend the two inputs and development of associated scientific tools to formalize and achieve blended shared control is the focus of this work. Specifically, the following essential aspects are investigated and studied. Task learning: modeling of a human-operated robotic task from demonstration into subgoals such that execution patterns are captured in a simple manner and provide reference for human intent prediction and automatic control generation. Intent prediction: prediction of human operator's intent in the framework of subgoal models such that it encodes the probability of a human operator seeking a particular subgoal. Input blending: generating automatic control input and dynamically combining it with human operator's input based on prediction probability; this will also account for situations where the human operator may take unexpected actions to avoid danger by yielding full control authority to the human operator. Subgoal adjustment: adjusting the learned, nominal task model dynamically to adapt to task changes, such as changes to target object, which will cause the nominal model learned from demonstration to lose its effectiveness. This dissertation formalizes these notions and develops novel tools and algorithms for enabling blended shared control. To evaluate the developed scientific tools and algorithms, a scaled hydraulic excavator for a typical trenching and truck-loading task is employed as a specific example. Experimental results are provided to corroborate the tools and methods. To expand the developed methods and further explore shared control with different applications, this dissertation also studied the collaborative operation of robot manipulators. Specifically, various operational interfaces are systematically designed, a hybrid force-motion controller is integrated with shared control in a mixed world-robot frame to facilitate human-robot collaboration, and a method that utilizes vision-based feedback to predict the human operator's intent and provides shared control assistance is proposed. These methods provide ways for human operators to remotely control robotic manipulators effectively while receiving assistance by intelligent shared control in different applications. Several robotic manipulation experiments were conducted to corroborate the expanded shared control methods by utilizing different industrial robots

An Augmented Interaction Strategy For Designing Human-Machine Interfaces For Hydraulic Excavators

Lack of adequate information feedback and work visibility, and fatigue due to repetition have been identified as the major usability gaps in the human-machine interface (HMI) design of modern hydraulic excavators that subject operators to undue mental and physical workload, resulting in poor performance. To address these gaps, this work proposed an innovative interaction strategy, termed “augmented interaction”, for enhancing the usability of the hydraulic excavator. Augmented interaction involves the embodiment of heads-up display and coordinated control schemes into an efficient, effective and safe HMI. Augmented interaction was demonstrated using a framework consisting of three phases: Design, Implementation/Visualization, and Evaluation (D.IV.E). Guided by this framework, two alternative HMI design concepts (Design A: featuring heads-up display and coordinated control; and Design B: featuring heads-up display and joystick controls) in addition to the existing HMI design (Design C: featuring monitor display and joystick controls) were prototyped. A mixed reality seating buck simulator, named the Hydraulic Excavator Augmented Reality Simulator (H.E.A.R.S), was used to implement the designs and simulate a work environment along with a rock excavation task scenario. A usability evaluation was conducted with twenty participants to characterize the impact of the new HMI types using quantitative (task completion time, TCT; and operating error, OER) and qualitative (subjective workload and user preference) metrics. The results indicated that participants had a shorter TCT with Design A. For OER, there was a lower error probability due to collisions (PER1) with Design A, and lower error probability due to misses (PER2)with Design B. The subjective measures showed a lower overall workload and a high preference for Design B. It was concluded that augmented interaction provides a viable solution for enhancing the usability of the HMI of a hydraulic excavator

Volume 1 – Symposium

We are pleased to present the conference proceedings for the 12th edition of the International Fluid Power Conference (IFK). The IFK is one of the world’s most significant scientific conferences on fluid power control technology and systems. It offers a common platform for the presentation and discussion of trends and innovations to manufacturers, users and scientists. The Chair of Fluid-Mechatronic Systems at the TU Dresden is organizing and hosting the IFK for the sixth time. Supporting hosts are the Fluid Power Association of the German Engineering Federation (VDMA), Dresdner Verein zur Förderung der Fluidtechnik e. V. (DVF) and GWT-TUD GmbH. The organization and the conference location alternates every two years between the Chair of Fluid-Mechatronic Systems in Dresden and the Institute for Fluid Power Drives and Systems in Aachen. The symposium on the first day is dedicated to presentations focused on methodology and fundamental research. The two following conference days offer a wide variety of application and technology orientated papers about the latest state of the art in fluid power. It is this combination that makes the IFK a unique and excellent forum for the exchange of academic research and industrial application experience. A simultaneously ongoing exhibition offers the possibility to get product information and to have individual talks with manufacturers. The theme of the 12th IFK is “Fluid Power – Future Technology”, covering topics that enable the development of 5G-ready, cost-efficient and demand-driven structures, as well as individual decentralized drives. Another topic is the real-time data exchange that allows the application of numerous predictive maintenance strategies, which will significantly increase the availability of fluid power systems and their elements and ensure their improved lifetime performance. We create an atmosphere for casual exchange by offering a vast frame and cultural program. This includes a get-together, a conference banquet, laboratory festivities and some physical activities such as jogging in Dresden’s old town.:Group A: Materials Group B: System design & integration Group C: Novel system solutions Group D: Additive manufacturing Group E: Components Group F: Intelligent control Group G: Fluids Group H | K: Pumps Group I | L: Mobile applications Group J: Fundamental

New Perspectives on Electric Vehicles

Modern transportation systems have adverse effects on the climate, emitting greenhouse gases and polluting the air. As such, new modes of non-polluting transportation, including electric vehicles and plug-in hybrids, are a major focus of current research and development. This book explores the future of transportation. It is divided into four sections: “Electric Vehicles Infrastructures,” “Architectures of the Electric Vehicles,” “Technologies of the Electric Vehicles,” and “Propulsion Systems.” The chapter authors share their research experience regarding the main barriers in electric vehicle implementation, their thoughts on electric vehicle modelling and control, and network communication challenges

Organizational Measures to Improve Energy Efficiency of Construction Production

Секция IV : Организация строительства и управление недвижимостьюС развитием промышленности человечество в геометрической прогрессии увеличивало потребление невозобновляемых энергетических ресурсов. Строительство занимает весомую часть в экономике Российской Федерации, где при выполнении строительно-монтажных работ затрачивается немалое количество различных энергоресурсов. В представленной статье разработаны организационно-технологические мероприятия по снижению потребления энергоресурсов и альтернативные решения по повышению энергоэффективности строительного производства. Проведенные исследования позволили выявить приоритетные направления и резервы для оптимизации энерго- и ресурсопотребления строительства.=With the development of industry, mankind exponentially increased the consumption of nonrenewable energy resources. Construction occupies a significant part in the economy of the Russian Federation, where a considerable amount of various energy resources is spent during construction and installation works. The article presents organizational and technological measures to reduce energy consumption and alternative solutions to improve energy efficiency of construction production. The conducted researches allowed to reveal priority directions and reserves for optimization of energy and resource consumption of construction

Organizational Measures to Improve Energy Efficiency of Construction Production

Секция IV : Организация строительства и управление недвижимостьюС развитием промышленности человечество в геометрической прогрессии увеличивало потребление невозобновляемых энергетических ресурсов. Строительство занимает весомую часть в экономике Российской Федерации, где при выполнении строительно-монтажных работ затрачивается немалое количество различных энергоресурсов. В представленной статье разработаны организационно-технологические мероприятия по снижению потребления энергоресурсов и альтернативные решения по повышению энергоэффективности строительного производства. Проведенные исследования позволили выявить приоритетные направления и резервы для оптимизации энерго- и ресурсопотребления строительства.=With the development of industry, mankind exponentially increased the consumption of nonrenewable energy resources. Construction occupies a significant part in the economy of the Russian Federation, where a considerable amount of various energy resources is spent during construction and installation works. The article presents organizational and technological measures to reduce energy consumption and alternative solutions to improve energy efficiency of construction production. The conducted researches allowed to reveal priority directions and reserves for optimization of energy and resource consumption of construction

EXAMINING SUCCESS FACTORS FOR INNOVATION IN THE MEDICAL DEVICE SPACE: A PATH FORWARD FOR FUTURE ENTREPRENEURS

The medical device industry in the United States is a complicated and dynamic business environment. This industry is characterized by some high-profile entrepreneurial successes and many failures. Although innovation and entrepreneurship are well studied in the literature, many existing theories face challenges when scrutinized within the context of medical devices. This thesis explores the determinants of entrepreneurial success in the medical device industry. Two models are considered: one that explains the ability of current medical device startups to receive funding and one that explains the likelihood that a startup will either succeed or fail. The models show that increasing the founders’ LinkedIn followers, focusing on an FDA Class 3 medical device, and being embedded in a robust entrepreneurial ecosystem are all significant and positive factors for determining funding. In addition, having more founders and including a founder with a medical degree both increase the likelihood of startup success.Bachelor of Business Administratio

A stochastic method for representation, modelling and fusion of excavated material in mining

The ability to safely and economically extract raw materials such as iron ore from a greater number of remote, isolated and possibly dangerous locations will become more pressing over the coming decades as easily accessible deposits become depleted. An autonomous mining system has the potential to make the mining process more efficient, predictable and safe under these changing conditions. One of the key parts of the mining process is the estimation and tracking of bulk material through the mining production chain. Current state-of-the-art tracking and estimation systems use a deterministic representation for bulk material. This is problematic for wide-scale automation of mine processes as there is no measurement of the uncertainty in the estimates provided. A probabilistic representation is critical for autonomous systems to correctly interpret and fuse the available data in order to make the most informed decision given the available information without human intervention. This thesis investigates whether bulk material properties can be represented probabilistically through a mining production chain to provide statistically consistent estimates of the material at each stage of the production chain. Experiments and methods within this thesis focus on the load-haul-dump cycle. The development of a representation of bulk material using lumped masses is presented. A method for tracking and estimation of these lumped masses within the mining production chain using an 'Augmented State Kalman Filter' (ASKF) is developed. The method ensures that the fusion of new information at different stages will provide statistically consistent estimates of the lumped mass. There is a particular focus on the feasibility and practicality of implementing a solution on a production mine site given the current sensing technology available and how it can be adapted for use within the developed estimation system (with particular focus on remote sensing and volume estimation)