Formal based operation strategy design for collaborative shared teleoperation system

A zone-based delivery scheduling approach is introduced in this research using artificial intelligence techniques. In this approach, delivery scheduling is conducted at three different levels: (1) prediction of customer delivery demand using a multi-level pattern clustering and matching method, (2) creation of delivery zones based on customer delivery demand, and (3) identification of the optimal sequence and timing parameters of delivery tasks. The system was implemented using Smalltalk, an object oriented programming language.

A fuzzy mathematics based optimal delivery scheduling approach

A fuzzy-based optimal delivery scheduling approach is introduced in this research. In this approach, optimal delivery scheduling is conducted at three different levels considering (1) one driver and one load, (2) one driver and multiple loads, and, (3) multiple drivers and multiple loads. Fuzzy-based clustering methods are employed to classify delivery tasks into driver groups, load groups, and location groups. The optimal sequence and timing parameters of the delivery tasks are identified using the fuzzy-based clustering results and state-space search. The intelligent optimal delivery scheduling system was implemented using Smalltalk.

Optimal task sequence planning using a simulated annealing technique is developed for a high throughput chip placement machine. To minimize tact time, chips are mounted by multiple robots on moving PC boards being transferred by a precision conveyer. To avoid both geometric and dynamic interferences among multiple robots, while minimizing the tact time, the sequence of operations as well as the allocation of the multiple robots are optimized by using simulated annealing. The optimization problem is a type of traveling salesman problem, but is highly complicated because the target destinations for placing individual chips keep moving together with the PC board. First, the configuration of the new chip placement system is described. The task sequence optimization problem is then formulated, followed by the application of simulated annealing to solving the problem. An efficient algorithm for perturbing a task sequence is developed and applied to practical chip placement tasks.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Human-centered design teams must integrate diverse individual perspectives into a shared understanding during conceptual design. The team's shared knowledge of their users becomes the basis for later design decisions. We conducted a formative study that shows how generic groupware is insufficient to support the transition from individual to collaborative creative work. We developed a set of design guidelines and implemented them in Dazzle, a collaborative shared display system for co-located design team meetings. Dazzle associates the action of showing information on the shared display with granting the rest of the team access to that information: showing is sharing. Dazzle also records a history of shown files. Team members can annotate this log using cross-platform synchronized clients. Teams of novice designers tested Dazzle over two consecutive sessions: the first focused on synthesizing user research, and the second focused on brainstorming. Dazzle was very effective at grounding team conversations about user research, but was used less for sharing information during brainstorming tasks. Items from the shared activity log were used as sources of inspiration and decision criteria during the brainstorming task. Future work includes additional support for active decision-making, and ambient feedback on design activity.

PurposeThe purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.Design/methodology/approachA model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.FindingsThis modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.Research limitations/implicationsThe research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.Practical implicationsThe study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2 footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).Social implicationsThe growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.Originality/valueDespite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.

The paper presents an overview of a pilot study aiming at developing university students’ writing competence with the use of a selected Web 2.0 collaboration tool as authoring environment for Task-Based Language Teaching. Online word processors were used to design a pre-task, task and post-task sequence which was implemented in varied modes of work when teaching English to students at the Department of German Studies, Maria CurieSkłodowska University in Lublin, Poland. The specific aim of the study was to investigate the applicability of one particular online collaborative environment, namely an online word processor such as Google Docs (http://docs.google.com), to implement form-focused language teaching at a tertiary level. The study was conducted in an action research fashion, with two parallel groups of students using two different collaborative environments (wikis and online word processors). As it was found out, the idea of learning writing in collaboration mediated by online resources was new to students and the awareness of how to make the best use of peer editing and peer feedback had to be established among students first. Once it was done, however, collaborative learning online proved to be a powerful vehicle for language instruction. The emergence of new Web 2.0 tools facilitating collaboration and publishing such as online word processors has helped to redefine the process of teaching language skills, and specifically writing, in the TBLT framework.

Human-centered Evaluation of Shared Teleoperation System for Maintenance and Repair Tasks in Nuclear Power Plants

We present an experimental study of the single source shortest path problem with non-negative edge weights (NSSP) on large-scale graphs using the Δ-stepping parallel algorithm. We report performance results on the Cray MTA-2, a multithreaded parallel computer. The MTA-2 is a high-end shared memory system offering two unique features that aid the efficient parallel implementation of irregular algorithms: the ability to exploit fine-grained parallelism, and low-overhead synchronization primitives. Our implementation exhibits remarkable parallel speedup when compared with competitive sequential algorithms, for low-diameter sparse graphs. For instance, Δ-stepping on a directed scale-free graph of 100 million vertices and 1 billion edges takes less than ten seconds on 40 processors of the MTA-2, with a relative speedup of close to 30. To our knowledge, these are the first performance results of a shortest path problem on realistic graph instances in the order of billions of vertices and edges.

In large-scale arable farming, multiple sequential operations involving multiple machines must be carried out simultaneously due to restrictions of short time windows. However, the coordination and planning of multiple sequential operations is a nontrivial task for farmers, since each operation may have its own set of operational features, e.g., operating width and turning radius. Taking the two sequential operations—hoeing cultivation and seeding—as an example, the seeder has double the width of the hoeing cultivator, and the seeder must remain idle while waiting for the hoeing cultivator to finish two rows before it can commence its seeding operation. A flow-shop working mode can coordinate multiple machines in multiple operations within a field when different operations have different implement widths. To this end, an auto-steering-based collaborative operating system for fleet management (FMCOS) was developed to realize an in-field flow-shop working mode, which is often adopted by the scaled agricultural machinery cooperatives. This paper proposes the structure and composition of the FMCOS, the method of operating strip segmenting, and a new algorithm for strip state updating between successive field operations under an optimal strategy for waiting time conditioning between sequential operations. A simulation model was developed to verify the state-updating algorithm. Then, the prototype system of FMCOS was combined with auto-steering systems on tractors, and the collaborative operating system for the server was integrated. Three field experiments of one operation, two operations, and three operations were carried out to verify the functionality and performance of FMCOS. The results of the experiment showed that the FMCOS could coordinate in-field fleet operations while improving both the job quality and the efficiency of fleet management by adopting the flow-shop working mode.

Fully autonomous robot systems, though highly desired, face substantial theoretical and practical challenges when being deployed into a dynamic environment where humans co-exist. To tackle this challenge, this thesis investigates the concept of human-in-the-loop (HITL) systems, which incorporate human input to enhance robot functionality. HITL systems offer a pragmatic alternative, combining human versatility with robotic precision. This research aims to address critical questions in one particular HITL architecture which prioritizes the dominant role of the human within the system, positioning the robot primarily in an assistive capacity that adheres to human commands to facilitate the achievement of a shared goal. It explores two primary paradigms of shared assistance methods-Shared Control (SC) and Shared Autonomy (SA)-and discusses the system designs as well as specific algorithms to implement the three critical components in a HITL system: human intention estimation, modulation of human inputs and robot autonomy, and the human-robot communication channel. Due to the variety of use cases and their unique challenges, four distinct HITL systems are developed and analyzed to illustrate how shared assistance methods could be employed to assist human operators: an assistive wheelchair for indoor navigation, a human-centered robot system design for industrial tasks, a mobile bi-manual robot for tele-manipulation, and a virtual reality (VR)-based customizable shared control system for fine teleoperation. Although each system represents a comprehensive robotic solution, the research contributions for each work vary. In the assistive wheelchair navigation system, the focus was on human intent estimation via a low-throughput interface utilizing a recursive Bayesian filter, with significant efforts dedicated to developing a real-time user interface serving as the communication channel. In the human-robot collaboration system for industrial setting, the emphasis was on human state estimation through camera-based posture tracking and exploring the interplay between robot behavior and human ergonomics. For the two teleoperation systems, the primary focus was on the real-time modulation of human inputs and robot autonomy to aid in achieving dexterous manipulation tasks. A novel VR-based user interface was developed to enable users to customize the robot autonomy level. Each system was validated through a pilot study involving 10-20 human subjects, accompanied by extensive data analysis to provide insights into designing HITL systems for various applications. In conclusion, this thesis contributes to a deeper understanding of HITL systems, highlighting their potential to enhance human productivity, ergonomics, and quality of life in varied applications through concrete examples. The integration of human intent estimation and real-time shared control methods into robotic systems demonstrates the feasibility and benefits of HITL approaches. Our extensive experimental analysis underscores the critical role of human feedback in designing practical HITL systems that can be deployed in real-world scenarios. --Author's abstract

The current remote operation and maintenance platform of substation DC system mainly relies on manual use of each independent monitoring device to collect and transmit data for remote operation and maintenance management, which is difficult to realize intelligent operation of substation and also leads to slow response and poor processing performance of operation and maintenance platform. In order to optimize the above-mentioned problems, the remote operation and maintenance platform of substation DC system for on-site defect processing is studied and designed. Based on the framework of the operation and maintenance platform, a combination of balanced and unbalanced bridge-based principle is used to monitor the DC insulation status. The JSON protocol is used to realize the data interaction between the operation and maintenance platform and the management terminals, and to design the DC system defect processing scheme. The Floyd shortest path algorithm is used to plan the operation and maintenance inspection strategy of the platform to achieve fast operation and maintenance of the DC system. The platform performance test results show that the performance index of the designed operation and maintenance platform is significantly improved, and the average request response time is shortened by 28.1%, and the operation and maintenance management effect is better.

Robotic systems, working together as a team, are becoming valuable players in different real-world applications, from disaster response to warehouse fulfillment services. Centralized solutions to coordinating multi-robot teams often suffer from poor scalability and vulnerability to communication disruptions. This paper develops a decentralized multi-agent task allocation (Dec-MATA) algorithm for multi-robot applications. The task planning problem is posed as a maximum-weighted matching of a bipartite graph, the solution of which using the blossom algorithm allows each robot to autonomously identify the optimal sequence of tasks it should undertake. The graph weights are determined based on a soft clustering process, which also plays a problem decomposition role seeking to reduce the complexity of the individual-agents’ task assignment problems. To evaluate the new Dec-MATA algorithm, a series of case studies (of varying complexity) are performed, with tasks being distributed randomly over an observable 2D environment. A centralized approach, based on a state-of-the-art MILP formulation of the multi-Traveling Salesman problem is used for comparative analysis. While getting within 7–28% of the optimal cost obtained by the centralized algorithm, the Dec-MATA algorithm is found to be 1–3 orders of magnitude faster and minimally sensitive to task-to-robot ratios unlike the centralized algorithm.

This work proposes a task scheduling method in an optimization framework with applications on a dual-arm cooking robot in a controlled cooking environment. A mixed-integer programming (MIP) framework is used to find an optimal sequence of tasks to be done for each arm. The optimization is fast and simple as a priori information about the tasks to be scheduled reveal dependency and kinematic constraints between them which significantly reduces the problem size as infeasible solutions are removed pre-optimization. The optimization approach's feasibility is validated on a series of simulations and an in-depth scalability analysis is conducted by changing the number of tasks to be done, the dishes to be completed, as well as the locations where the tasks can be done. Considering the unique configuration of the platform, analysis on selecting the minimum time required tasks as opposed tasks that will give the most flexibility to the other arm is also done. An example is presented on a real set of tasks to show the optimality of the solution.

To address the issues of path redundancy, frequent turning, and low task allocation efficiency in multi-AGV path planning for automotive warehousing environments, this paper proposes a cooperative path planning method that integrates the Grey Wolf Optimizer (GWO) with a penalty-function-based A-star algorithm. First, a dynamic penalty function mechanism is introduced into the traditional A-star algorithm, where penalty weights are applied to AGV steering actions to effectively suppress unnecessary turns, thus improving path smoothness and operational efficiency. A bidirectional search strategy is also adopted to reduce computational complexity. Second, the multi-target task allocation problem is modeled as a Traveling Salesman Problem (TSP) and globally optimized using an enhanced Grey Wolf Optimizer. By incorporating a nonlinear convergence factor and a dynamic weight strategy, the algorithm’s ability to escape local optima is improved, allowing for rapid generation of the optimal task sequence. Experiments conducted on a warehouse map show that, compared with the traditional A-star algorithm, the proposed method offers significant advantages in path length, number of unnecessary turns, and overall task completion time. This method greatly enhances the transportation reliability, safety, and operational efficiency of AGV systems in automotive warehousing, providing effective technical support for the intelligent operation of high-density warehousing logistics.