Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the extrusion sequence, end-effector poses, joint configurations, and transition trajectories for spatial trusses with nonstandard topologies. This paper also presents the first detailed computation data to reveal the runtime bottleneck on solving SAMP problems, which provides insight and comparing baseline for future algorithmic development. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure

Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

What's next? : operational support for business process execution

In the last decade flexibility has become an increasingly important in the area of business process management. Information systems that support the execution of the process are required to work in a dynamic environment that imposes changing demands on the execution of the process. In academia and industry a variety of paradigms and implementations has been developed to support flexibility. While on the one hand these approaches address the industry demands in flexibility, on the other hand, they result in confronting the user with many choices between different alternatives. As a consequence, methods to support users in selecting the best alternative during execution have become essential. In this thesis we introduce a formal framework for providing support to users based on historical evidence available in the execution log of the process. This thesis focuses on support by means of (1) recommendations that provide the user an ordered list of execution alternatives based on estimated utilities and (2) predictions that provide the user general statistics for each execution alternative. Typically, estimations are not an average over all observations, but they are based on observations for "similar" situations. The main question is what similarity means in the context of business process execution. We introduce abstractions on execution traces to capture similarity between execution traces in the log. A trace abstraction considers some trace characteristics rather than the exact trace. Traces that have identical abstraction values are said to be similar. The challenge is to determine those abstractions (characteristics) that are good predictors for the parameter to be estimated in the recommendation or prediction. We analyse the dependency between values of an abstraction and the mean of the parameter to be estimated by means of regression analysis. With regression we obtain a set of abstractions that explain the parameter to be estimated. Dependencies do not only play a role in providing predictions and recommendations to instances at run-time, but they are also essential for simulating the effect of changes in the environment on the processes, both locally and globally. We use stochastic simulation models to simulate the effect of changes in the environment, in particular changed probability distribution caused by recommendations. The novelty of these models is that they include dependencies between abstraction values and simulation parameters, which are estimated from log data. We demonstrate that these models give better approximations of reality than traditional models. A framework for offering operational support has been implemented in the context of the process mining framework ProM

User recommendations for the optimized execution of business processes

In order to be able to flexibly adjust a company's business processes (BPs) there is an increasing interest in flexible process-aware information systems (PAISs). This increasing flexibility, however, typically implies decreased user guidance by the PAIS and thus poses significant challenges to its users. As a major contribution of this work, we propose a recommendation system which assists users during process execution to optimize performance goals of the processes. The recommendation system is based on a constraint-based approach for planning and scheduling the BP activities and considers both the control-flow and the resource perspective. To evaluate the proposed constraint-based approach different algorithms are applied to a range of test models of varying complexity. The results indicate that, although the optimization of process execution is a highly constrained problem, the proposed approach produces a satisfactory number of suitable solutions.Ministerio de Ciencia e Innovación TIN2009-1371

Shift Scheduling Optimization for PSU Library

Scheduling is important in any business as it creates an order and flow ensuring that all the tasks are covered at appropriate times. According to experts, scheduling determines the economics of a job, the quality of the team, and the skill-building and motivation of professionals doing the work. Therefore, it is essential to have optimized staff schedules to meet the requirements of staff availability, tasks coverage, shift equity and staff preferences. Though staff scheduling is of such prime importance, it is mostly implemented in traditional ways of manually creating spreadsheets and web calendars proving to be laborious and often leaving room for errors. Additionally, staff preferences are arbitrarily handled through this format which results in overstaffing /understaffing of resources. Our project is aimed at developing an optimization model of staff scheduling for the PSU library using linear programming and create a tool with open solver that reduces the surplus working hours of the staff in the library while maximizing the staff preferences. We expect our model to achieve better efficiency and flexibility than the traditional format of scheduling implemented by the library. Also, our model could have broader capabilities of implementation in different departments of the Portland State University

Enablers for uncertainty quantification and management in early stage computational design. An aircraft perspective

Presented in this thesis are novel methods for uncertainty quantification and management (UQ&M) in computational engineering design. The research has been motivated by the industrial need for improved UQ&M techniques, particularly in response to the rapid development of the model-based approach and its application to the (early) design of complex products such as aircraft. Existing work has already addressed a number of theoretical and computational challenges, especially regarding uncertainty propagation. In this research, the contributions to knowledge are within the wider UQ&M area. The first contribution is related to requirements for an improved margin management policy, extracted from the FP7 European project, TOICA (Thermal Overall Integrated Conception of Aircraft). Margins are traditional means to mitigate the effect of uncertainty. They are relatively better understood and less intrusive in current design practice, compared with statistical approaches. The challenge tackled in this research has been to integrate uncertainty analysis with deterministic margin allocations, and to provide a method for exploration and trade-off studies. The proposed method incorporates sensitivity analysis, uncertainty propagation, and the set-based design paradigm. The resulting framework enables the designer to conduct systematic and interactive trade-offs between margins, performances and risks. Design case studies have been used to demonstrate the proposed method, which was partially evaluated in the TOICA project. The second contribution addresses the industrial need to properly ‘allocate’ uncertainty during the design process. The problem is to estimate how much uncertainty could be tolerated from different sources, given the acceptable level of uncertainty associated with the system outputs. Accordingly, a method for inverse uncertainty propagation has been developed. It is enabled by a fast forward propagation technique and a workflow reversal capability. This part of the research also forms a contribution to the TOICA project, where the proposed method was applied on several test-cases. Its usefulness was evaluated and confirmed through the project review process. The third contribution relates to the reduction of UQ&M computational cost, which has always been a burden in practice. To address this problem, an efficient sensitivity analysis method is proposed. It is based on the reformulation and approximation of Sobol’s indices with a quadrature technique. The objective is to reduce the number of model evaluations. The usefulness of the proposed method has been demonstrated by means of analytical and practical test-cases. Despite some limitations for several specific highly non-linear cases, the tests confirmed significant improvement in computational efficiency for high dimensional problems, compared with traditional methods. In conclusion, this research has led to novel UQ&M tools and techniques, for improved decision making in computational engineering design. The usefulness of these methods with regard to efficiency and interactivity has been demonstrated through relevant test-cases and qualitative evaluation by (industrial) experts. Finally, it is argued that future work in this field should involve research and development of a comprehensive framework, which is able to accommodate uncertainty, not only with regard to computation, but also from the perspective of (expert) knowledge and assumptions