Modelling iteration in engineering design

This paper examines design iteration and its modelling in the simulation of New Product Development (NPD) processes. A framework comprising six perspectives of iteration is proposed and it is argued that the importance of each perspective depends upon domain-specific factors. Key challenges of modelling iteration in process simulation frameworks such as the Design Structure Matrix are discussed, and we argue that no single model or framework can fully capture the iterative dynamics of an NPD process. To conclude, we propose that consideration of iteration and its representation could help identify the most appropriate modelling framework for a given process and modelling objective, thereby improving the fidelity of design process simulation models and increasing their utility

Using specification and description language for life cycle assesment in buildings

The definition of a Life Cycle Assesment (LCA) for a building or an urban area is a complex task due to the inherent complexity of all the elements that must be considered. Furthermore, a multidisciplinary approach is required due to the different sources of knowledge involved in this project. This multidisciplinary approach makes it necessary to use formal language to fully represent the complexity of the used models. In this paper, we explore the use of Specification and Description Language (SDL) to represent the LCA of a building and residential area. We also introduce a tool that uses this idea to implement an optimization and simulation mechanism to define the optimal solution for the sustainability of a specific building or residential.Peer ReviewedPostprint (published version

Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing

Robotic picking from cluttered bins is a demanding task, for which Amazon Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required stowing items into a storage system, picking specific items, and packing them into boxes. In this paper, we describe the entry of team NimbRo Picking. Our deep object perception pipeline can be quickly and efficiently adapted to new items using a custom turntable capture system and transfer learning. It produces high-quality item segments, on which grasp poses are found. A planning component coordinates manipulation actions between two robot arms, minimizing execution time. The system has been demonstrated successfully at ARC, where our team reached second places in both the picking task and the final stow-and-pick task. We also evaluate individual components.Comment: In: Proceedings of the International Conference on Robotics and Automation (ICRA) 201