Rolling Kanban: a new visual tool to schedule family batch manufacturing processes with kanban

Rolling Kanban is the name of a kanban management methodology for batch processes manufacturing environment. Proposed in the early 2000s by FESTO Consulting, for two decades we have lost knowledge both from operative and scientific point of view. Basically, Rolling Kanban means a visual planning methodology based on the production of product-families and variants where: (i) set-up times are reduced between the products of the same family, and (ii) relevant times (dozens of minutes if not even hours) must be considered for changeover between products of different families. In addition, the cyclic production sequence between different product families cannot be maintained. Considering that very few technical information and documents are available about this approach, the main objective of this paper is to retrieve and present, for the first time to the scientific community, the Rolling Kanban methodology. Besides, a real industrial implementation concerning a manufacturer of domestic fittings is discussed as a case study. More specifically, two novel versions of the original Rolling Kanban technique are fully presented to effectively overcome certain limits and criticalities found during its operative use, such as the difficulty to realise a pull production, considering increased set-up time for changeover between products of different families.     

码垛机器人运动学分析及关节空间轨迹规划研究

以IRB-460工业机器人为研究对象,针对其运动学建模及在笛卡尔空间和关节空间下的轨迹规划方法展开研究并进行对比。对机器人本体进行了机器人运动学分析,利用多项式轨迹插值算法进行了关键点间的轨迹规划,完成了重叠型码垛的作业流程。利用Robotics Toolbox完成了轨迹规划算法仿真,并进行了结果分析。仿真结果表明在关节空间下进行轨迹插补所得的路径点更为优化,所得的轨迹更为满足作业任务,具备合理性。     

Drivers of revitalization in Great Lakes coastal communities

Sediment remediation and habitat restoration projects have been increasingly employed along the coast of the Great Lakes to improve environmental quality since the designation of 43 highly degraded Areas of Concern (AOCs) by the 1987 Great Lakes Water Quality Agreement between the U.S. and Canada. Improvements in water quality, habitat, and other environmental conditions can also support community wellbeing and revitalization; however, the mechanisms that support these connections are relatively unclear. We address this gap through a case study of three AOCs near Lake Michigan: 1) Grand Calumet River; 2) White Lake, and 3) Muskegon Lake. By analyzing secondary data and planning documents, we found that alongside environmental cleanup, anchor institutions, housing and economic development, and local events drive revitalization. Our research also illustrates that, rather than acting as discrete processes, environmental cleanup and revitalization drivers overlap in time and space. Finally, our research reveals a high level of variation within and across AOCs in terms of diverse socioeconomic contexts, planning capacities, and existing partnerships. Together, our findings point to the need for collaborative and inclusive planning processes that account for the heterogeneity present within and across AOCs to simultaneously support remediation, restoration, and revitalization and to sustain continued revitalization in AOC communities after delisting.     

Motion Planning by T‐RRT with Potential Function for Vertical Articulated Robots

RRT (rapidly exploring random tree) with random sampling is an effective method for path planning, and is often used for robot manipulators. The RRT has many modified methods for applying various problems and conditions. Particularly, T‐RRT (Transition‐based RRT) one of those has advantage that it is able to adopt arbitrary evaluation function. In this paper, a novel path planning method based on the T‐RRT is proposed for ensuring “quality” of a generated path. Then, its effectiveness is evaluated via comparison with other sampling‐based methods using simulation of the industrial robot having seven DOFs.     

基于压迫感分析的城市高架桥美学设计研究

为解决城市高架桥对其地面道路及周边地块产生的压迫感这一问题,并且指导城市高架桥的设计和规划,使之在满足交通功能和用地要求的同时兼顾美学的考虑,研究根据压迫感主要来源为建筑对视野限制的推论,分析了压迫感的主要影响因素为建筑在视野中形成的视角,讨论了现有立体角理论模型的适用性,并且结合城市高架桥视角的特点,将立体角理论模型简化为平面视角理论模型以增强其适用性。建立了桥梁的结构尺寸与影响点的视角的关系式,以压迫感接受率表征压迫感的大小,再根据调查统计数据拟合,确定视角与压迫感接受率的关系曲线,从而得到桥梁的结构尺寸与影响点压迫感接受率的关系式。在规划和设计时,可根据该公式合理确定桥梁下地面道路的红线宽度或两侧地块的退红净距。     

华南植物园藤本植物专类园规划设计

专类园是一种强调专类植物收集、展示和植物造 景的园林形式，在植物园建设中具有重要地位。藤本植物因 其独特的观赏特征、生长习性及生态效应，近年已成为城市 园林绿化的重要造景材料。在分析华南植物园藤本植物专类 园建设背景、园址概况的基础上，结合其功能定位，提出了 科学性、功能性、地域性、艺术性及以人为本的规划原则； 确定了“一轴、三线、三点、五片区”的总体规划布局。在 文化线的园林构图及造景上，重点阐述了三大主题景观节 点——帷幄疏影、西关居、文昌亭的设计理念及其园林建筑 风格，探讨了岭南传统建筑、小品及文化在构建地域特色园 林景观中的作用；在植物线的园林造景上，简介了各景区的 植物布置方式及组景模式；在科普旅游线的组织上，介绍了 园区的园林小品造景意境及科普设施布置形式，以期为中国 藤本植物专类园建设提供参考。     

Cooperative path planning with priority target assignment and collision avoidance guidance for rescue unmanned surface vehicles in a complex ocean environment

In this paper, a novel cooperative path planning scheme of unmanned surface vehicles (USVs) for rescuing targets in a complex ocean environment is proposed. The primary objective of the rescue USVs is to bring all targets back safely on the premise of first rescuing priority targets, while optimizing the path length, the navigation time and the angular energy. The main contributions of this paper are as follows: (1) The proposed K-means-division (KMD) algorithm is able to identify a complex ocean environment with collision-free zone and static-obstacles zone; (2) The proposed path planning method with fast-marching-method-based ellipse guidance range (E-FMM) is able to optimize the angular energy while ensuring safety; (3) The proposed cooperative management system (including priority-target-assignment (PTA) with reward-mechanism genetic-optimization (RM-GO) and collision-avoidance (CA) guidance law with Tangent-based surge-varying wave-disturbances-observer (Tangent-SV-WDO)) can accomplish the mission of the rescue USVs. Comparative studies with the state-of-the-art methods demonstrate that the proposed cooperative path planning scheme is superior in terms of priority-target-assignment (PTA) and collision-avoidance (CA) of the actual rescue work.     

Recovery planning of industrial robots based on semantic information of failures and time-dependent utility

Industrial robots are required to recover from temporary errors and continue operations under a changing environment. In this paper, we propose a recovery planning system that considers the semantic information behind errors during robotic actions. In order to establish general repair strategies for feasible recovery plans under uncertainties, the proposed system uses a conceptual graph based on case grammar and a Bayesian network that is dynamically constructed according to the semantic information. In addition, we tackle the problem that the wealth of the recovery plan depends on the uncertainty of execution costs against the deadline at the production site. The proposed system controls the decision model by using a time-dependent utility. We demonstrate the effectiveness of the proposed system through simulations of assembly tasks by multiple robots.     

个性化学习路径推荐综述

近年来，伴随着现代信息技术的迅猛发展，以人工智能为代表的新兴技术在教育领域得到了广泛应用，引发了学习理念和方式的深刻变革.在这种大背景下，在线学习超越了时空的限制，为学习者“随时随地”学习提供了更多的可能性，从而得到了蓬勃发展.然而，在线学习中师生时间、空间分离的特征，导致教师无法及时掌握学生的学习状态，一定程度上制约了在线学习中教学质量的提升.面对多元化的学习需求及海量学习资源，如何迅速完成学习目标、降低学习成本、合理分配学习资源等问题成为限制个人和时代发展的重大问题.然而，传统的“一刀切”的教育模式已经不能满足人们获取知识的需求了，需要一个更高效、更科学的个性化教育模式，以帮助学习者以最小的学习成本最大限度地完成学习目标.基于以上背景，如何自动高效识别学习者特征，高效地组织和分配学习资源，为每一位学习者规划个性化路径，成为面向个体的精准化教育资源匹配机制研究中亟待解决的问题.系统地综述并分析了当前个性化学习路径推荐的研究现状，并从多学科领域的角度分析了对于同一问题的不同研究思路，同时也归纳总结了当前研究中最为主流的核心推荐算法.最后，强调当前研究存在的主要不足之处.     

Modeling dynamic construction work template from existing scheduling records via sequential machine learning

Today, construction planning and scheduling is almost always performed manually, by experienced practitioners. The knowledge of those individuals is materialized, maintained, and propagated through master schedules and look-ahead plans. While historical project schedules are available, manually mining their embedded knowledge to create generic work templates for future projects or revising look-ahead schedules is very difficult, time-consuming and error-prone. The rigid work templates from prior research are also not scalable to cover the inter and intra-class variability in historical schedule activities. This paper aims at fulfilling these needs via a new method to automatically learn construction knowledge from historical project planning and scheduling records and digitize such knowledge in a flexible and generalizable data schema. Specifically, we present Dynamic Process Templates (DPTs) based on a novel vector representation for construction activities where the sequencing knowledge is modeled with generative Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs). Our machine learning models are exhaustively tested and validated on a diverse dataset of 32 schedules obtained from real-world projects. The experimental results show our method is capable of learning planning and sequencing knowledge at high accuracy across different projects. The benefits for automated project planning and scheduling, schedule quality control, and automated generation of project look-aheads are discussed in detail.