Prospective on Automation for Omnichannel Services and the Need for New Robotic Solutions for Store Fulfillment Operations

As businesses offer omnichannel services, such as buy-online-pickup-in-store, more logistical processes need to be conducted within or close to a retail environment. For retailers who adopt a store fulfillment concept, order picking for online orders is conducted inside a store environment and is in addition to the logistic processes required to support in-store customer requests. A store fulfillment approach has the advantage of enabling inventory, labor, infrastructure, and automation to be pooled for online orders, in-store customers, and return processing. Yet, the design and operation of logistical tasks completed in a retail environment is more challenging and requires considering the salient features that vary from a distribution environment. This work provides an overview of omnichannel logistical processes and connects their unique features to open challenges in automating these processes. A benchmarking and classification study describes the state of the practice in 2022 in automated picking solutions. We find that the current market for automated picking solutions that could support a microfulfillment strategy is more mature than solutions that could support a store fulfillment strategy. We identify a set of design and technical requirements for an automated picking solution deployed in a retail environment to support store fulfillment. Moveable robotic piece-level picking solutions need to become more flexible so that they can accommodate different item types, store shelf designs, facility layouts, logistical tasks, and human interactions, as well as more agile so they can robustly operate in uncertain and new environments

A Practical Approach for Picking Items in an Online Shopping Warehouse

Commercially viable automated picking in unstructured environments by a robot arm remains a difficult challenge. The problem of robot grasp planning has long been around but the existing solutions tend to be limited when it comes to deploy them in open-ended realistic scenarios. Practical picking systems are called for that can handle the different properties of the objects to be manipulated, as well as the problems arising from occlusions and constrained accessibility. This paper presents a practical solution to the problem of robot picking in an online shopping warehouse by means of a novel approach that integrates a carefully selected method with a new strategy, the centroid normal approach (CNA), on a cost-effective dual-arm robotic system with two grippers specifically designed for this purpose: a two-finger gripper and a vacuum gripper. Objects identified in the scene point cloud are matched to the grasping techniques and grippers to maximize success. Extensive experimentation provides clues as to what are the reasons for success and failure. We chose as benchmark the scenario proposed by the 2017 Amazon Robotics Challenge, since it represents a realistic description of a retail shopping warehouse case; it includes many challenging constraints, such as a wide variety of different product items with a diversity of properties, which are also presented with restricted visibility and accessibility.This paper describes research conducted at the UJI Robotic Intelligence Laboratory. Support for this laboratory is provided in part by Ministerio de Economía y Competitividad (DPI2015-69041-R, DPI2017-89910-R), by Universitat Jaume I (P1-1B2014-52) and by Generalitat Valenciana (PROMETEO/2020/034). The first author was recipient of an Erasmus Mundus scholarship by the European Commission for the EMARO+ Master Program

Implementation and testing of point cloud based grasping algorithms for objetct picking

Treball de Final de Màster Universitari Erasmus Mundus en Robòtica Avançada. Codi: SJD024. Curs acadèmic 2016-2017The purpose of this study is to investigate the most effective methodologies for the grasping of items in an environment where success, robustness and time of the algorithmic computation and its implementation are a key constraint. The study originates from the Amazon Robotics Challenge 2017 (ARC’17) which addresses the problem of automating the picking process in online shopping warehouses. In a real warehouse environment the robot has to deal with restricted visibility and accessibility. The proposed solution to grasping was to retrieve a final position and orientation of the end effector given only sensory information without mesh reconstruction. Two grippers were used: a two finger gripper with a narrow opening width and a vacuum gripper. Antipodal Grasp Identification and Learning (AGILE) and Height Accumulated Features (HAF) methods were chosen for implementation on a two finger gripper due to their ease of applicability, same type of input, and reportedly high success rate. One major contribution of this work was the creation of the Centroid Normals Approach (CNA) method for the vacuum gripper that chooses the most central point cloud grasp location on the flattest part of the object. Since it does not include calculation of orientation, its computation time is faster than the other approaches. It was concluded that CNA should be used on as many objects as possible with both the vacuum gripper and the two finger gripper. A final scheme has been devised to pick up the maximum number of items by combining algorithms on the two different grippers, given the hardware restrictions, to cater to different objects in the challenge

Decision models for fast-fashion supply and stocking problems in internet fulfillment warehouses

Internet technology is being widely used to transform all aspects of the modern supply chain. Specifically, accelerated product flows and wide spread information sharing across the supply chain have generated new sets of decision problems. This research addresses two such problems. The first focuses on fast fashion supply chains in which inventory and price are managed in real time to maximize retail cycle revenue. The second is concerned with explosive storage policies in Internet Fulfillment Warehouses (IFW). Fashion products are characterized by short product life cycles and market success uncertainty. An unsuccessful product will often require multiple price discounts to clear the inventory. The first topic proposes a switching solution for fast-fashion retailers who have preordered an initial or block inventory, and plan to use channel switching as opposed to multiple discounting steps. The FFS Multi-Channel Switching (MCS) problem then is to monitor real-time demand and store inventory, such that at the optimal period the remaining store inventory is sold at clearance, and the warehouse inventory is switched to the outlet channel. The objective is to maximize the total revenue. With a linear projection of the moving average demand trend, an estimation of the remaining cycle revenue at any time in the cycle is shown to be a concave function of the switching time. Using a set of conditions the objective is further simplified into cases. The Linear Moving Average Trend (LMAT) heuristic then prescribes whether a channel switch should be made in the next period. The LMAT is compared with the optimal policy and the No-Switch and Beta-Switch rules. The LMAT performs very well and the majority of test problems provide a solution within 0.4% of the optimal. This confirms that LMAT can readily and effectively be applied to real time decision making in a FFS. An IFW is a facility built and operated exclusively for online retail, and a key differentiator is the explosive storage policy. Breaking the single stocking location tradition, in an IFW small batches of the same stock keeping unit (SKU) are dispersed across the warehouse. Order fulfillment time performance is then closely related to the storage location decision, that is, for every incoming bulk, what is the specific storage location for each batch. Faster fulfillment is possible when SKUs are clustered such that narrow band picklists can be efficiently generated. Stock location decisions are therefore a function of the demand arrival behavior and correlations with other SKUs. Faster fulfillment is possible when SKUs are clustered such that narrow band picklists can be efficiently generated. Stock location decisions are therefore a function of the demand behavior and correlations with other SKUs. A Joint Item Correlation and Density Oriented (JICDO) Stocking Algorithm is developed and tested. JICDO is formulated to increase the probability that M pick able order items are stocked in a δ band of storage locations. It scans the current inventory dispersion to identify location bands with low SKU density and combines the storage affinity with correlated items. In small problem testing against a MIP formulation and large scale testing in a simulator the JICDO performance is confirmed

Learning-based robotic manipulation for dynamic object handling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronic Engineering at the School of Food and Advanced Technology, Massey University, Turitea Campus, Palmerston North, New Zealand

Figures are re-used in this thesis with permission of their respective publishers or under a Creative Commons licence.Recent trends have shown that the lifecycles and production volumes of modern products are shortening. Consequently, many manufacturers subject to frequent change prefer flexible and reconfigurable production systems. Such schemes are often achieved by means of manual assembly, as conventional automated systems are perceived as lacking flexibility. Production lines that incorporate human workers are particularly common within consumer electronics and small appliances. Artificial intelligence (AI) is a possible avenue to achieve smart robotic automation in this context. In this research it is argued that a robust, autonomous object handling process plays a crucial role in future manufacturing systems that incorporate robotics—key to further closing the gap between manual and fully automated production. Novel object grasping is a difficult task, confounded by many factors including object geometry, weight distribution, friction coefficients and deformation characteristics. Sensing and actuation accuracy can also significantly impact manipulation quality. Another challenge is understanding the relationship between these factors, a specific grasping strategy, the robotic arm and the employed end-effector. Manipulation has been a central research topic within robotics for many years. Some works focus on design, i.e. specifying a gripper-object interface such that the effects of imprecise gripper placement and other confounding control-related factors are mitigated. Many universal robotic gripper designs have been considered, including 3-fingered gripper designs, anthropomorphic grippers, granular jamming end-effectors and underactuated mechanisms. While such approaches have maintained some interest, contemporary works predominantly utilise machine learning in conjunction with imaging technologies and generic force-closure end-effectors. Neural networks that utilise supervised and unsupervised learning schemes with an RGB or RGB-D input make up the bulk of publications within this field. Though many solutions have been studied, automatically generating a robust grasp configuration for objects not known a priori, remains an open-ended problem. An element of this issue relates to a lack of objective performance metrics to quantify the effectiveness of a solution—which has traditionally driven the direction of community focus by highlighting gaps in the state-of-the-art. This research employs monocular vision and deep learning to generate—and select from—a set of hypothesis grasps. A significant portion of this research relates to the process by which a final grasp is selected. Grasp synthesis is achieved by sampling the workspace using convolutional neural networks trained to recognise prospective grasp areas. Each potential pose is evaluated by the proposed method in conjunction with other input modalities—such as load-cells and an alternate perspective. To overcome human bias and build upon traditional metrics, scores are established to objectively quantify the quality of an executed grasp trial. Learning frameworks that aim to maximise for these scores are employed in the selection process to improve performance. The proposed methodology and associated metrics are empirically evaluated. A physical prototype system was constructed, employing a Dobot Magician robotic manipulator, vision enclosure, imaging system, conveyor, sensing unit and control system. Over 4,000 trials were conducted utilising 100 objects. Experimentation showed that robotic manipulation quality could be improved by 10.3% when selecting to optimise for the proposed metrics—quantified by a metric related to translational error. Trials further demonstrated a grasp success rate of 99.3% for known objects and 98.9% for objects for which a priori information is unavailable. For unknown objects, this equated to an improvement of approximately 10% relative to other similar methodologies in literature. A 5.3% reduction in grasp rate was observed when removing the metrics as selection criteria for the prototype system. The system operated at approximately 1 Hz when contemporary hardware was employed. Experimentation demonstrated that selecting a grasp pose based on the proposed metrics improved grasp rates by up to 4.6% for known objects and 2.5% for unknown objects—compared to selecting for grasp rate alone. This project was sponsored by the Richard and Mary Earle Technology Trust, the Ken and Elizabeth Powell Bursary and the Massey University Foundation. Without the financial support provided by these entities, it would not have been possible to construct the physical robotic system used for testing and experimentation. This research adds to the field of robotic manipulation, contributing to topics on grasp-induced error analysis, post-grasp error minimisation, grasp synthesis framework design and general grasp synthesis. Three journal publications and one IEEE Xplore paper have been published as a result of this research

Mobile Manipulation Hackathon: Moving into Real World Applications

The Mobile Manipulation Hackathon was held in late 2018 during the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) to showcase the latest applications of wheeled robotic manipulators. The challenge had an open format, where teams developed an application using simulation tools and integrated it into a robotic platform. This article presents the competition and analyzes the results, with information gathered during the event and from a survey circulated among the finalist teams. We provide an overview of the mobile manipulation field, identify key areas required for further development to facilitate the implementation of mobile manipulators in real applications, and discuss ideas about how to structure future hackathon-style competitions to enhance their impact on the scientific and industrial communities.Peer ReviewedPostprint (published version

Cold storage work and cold protective gloves – a review

The rise of e-commerce and the increasing demand for online grocery shopping and delivery has prompted the growth of warehouse workers subjected to cold temperature working conditions. To ensure food safety and the preservation of inventory, majority of these e-grocery warehouses are cold storage with temperatures from 10°C to -23°C. Exposure to such cold working environments can have effects on the workers comfort, performance and health. As the demand for workers in cold temperature environments increases, it is important to understand how this environment affects the workers and the challenges it may present. This review evaluates how working in cold temperature work affects the human body in both work ability and productivity. It presents the challenges with using cold weather gloves in cold working environments as it affects dexterity, hand grip and causes fatigue. Selection criteria of cold weather gloves and relevant US and international standards are discussed. Difficulties with using thick gloves in cold temperature on complex tasks are reviewed and alternative solutions suggested to improve employee comfort and productivity

Модель функціонування логістичного центру в аеропорту з управлінням ланцюгами поставок

Робота публікується згідно наказу ректора від 29.12.2020 р. №580/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії НАУ" . Керівник проекту: Керівник проекту: к. фіз-мат.н., доцент Коновалюк Валентина СтаніславівнаModern development of the economy is impossible without adequate development of the transport network. It functions as a circulatory system in the human body, delivering passengers and cargo throughout the country and abroad. One of the most important components of the transport system is aviation. It is impossible to create and develop a single market of goods and services, to integrate the Ukrainian economy into the world economic system, to improve the quality of life of Ukrainians, to restore the position of Ukraine as one of the full members of the world community without the proactive development of a comprehensive national transport system. Civil aviation has always been a kind of transport for our country, ensuring access to all its territories and connecting with other countries and continents. Improvement of transport technologies and transport equipment is the main direction of increase of labor productivity at transport and the most important condition of guarantee of safety, environmental and economy of transport processes. The organization of passenger transportation by air involves the organization of such technological processes: regulatory and legal support of transportation; transportation sales; service of passengers and their luggage at the airport (service before departure and after); maintenance of aircraft of the airline; servicing passengers on board an aircraft. The basis of the scheme of research of this influence is the model of traffic of passengers and freight traffic and aircraft fleet (in terms of its technical and economic indicators). In the process of implementation of the model, recommendations for the composition of the park are developed, which corresponds to the studied streams to the greatest extent.Сучасний розвиток економіки неможливий без належного розвитку транспортної мережі. Він функціонує як кровоносна система в організмі людини, доставляючи пасажирів та вантажі по всій країні та за кордон. Однією з найважливіших складових транспортної системи є авіація. Неможливо створити і розвинути єдиний ринок товарів і послуг, інтегрувати українську економіку у світову економічну систему, поліпшити якість життя українців, відновити позиції України як одного з повноправних членів світу громади без активного розвитку всеосяжної національної транспортної системи. Цивільна авіація завжди була своєрідним транспортом для нашої країни, забезпечуючи доступ на всі її території та сполучуючи з іншими країнами та континентами. Удосконалення транспортних технологій та транспортного обладнання є основним напрямком підвищення продуктивності праці на транспорті та найважливішою умовою гарантування безпеки, екології та економії транспортних процесів. Організація пасажирських перевезень повітряним транспортом передбачає організацію таких технологічних процесів: нормативно-правове забезпечення перевезень; транспортні продажі; обслуговування пасажирів та їх багажу в аеропорту (обслуговування до вильоту та після); технічне обслуговування літаків авіакомпанії; обслуговування пасажирів на борту літака. В основі схеми дослідження цього впливу лежить модель перевезень пасажирів та вантажних перевезень та авіаційний парк (з точки зору її техніко-економічних показників). У процесі впровадження моделі розробляються рекомендації щодо композиції парку, що найбільшою мірою відповідає досліджуваним потокам