A Flexible Robotic Depalletizing System for Supermarket Logistics

Depalletizing robotic systems are commonly deployed to automatize and speed-up parts of logistic processes. Despite this, the necessity to adapt the preexisting logistic processes to the automatic systems often impairs the application of such robotic solutions to small business realities like supermarkets. In this work we propose a robotic depalletizing system designed to be easily integrated into supermarket logistic processes. The system has to schedule, monitor and adapt the depalletizing process considering both on-line perceptual information given by non-invasive sensors and constraints provided by the high-level management system or by a supervising user. We describe the overall system discussing two case studies in the context of a supermarket logistic process. We show how the proposed system can manage multiple depalletizing strategies and multiple logistic requests

A Reconfigurable Gripper for Robotic Autonomous Depalletizing in Supermarket Logistics

Automatic depalletizing is becoming a practice widely applied in warehouses to automatize and speed-up logistics. On the other hand, the necessity to adapt the preexisting logistic lines to a custom automatic system can be a limit for the application of robotic solutions into smaller facilities like supermarkets. In this work, we tackle this issue by proposing a flexible and adaptive gripper for robotic depalletizing. The gripper is designed to be assembled on the end-tip of an industrial robotic arm. A novel patent-pending mechanism allows grasping boxes and products from both the upper and the lateral side enabling the depalletizing of boxes with complex shape. Moreover, the gripper is reconfigurable with five actuated degrees of freedom, that are automatically controlled using the embedded sensors to adapt grasping to different shapes and weights

Toward Future Automatic Warehouses: An Autonomous Depalletizing System Based on Mobile Manipulation and 3D Perception

This paper presents a mobile manipulation platform designed for autonomous depalletizing tasks. The proposed solution integrates machine vision, control and mechanical components to increase flexibility and ease of deployment in industrial environments such as warehouses. A collaborative robot mounted on a mobile base is proposed, equipped with a simple manipulation tool and a 3D in-hand vision system that detects parcel boxes on a pallet, and that pulls them one by one on the mobile base for transportation. The robot setup allows to avoid the cumbersome implementation of pick-and-place operations, since it does not require lifting the boxes. The 3D vision system is used to provide an initial estimation of the pose of the boxes on the top layer of the pallet, and to accurately detect the separation between the boxes for manipulation. Force measurement provided by the robot together with admittance control are exploited to verify the correct execution of the manipulation task. The proposed system was implemented and tested in a simplified laboratory scenario and the results of experimental trials are reported

RGB-D Recognition and Localization of Cases for Robotic Depalletizing in Supermarkets

Integrating a robotic system into the depalletizing process of a supermarket demands a high level of autonomy, based on strong perceptive capabilities. This letter presents a system for detection, recognition, and localization of heterogeneous cases in a depalletizing robotic cell, using a single RGB-D camera. Such a system integrates apriori information on the content of the pallet with data from the RGB-D camera, exploiting a sequence of 2D and 3D model-based computer-vision algorithms. The effectiveness of the proposed methodology is assessed in an experiment where multiple cases and pallet configurations are considered. Finally, a complete depalletizing process is shown

Towards Packaging Unit Detection for Automated Palletizing Tasks

For various automated palletizing tasks, the detection of packaging units is a crucial step preceding the actual handling of the packaging units by an industrial robot. We propose an approach to this challenging problem that is fully trained on synthetically generated data and can be robustly applied to arbitrary real world packaging units without further training or setup effort. The proposed approach is able to handle sparse and low quality sensor data, can exploit prior knowledge if available and generalizes well to a wide range of products and application scenarios. To demonstrate the practical use of our approach, we conduct an extensive evaluation on real-world data with a wide range of different retail products. Further, we integrated our approach in a lab demonstrator and a commercial solution will be marketed through an industrial partner

Increasing the Energy-Efficiency in Vacuum-Based Package Handling Using Deep Q-Learning

Billions of packages are automatically handled in warehouses every year. The gripping systems are, however, most often oversized in order to cover a large range of different carton types, package masses, and robot motions. In addition, a targeted optimization of the process parameters with the aim of reducing the oversizing requires prior knowledge, personnel resources, and experience. This paper investigates whether the energy-efficiency in vacuum-based package handling can be increased without the need for prior knowledge of optimal process parameters. The core method comprises the variation of the input pressure for the vacuum ejector, compliant to the robot trajectory and the resulting inertial forces at the gripper-object-interface. The control mechanism is trained by applying reinforcement learning with a deep Q-agent. In the proposed use case, the energy-efficiency can be increased by up to 70% within a few hours of learning. It is also demonstrated that the generalization capability with regard to multiple different robot trajectories is achievable. In the future, the industrial applicability can be enhanced by deployment of the deep Q-agent in a decentral system, to collect data from different pick and place processes and enable a generalizable and scalable solution for energy-efficient vacuum-based handling in warehouse automation

Industrial, Collaborative and Mobile Robotics in Latin America: Review of Mechatronic Technologies for Advanced Automation

Mechatronics and Robotics (MaR) have recently gained importance in product development and manufacturing settings and applications. Therefore, the Center for Space Emerging Technologies (C-SET) has managed an international multi-disciplinary study to present, historically, the first Latin American general review of industrial, collaborative, and mobile robotics, with the support of North American and European researchers and institutions. The methodology is developed by considering literature extracted from Scopus, Web of Science, and Aerospace Research Central and adding reports written by companies and government organizations. This describes the state-of-the-art of MaR until the year 2023 in the 3 Sub-Regions: North America, Central America, and South America, having achieved important results related to the academy, industry, government, and entrepreneurship; thus, the statistics shown in this manuscript are unique. Also, this article explores the potential for further work and advantages described by robotic companies such as ABB, KUKA, and Mecademic and the use of the Robot Operating System (ROS) in order to promote research, development, and innovation. In addition, the integration with industry 4.0 and digital manufacturing, architecture and construction, aerospace, smart agriculture, artificial intelligence, and computational social science (human-robot interaction) is analyzed to show the promising features of these growing tech areas, considering the improvements to increase production, manufacturing, and education in the Region. Finally, regarding the information presented, Latin America is considered an important location for investments to increase production and product development, taking into account the further proposal for the creation of the LATAM Consortium for Advanced Robotics and Mechatronics, which could support and work on roboethics and education/R+D+I law and regulations in the Region. Doi: 10.28991/ESJ-2023-07-04-025 Full Text: PD

Uudelleenkonfiguroitavat Teollisuuden Robottijärjestelmät

Teollisissa robottisoluissa seuraavana kehitysaskeleena on toimintojen lisääminen aina kehittyvien tuotantovaatimuksien kohtaamiseksi. Kuitenkin uusien laitteiden lisääminen teolliseen robottisoluun ei ole aina mahdollista, tai edes kannattavaa. Tähän ongelmaan on esitetty monia eri ratkaisuita, jotka hyödyntävät uudelleenkonfiguroitavuutta robottisolun eri tasoilla. Näitä ratkaisuita ja niiden uudelleenkonfiguroitavuuden toimivuutta on kartoitettu tässä työssä. Tämä työ on suoritettu kirjallisuustutkimuksena, jonka keskeisinä tutkimuskysymyksinä olivat: Miten uudelleenkonfiguroitavuutta hyödynnetään robottisoluissa, miten uudelleenkonfiguroitavia robottisoluja hyödynnetään käytännössä ja mitä etuja ja haittoja uudelleenkonfiguroitavissa robottisoluissa esiintyy verrattuna perinteiseen robotiikkaan. Työssä keskityttiin robottisolujen osissa ja kokonaisuuksissa esiintyvän uudelleenkonfiguroitavuuden mekaanisiin toteutuksiin. Ensimmäisenä työssä yhdistetään uudelleenkonfiguroitavien tuotantojärjestelmien toimintaperiaatteet robottisolujen rakenteeseen ja toimintaan. Uudelleenkonfiguroitavuus jakautuu kahteen kriittiseen kategoriaan, modulaarisuus ja integroitavuus, sekä neljään tukevaan kateoriaan, jotka ovat: kustomoitavuus, skaalautuvuus, muunneltavuus ja diagnosoitavuus. Tätä jakoa hyödyntäen analysoitiin robottisolujen ja niiden osien prototyypeistä niissä esiintyvän uudelleenkonfiguroitavuuden tasoa ja ratkaisuiden toimivuutta. Lisäksi jokaisessa prototyypissä otettiin huomioon myös sen käyttö teollisuudessa. Tutkimuksessa huomattiin, että uudelleenkonfiguroitavien robottisolujen järjestelmiin on kehitetty monia ratkaisuita, joilla saadaan merkittävää hyötyä irti robottisolun toiminnan muokkaamisella. Lisäksi tutkimuksessa huomattiin, että vain pientä osaa näistä eri ratkaisuista oli kokeiltu teollisessa ympäristössä, joka todennäköisesti aiheutuu uudelleenkonfiguroitavien järjestelmien teknisen monimutkaisuuden aiheuttamasta suuremmasta hinnasta