from one location to another. Today, robots are essential in this field. The European sector's share reaches 23 %, yet Europe must still compete with China, Japan, and Korea. Improved efficiency and innovative solutions would be an advantage. However, the transportation of deformable objects such as plastic items and food still cannot be handled by robots. The EUfunded AGILEHAND project will develop innovative technologies that will allow robots to smartly handle and package soft and deformable objects in agile and reconfigurable lines, while intelligence solutions will monitor, control, and synchronize production and logistics for improved efficiency. AGILEHAND's solutions will be demonstrated in four industrial pilots. The work presented here is focused on the testing procedures used for validating and assessing the basics of AGILEHAND solutions functionality and increasing the userfriendliness.
The conveyor system market and, in particular, robot segment for the automated material
handling are experiencing double-digit growth at a compound annual growth rate (CAGR). EU
manufacturers in this sector play a significant role, covering 23% of the market but this technological
edge is being challenged due to the astonishing growth of China, Japan, and Korea [
In this context, EU funded project AGILEHAND [
AGILEHAND project will deploy 3 integrated Suites: 1) Smart Sensing Suite, self-calibrating sensing solutions to grade the quality (both interior and exterior) of delicate objects and to produce a mesh of integrated and overlapping sensors that will improve production-line traceability, agility and reconfigurability; 2) Self-Adaptive Handling, Sorting And Packaging Suite, robotic manipulation systems that reacts to product quality and that can Pick-Up and Re-Orientated Different Soft and
Deformable Products without causing product damage considering collaborative (human-in-themiddle) approaches; 3) Agile, Flexible And Rapid Reconfigurable Suite, a set of AI based solutions that will allow for monitoring, adaptive control and synchronization of production and logistics flows in a factory, even when faced with a variability of products, production mix or fresh market, guaranteeing high performance in customer response time, and an efficient use of resources.
The AGILEHAND Solutions will be demonstrated in 4 industrial pilots that differ in characteristics of the surface, deformability, and consistency of the products to be handled.
The work presented here is focused on the testing procedures used for validating and assessing the basics of AGILEHAND solutions functionality and increase user-friendliness. Regarding the software mock-ups, these are developed through service virtualization, API mocks and simulators. These tools will allow the creation of user interfaces that show the end-user what the software will look like without having to build the software or the underlying functionality. These software UI (User Interface) mockups are semi functional user interfaces developed in a software development tool. Usage models and mock-ups are used by AGILEHAND solutions designers mainly to acquire feedback from users.
This section focusses on describing the tool and methodology used for designing the UI component
of AGILHAND platform, described previously. Designing good user interfaces is often a challenging
process which comprises consideration of several factors such as good functional and systematic
requirement analysis. To cover all aspects of the design process a fully functional tool is a necessity
which can support all kinds of design process needs. One such tool that supports a wide variety of
such functionalities and is openly accepted in enterprise practices is a cloud-based design tool called
Figma [
Usability testing is a systematic evaluation of ease of use and user friendliness in a product or
system which is usually performed through direct participation from stakeholders and end-users by
providing relevant observation and feedback on the experience [
Agilehand partners were invited to participate in the usability test of common platform. The usability test gave us insights into many useful findings. Leveraging the Metrics provided by System Usability Scale gave useful information to understand user behaviors. The feedback provided by participants was analyzed, focusing on metrics such as completion time, completion rate and user ratings. The following Figure 4 is a click heatmap of home page, giving insights on what parts of the page users expect to be interactive and should give users visual feedback when they click on it.
In conclusion, the usability testing revealed that overall proposed design has good user experience while there is room for improvement in parts of the design which users found unreachable or not interactable. In perspective of the future work, more research could be carried out on alternative navigation patterns or input methods for higher user intractability with the design components to enhance usability. Additionally, further rounds of usability testing could also be carried out with diverse user groups which could provide valuable insights.
This work highlights the results of AGILEHAND project that is directly relevant to the objectives of the workshop which aims to integrate innovative technologies such as AI, IoT, and data driven approaches in manufacturing. The workshop's primary objective is to foster knowledge sharing and collaboration towards creating efficient, resilient, and intelligent manufacturing systems. The proposed paper contributes to this goal, by developing a testing procedure to be used for validating and assessing the basics of AGILEHAND solutions functionality and increasing user-friendliness. Such solutions focus on smart handling technologies that improve efficiency and potentially resilience and intelligence in manufacturing and logistics operations. Moreover, both the workshop and the AGILEHAND project highlight the importance of cross-sectoral collaboration for advancing Industry 4.0. The proposed paper focuses on demonstrating the usage of AGILEHAND unified UI platform in four industrial pilots.
The authors acknowledge the European Commission for the support and funding under the scope of Horizon Europe AGILEHAND Project (Grant Agreement Number 101092043) and the partners of the AGILEHAND Project Consortium.
The author(s) have not employed any Generative AI tools.