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Today, many industrial tasks are not automated and still require human intervention. One of these tasks is the unloading of oversea containers. After the end of transportation to the sorting center, the containers must be unloaded manually for further sending the parcels to the recipients. A robot-based automatic unloading of containers was therefore researched. However, the promising results of the system developed in these projects could not be commercialized due to problems with its reliability. Mechanical, algorithmic or other limitations are possible causes of the observed errors. To analyze errors, it is necessary to evaluate the results of the robot’s work without complicating the existing system by adding new sensors to it. This paper presents a reference system based on machine learning to evaluate the robotics grasps of parcels. It analyzes two states of the container: before and after picking up one box. The states are represented as a point cloud received from a laser scanner. The proposed system evaluates the success of transferring a box from an overseas container to the sorting line by supervised learning using convolutional neural networks (CNN) and manual labeling of the data. The process of obtaining a working model using a hyperband model search with a maximum classification error of 3.9 % is also described.
Reacting to ever-changing business environments, in the last decade complex systems of systems accomplished giant leaps forward leading to great technological flexibility. However, this dimension of flexibility is often limited by the rigidity of super-ordinated planning systems. Especially when hybrid teams of automated and human resources are in place, the dynamic assignment of tasks taking into account ergonomics remains a challenge. After exposing a gap in the state of the art on the topic, this paper presents an approach to include ergonomics in dynamic resource allocation models. Combining and complementing existing approaches, the presented method monitors the actual ergonomic burden of the resources during a shift and it provides a linear optimization model to steer the resource allocation process.
The high system flexibility necessary for the full automation of complex and unstructured tasks leads to increased technological complexity, thus to higher costs and lower performance. In this paper, after an introduction to the different dimensions of flexibility, a method for flexible modular configuration and evaluation of systems of systems is introduced. The method starts from process requirements and, considering factors such as feasibility, development costs, market potential and effective impact on the current processes, enables the evaluation of a flexible systems of systems equipped with the needed functionalities before its actual development. This allows setting the focus on those aspects of flexibility that add market value to the system, thus promoting the efficient development of systems addressed to interested customers in intralogistics. An example of application of the method is given and discussed.
The high system flexibility necessary for the full automation of complex and unstructured tasks leads to increased complexity, thus higher costs. On the other hand, the effectiveness and performance of such systems decrease, explaining the unfulfilled potential of robotcs in sectors such as intralogistics, where the benefits of a robotic solution rarely justify its costs. Taking the distance from the false idea that a task should be either fully automated, or fully manual, this aper presents a method for design of a lean human-robot interaction (HRI) withe the objective of the "right level of automation", where functions are divided among human and automated agends, so that the overall process gains in performances and/or costs. ... The 10 progressive steps of the method are presented and discussed with reference to their graphical tool: the House of Quality Interaction.
The success of an autonomous robotic system is influenced by several interdependent factors not easily identifiable. This paper is set to lay the foundation of a new integrated approach in order to deeply examine all the parameters and understand their contribution to success. After introducing the problem, two cutting edge autonomous systems for the process of unloading of containers will be presented. Then the STIC analysis, a recently developed method for modelling and interpreting all the parameters, will be introduced. The preliminary results of applying such a methodology to a first study case, based on one of the two systems available to the authors, will be shortly presented. Future research is in the end recommended in order to prove that this methodology is the only way to efficiently and effectively mitigate the risk that stops potential users from investing in autonomous systems in the logistics sector.
In this paper it is first identified the trade-off among costs, flexibility and performances of autonomous robotic solutions for material handling processes, where adding value with automation is not as trivial as in production processes: hence the requirement for automated solutions to be simple, lean and efficient becomes even stricter. Then a method for modelling and comparing differential performances and costs of manual and autonomous solutions is developed. As a result of the method, a smart man-machine collaborative interface is designed and its impact evaluated on a specific case of study. Results are then generalized and prove the strong conclusions that in unconstrained environments, where full standardization cannot be achieved, the risk of investing in autonomous solutions can only be mitigated by creating a fast and smart man-machine collaborative interface.