HRC in Production

Massive new changes in industrial production are expected to be witnessed as new requirements are proposed by Industry 4.0. These new requirements are expressed as the union of "lot-size one" (a product in this form is only produced in one piece, i.e. exactly once) and "mass customization" (each individual product in a large series is strongly adapted to the needs of a single user). The requirements for production automation have been changing in many industries for some time. The reason for this is the increasing variety of products to be manufactured and the ever-shorter product life cycles. In many cases, classic mass production in the true sense no longer takes place. In principle, the new requirements can be met in productions that essentially rely on human labor, mostly with lower quality than machines. Also and the strong production machinery adapted to the industrial products with relatively rigid processes is actually only for mass production. This poses a considerable challenge for conventional automation technology. In high-wage countries and in the face of demographic change, however, one is dependent on the increase in productivity that can be achieved through automation in order to be able to produce competitively. Based on the fact that the production processes that can be automated with conventional techniques are already automated, it is now necessary to promote automation to the next step to reach the required manual dexterity and intelligence that humans can achieve to date.

Such a strategy naturally leads to the concept of human-robot collaboration (HRC) which requires that the human labor and robots are not only coexisting in a common workspace but also accomplish the same industrial production task. This is based on a naive assumption that machines will not be able to completely replace humans in the foreseeable future, which is why seamless cooperation between humans and machines is of outstanding importance. Through cooperation, humans and robots can coordinate their actions and thus act as an effective team. Such an ability requires decision-making competence on the part of the robot, which enables a cooperative choice of action within a group of several humans and robots. The theoretical and conceptual foundation of HRC is built upon human-robot interaction (HRI) which has received much attention from the academic world in recent years, with promising partial concepts developed. However, a closed overall concept for the close cooperation between humans and robots in industrial production scenarios does not yet exist. The main challenges in this context arise from the information uncertainty, which can hardly be avoided in real scenarios, as well as the rapidly increasing problem complexity. This information uncertainty arises from the fact that the robot system cannot exactly know the exact state of the world, for example, the spatial position of task-relevant objects. Responsible for this is, on the one hand, residual errors in the evaluation of the sensors and, on the other hand, the fact that the activities of the human team members in particular cannot be fully observed. On the other hand, considerable previous knowledge is available, especially in the industrial assembly scenarios considered in this project. Both the properties and function of the objects relevant to the task and the actual assembly process in all details are known from the outset because they are recorded in the corresponding engineering systems as part of product development anyway. This previous knowledge should be used both when planning an assembly task and when monitoring the execution of the task by the human-robot team, for example, to interpret human actions and assign them to given elementary actions.

From this perspective, this project is actually aiming at providing a breaking new technical and scientific ground for the theoretical development and practical applications of HRC in industrial production. With this project, Siemens and the chair for control and regulation technology (LSR) at the Technical University of Munich are planning a cooperation to investigate the close human-robot collaboration in industrial production systems. 

Period: 2015.10 - 2017.09

Role: Participant as a doctoral candidate

Principal Investigator: Prof. Dr.-Ing./Univ. Tokyo Martin Buss, PD Dr.-Ing. habil. Dirk Wollherr, Dr.-Ing. Georg von Wichert

Affiliation: Technical University of Munich (TUM)

Funding Source: Siemens AD-Campus project

Website: http://www.campus-ad.de/forschung/human-robot-collaboration-in-production/

Consortium: Technical University of Munich (TUM) and Siemens AG.

The main objectives of the HRC project include:

• Literature review and survey on human-robot collaboration in industrial production.

• Modeling of collaborative assembly processes, including the description of assembly processes in human-robot collaboration, the cost functions for optimized scheduling, the description language for elementary skills, and the design of combined human-robot systems.

• Allocation of an overall task to human-robot teams, including the definition of elementary skills and the scheduling and assignment of the tasks.

• Coordinated execution of assembly sequences, including social parameters and boundary conditions in cooperative manipulation, the interpretation of movement patterns, readable movement patterns for robots, and action coordination by interpreting and executing legible movements.

• Execution and supervision of collaborative assembly operations, including progress monitoring and error detection and handling.

• Prototype realization of the developed concepts and processes on a suitable robot platform in the Siemens laboratories.

Publications

• Z. Zhang*, M. Leibold, and D. Wollherr, "Integral Sliding-Mode Observer-Based Disturbance Estimation for Euler–Lagrangian Systems," in IEEE Transactions on Control Systems Technology, vol. 28, no. 6, pp. 2377-2389, Nov. 2020, doi: 10.1109/TCST.2019.2945904. [IEEEXplore]

• Y. Sun*, Z. Zhang, M. Leibold, R. Hayat, D. Wollherr, and M. Buss. "Protective Control For Robot Manipulator By Sliding Mode Based Disturbance Reconstruction Approach." 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). Munich, Germany, 03-07 July 2017.

Pending Patent

• Zengjie Zhang, Sensor-less Collision Detector for Robot Manipulators, Technical University of Munich, Siemens AG, 2018-07E03 MR.