Safety requirements for symbiotic human–robot collaboration systems in smart factories: a pairwise comparison approach to explore requirements dependencies

Industry 4.0 is expected to deliver significant productivity gain taking advantage of Internet of things (IoT). Smart solutions, enhanced by IoT, are constantly driving revolutionary approaches in multiple domains. Smart factories are one domain where intelligent integrated robotic systems will revolutionize manufacturing, resulting in a complex ecosystem, where humans, robots and machinery are combined. In this setting, human safety requirements are of paramount importance. This paper focuses on symbiotic human–robot collaboration systems (HRC), where human safety requirements are essential. Hence, it aims to explore and prioritize human safety requirement dependencies, as well as their dependencies with other critical requirements of smart factory operation, as effectiveness and performance. Toward this end, the proposed approach is based on SysML to represent the requirements dependencies and pairwise comparisons, a fundamental decision-making method, to quantify the importance of these dependencies. This model-driven approach is used as the primary medium for conveying traceability among human safety requirements as well as traceability from safety requirements to effectiveness and performance requirements in the system model. The analysis is based on the operational requirements identified in the European project HORSE, which aims to develop a methodological/technical framework for easy adaptation of robotic solutions from small-/medium-sized enterprises. Validation of the results is also performed to further elaborate on human safety requirement dependency exploration. The outcomes of this paper may be beneficial for symbiotic HRC systems in the early design stage. As the system is being developed with an emphasis on human safety, all these requirements that have been assessed with highly prioritized dependencies should be taken into account, whereas those with negligible ones have to be ignored since they do not significantly affect the rest of the process. Since operational requirements may be conflicted and incompatible, this approach may be very useful for other systems as well during the system design phase to find the appropriate solution satisfying the majority of the requirements, giving a priority to the ones with highly ranked dependencies and hence facilitating the implementation phase and afterward the production line. The outcomes may be used as a step in developing a model-driven approach which should be able to support the manufacturing process, facilitating the integration of systems and software modeling, which is increasingly important for robotic systems in smart factories incorporating HRC.

The research leading to these results has received funding from the European H2020-FoF-2015 Project “Smart Integrated Robotics System for SMEs Controlled by Internet of Things Based on Dynamic Manufacturing Processes (HORSE)” (680734).

Authors and Affiliations

1. Department of Informatics and Telematics, Harokopio University, Kallithea, Greece

   Georgia Dede, Persefoni Mitropoulou, Mara Nikolaidou, Thomas Kamalakis & Christos Michalakelis

Corresponding author

Correspondence to Georgia Dede.

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Appendix

1.1 Questionnaire

1.1.1 Requirements definition

In this section, Table 1 of Sect. 4.1 is presented to the experts in order to understand the requirements and their description, before proceeding to the questionnaire. Moreover, the SysML diagram without the weights is also given to understand the requirements relationships.

1.1.2 Questionnaire Completion

Table 3 of Sect. 4.4 is also given to the experts in order to understand the nine-level scale and fill in the pairwise comparison matrices.

How to complete the questionnaire

The following questionnaire aims at prioritizing the dependencies between the requirements in order to evaluate their importance. For example, if S-FRQ01 depends on S-FRQ02 and S-FRQ03, we have to evaluate the importance of the dependencies in order to examine whether S-FRQ02 affects more S-FRQ01 than S-FRQ03 or the opposite.

Toward this end, you have to compare the requirements in pairs of two (pairwise comparisons) by allocating a value from the nine-level scale presented in Table 2. Please read carefully Table 3 (nine-level scale) and Table 1 (brief description of requirements) in order to complete the questionnaire.

Making the following pairwise comparisons, please allocate a number from the nine-level scale at each box. You compare the requirement presented in each row with all the other requirements presented in the columns, keeping in mind which requirement has more or less strong dependency and how much to the requirement that they affect.

For example, we know that both S-FRQ02 and S-FRQ03 requirements affect (derive/relate) the requirement S-FRQ01, then if we compare the S-FRQ02 with S-FRQ03 and put in the box the value 3, we mean that S-FRQ02 slightly affects more than the S-FRQ03 the requirement S-FRQ01.

Pairwise comparison for S-FRQ01 Requirement

1.1.3 Questionnaire

1.2 Example of dependency computation

The estimation of dependencies is based on the PWC procedure described in Sect. 4.4. We want to explore and prioritize the dependencies of requirements S-FRQ03, S-FRQ01, S-FRQ02, S-FRQ17 and S-FRQ04. We denote these safety requirements as S_k (1 ≤ k ≤ 5) and the requirements with which are related in terms of relate and derive relationship as R_i. Toward this end, each expert m from a group of M experts fills in the PWC matrices mentioned in the above section of “Appendix” in order to explore the dependencies of each S_k requirement mentioned in the title of the PWC matrices. Each PWC matrix depicts the dependencies of the S_k requirement with the requirements presented in the matrix. Each of the aforementioned PWC matrices, filled in by the mth expert, corresponds to the P^(m) matrix of the PWC process. The estimated weights w ^(m)_i of the matrix (according to Eq. 1) are the dependency of the requirement R_i of the mth expert with the related S_k requirement of each PWC matrix. Then, the average weights w_i for the M experts are estimated, based on Eq. (2). The weights w_i define the weights of dependencies of the requirements R_i with the related S_k.

For example, we consider the first PWC of the questionnaire, namely the matrix depicting the dependencies of the requirement S₁, namely S-FRQ03. We want to find the dependencies with the requirements S-FRQ01, E-FRQ07, E-FRQ13 and E-FRQ16. These requirements are the R_i requirements of the PWC process, where 1 ≤ i ≤ 4. Each expert 1 ≤ m ≤ M fills in this matrix, and hence, we have M PWC matrices for the dependencies of S-FRQ03. We consider each of these matrices as P^(m). For each P^(m), we apply the eigenvalue method and we estimate the weights w ^(m)_i (according to Eq. 1) which is the dependency of the requirement R_i of the mth expert to the S₁. We then estimate the average of these weights, say w_i, based on Eq. (2). The weights w_i are the weights of dependencies of the R_i to the S₁. These are the weights depicted in the SysML diagram (Fig. 4) as well as in Figs. 5, 6 and 7.

The procedure is the same for the exploration and prioritization of the dependencies of the other requirements.

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