## 5 Discussion and Conclusions  
The main results of this work were the description of the generic industrial scenario with its instances IGIS, the formulation of the generic planning problem and of one instance, the custom grippers IGPP, and the validation results for the selected IGPP. The results showed that the GIS derived from independent scenarios described in related works could be easily configured for a new scenario, i.e., the custom grippers scenario. Furthermore, the generic planning problem originally formulated for the GIS could also be easily adapted for the custom grippers IGPP. For the PDDL formulation of the IGPP, only changes in the naming of the PDDL artefacts and in the problem files were required. No further modelling was necessary. Using the validation methods presented in Section 3.3, we showed that the PDDL formulation of the main IGPP could guarantee a success rate of 90% for generating plans for a high number of situational IGPs. These results demonstrate that AI planning approaches (e.g., temporal planning) formulated in PDDL can be used in real-world IGIS. Automated planning approaches are already deployed as a high-level control strategy in simulated or real industrial scenarios [1; 4; 44]; however, no generic planning problems and corresponding validation methodologies for them have yet been developed. Automated planners for planning problems formulated in PDDL are usually compared one to another based on a set of standard planning domains and problems ([[https://ipc2018-classical.bitbucket.io/domains.html](https://ipc2018-classical.bitbucket.io/domains.html)]([https://ipc2018-classical.bitbucket.io/domains.html](https://ipc2018-classical.bitbucket.io/domains.html)) (accessed on 17 February 2022)). None of these planning problems is formulated for a generic industrial scenario. Some studies from the literature present planning problems, but only for specific scenarios [1; 45]. Therefore, no suggestions regarding a suitable temporal planner are available in the literature that can solve planning problems formulated for such scenarios. Further results of our work showed that the temporal planners optic and tfd were suitable temporal planners for the  
Figure 10: Normalized means of plans’ makespans and numbers of actions for the four planners and different \(n_{ps}\) values. IGPP formulation of the gripper scenario, when plans must be generated within a deadline of 10 s and the planning problems have \(n_{ps}\in\{4,6,8\}\) process steps. For planning problems with \(n_{ps}>8\), no concrete statements can be formulated. The work conducted so far shows the theoretical advantage of deploying automated task planning methodologies based on the GIS and the PDDL formulation of the GPP as high-level control approaches for industrial scenarios with mobile manipulators. In future work, we wish to transfer these results to simulated and real scenarios, by executing the plans generated for the different situational IGPPs. A further validation methodology with corresponding Qols will be defined. This will focus on the execution times and on the re-plan rates. In conclusion, this work introduced a generic industrial scenario, a generic planning problem formulated in PDDL, and a validation methodology for instances of the GPP. These models and approaches can be used as a starting point for deploying automated planning approaches in any further industrial scenario with mobile manipulators. Conceptualization, S.-O.B. ; methodology, S.-O.B. ; software, S.-O.B. ; validation, S.-O.B. ; formal analysis, S.-O.B. ; investigation, S.-O.B. ; resources, S.-O.B. ; data curation, S.-O.B. ; writing--original draft preparation, S.-O.B. ; writing--review and editing, S.-O.B. and B.C. ; visualization, S.-O.B. ; supervision, B.C. ; project administration, B.C. ; funding acquisition, B.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 820807 (Sharework). Not applicable.