## 8 Conclusions and Outlook  
This work showed the first steps towards automation in an area where in real world production manual work is still rampant. Also, planning and scheduling of automated tasks in this domain are still performed in large parts by hand, which is time-expensive and may often lead to sub-optimal solutions. Compared to this old-fashioned approach, our work shows how automation and simulation can be carried out in this domain. Moreover, our approach leads to both shorter process times and savings in human working time needed for the planning and scheduling of the process. Another point is that the simulation of the fiber material can also help to avoid superfluous tests and experiments. We plan to evaluate the presented approach (which was presented here at a relatively early stage) on a real world test case at the DLR facilities in Augsburg. This may also help to find mistakes in the planning and scheduling modules. Due to the modular character of the approach, as shown in Figure 7, there is plenty of room for experimentation and extensions in various places. We mention the following directions of future work:  
* Gripper assignment: Here, we demonstrated the assignment only for a special class of grippers and geometries. However, the approach described in Section 3 can be transferred to grippers of a comparable geometry, i.e., single curved and longish grippers. This holds regardless of the gripping method. For handling doubly curved geometries, both with respect to grippers and goal geometries of plies, approaches as described in [21] can be used. In this work, the grippers are also deformable, and not rigid as in the present paper. The integration into the framework presented here poses no problems; in this case, the greatest challenge here will be the modeling in Blender or another engine. * Scheduling: In the approach presented here, the scheduling of actions was determined via PDDL (and an associated solver). PDDL solvers are not guaranteed to find an optimal solution in acceptable time due to state explosion so they exploit (powerful) heuristics instead. Other methods as presented in [13; 33] could be an interesting field for further investigations. A more recent idea is the employment of quantum computing, as was investigated for similar problems, e.g., in [34]. * Parallelization: In the present work, parallel actions were introduced by hand as described in Section 4.2.2. PDDL 2.1 allows also for parallel actions as described in [35]. Using this feature, the modeling in PDDL could be simplified significantly. Also, these ideas can be integrated in the overall workflow without effort. Of course, the use of other PDDL solvers, rendering environments and physics engines, should also be investigated. For PDDL solvers, we refer again to the list from [27]; for simulation, we mention WeBots (see [36]), PyBullet (see [37]), Gazebo (see [38]) and MuJoCo (see [39]). To deal with the control of the high degree of freedom of the robots, one can consider methods as developed, e.g., in [40]. Lastly, the validation of the approach on a real world application should be executed to judge its merits. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14062475/s1](https://www.mdpi.com/article/10.3390/app14062475/s1)]([https://www.mdpi.com/article/10.3390/app14062475/s1](https://www.mdpi.com/article/10.3390/app14062475/s1)), TeamBots Simulation. The first author, [PERSON], carried the main work load of the paper; the second author, [PERSON], offered support in the area of PDDL. All authors have read and agreed to the published version of the manuscript. This work gratefully acknowledges support by the DFG project \"TeamBotS\". The data presented in this study are available on request from the corresponding author due to privacy. Author [PERSON] was employed by the company Franka Robotics.