## 4 Discussion
This approach is in contrast to our previous work in [17], where we used the model trained when only the LED was on to perform also the classification when the ambient light of the fluorescent tubes was present. Whilst for the application in [17] this approach was applicable, in this work, dealing with a completely different movement type and a largely increased complexity of the classification (foil, size of foil and speed of the train), it is not practicable without further advancements. In future work, we will investigate this issue to devise methods that can mitigate the effect of ambient light in the calculated features in order to be able to have a single trained model, regardless of whether ambient light is present or not. One of the limitations of our approach, utilizing a Lego train moving on given rails, is that the movement direction is limited to either Forward or Backward. To a certain extent, this movement on a given path (rails) can be compared to current real-world applications, where also autonomous mobile platforms in factories or warehouses do move on given paths, thus limiting the movement possibilities. For enabling also varying paths through the detection area, we will investigate an extension of our utilized system to incorporate multiple receivers, in order to overcome the limitations of the number of movement directions. Still, we would like to point out that in settings such as high rack warehouses or corridors, the possible movement directions are also quite limited. In the process of movement direction determination, especially in the algorithm that determines the positions of the two flanks, we had to define the parameter for building the difference between the samples. Of course, a lower or higher speed of the train would lead to the necessity of adjusting this parameter in order to perform the flank position determination. In this work, we set this parameter to 50 since this value renders good results for all the applied speed settings. As already described, an increased or decreased speed of the moving object without the adjustment of this parameter would lead to incorrect flank determination and consequently to false movement direction determination. The same issue consequently also rises with respect to the feature generation process. The utilized speed settings in this work are nevertheless very well comparable to the maximum speeds of mobile robots used in factory settings or healthcare and therefore, give reason to argue that the envisioned applications of identification, speed estimation and movement direction determination of such devices are realizable on the base of the presented approach. Our utilized hardware, where the light source and the receiving RGB photodiode are placed in close vicinity to each other, on the one hand, has the big advantage that this approach allows for integrating the light source and the receiving element into one compact module. In comparison to systems where the light source and the receiving elements are separated from each other, we can argue that this approach requires lower installation effort. On the other hand, this integration of the light source and photosensitive device in the same module also imposes some requirements regarding the applicable reflecting materials. In an application where the light source and the receiving element are placed further away from each other, it is clear that our retroreflective foils and the mirror element in the current setup (horizontally aligned) are not generally applicable. In such applications, it is necessary to investigate geometric models in combination with other reflecting materials as given in [26], where the authors showed the localization of a toy car by the means of visible light by placing reflective materials (mirrors or aluminum) in a certain geometric shape on the toy car. The same is also true for a largely parallel alignment of the light source and the surface on which the foils are mounted.