## 4 Conclusions  
We were able to design an algorithm that will allow the detection of dynamic objects based on 2D environmental measurements. The algorithm was developed to detect people who are in the mapped environment during SLAM execution. The algorithm was not bound to the used SLAM method but assumed the input data from the measurement and the position of the performed measurement. The proposed approach was based on the SegMach algorithm, which we used to find matching and nonmatching segments. We subsequently searched for mismatched segments in the previous measurement. Based on this procedure, we were able to identify dynamic objects in the mapped environment. One of the main problems with the occurrence of dynamic objects is the occurrence of defects in the resulting map. This was solved by a function that creates a map and is directly implemented in Matlab. Another problem is a possible collision of the MRS with the dynamic object. In our case, we proposed to solve this by calculating the distance of the dynamic object from our planned trajectory. In the case of approaching a predetermined critical distance, a new trajectory was calculated, taking into account the dynamic objects in the map. The advantage of this algorithm can be considered as its time response and the possibility of use in relatively busy areas. The average response of the algorithm was 0.3 s. The human walking speed is around 4.8 km/h, and the speed of the MRS used was 2 km/h; based on these data, we can theoretically state that the algorithm works in real-time and can react in advance to a dynamic object. If the algorithm fails to respond, the MRS system contains built-in security functions that work independently of our calculations. The time response can be further improved by using a more powerful computing unit. The main disadvantage of the algorithm was the inability to detect an object that is located near a wall or some larger (compared to a human) stationary object. From the point of view of ASLAM, we did not have to consider this too important, as when performing ASLAM, we usually tried to walk through the middle of the room or from a safe distance from obstacles. This, of course, depends on the path planning algorithm used and its settings. The second problem mentioned was the formation of randomly reflected light rays from the glass surface. However, this problem occurs with most LiDARs and can be eliminated by tuning the echo reflectance. Based on the mentioned disadvantages, we did not use the percentage of success of the algorithm, as it could be misleading. We consider the  
Figure 19: Flowchart for detecting dynamic objects during ASLAM execution.  
proposed method for the detection of dynamic objects to be the main scientific contribution of this work. This method can be used when conducting autonomous surveys or AGVs deployed in warehouses. One of the other directions of this research will probably be the deployment of several MRSs. These MRSs will be represented as a multi-agent system using ITS (Intelligent Transport System) architectures as described by the author in [26]. The goal will be to obtain the most comprehensive view of the environment.  
Conceptualization, M.M.; methodology, M.M.; software, M.M.; validation, P.V.; formal analysis, D.N.; investigation, P.H.; resources, B.M.; data curation, M.H.; writing--original draft preparation, M.H.; writing--review and editing, J.M.; visualization, M.H.; supervision, P.H.; project administration, D.N.; funding acquisition, P.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by VEGA 1/0241/22 Mobile robotic systems for support during crisis situations.  
The authors declare no conflict of interest.