# On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps
## 6 Experimental Results
On the other hand, for static new obstacles in the map, the underlying SLAM (Algorithm 2) algorithm estimates smaller positional uncertainty and therefore a larger decay time, ensuring its permanence in the map. Thus, uncertainty integration has two merits in the information sharing scheme. First, it acts a filter for wrong map updates corresponding to the dynamic obstacles in the environment through a quick confidence decay. Second, it ensures that only the correct information is shared with other robots corresponding to the new static obstacles. It should be noted that the dynamic detection of

\[S\to n_{2}\to n_{5}\to n_{6}\to n_{7}\]1 Estimation of confidence decay curve for different values of positional uncertainty with \(C_{th}\) = 0.45 and \(t_{z}\) = 1200 s. (**a**) Ufactor = 35%. (**b**) Ufactor = 45%. (**c**) Ufactor = 55%. (**d**) Ufactor = 65%. It can be seen that for higher uncertainties, the curve decays faster as desired. moving people can be done using image processing for camera-based sensors [51], RGBD sensors [52] or leg detector for Lidar-based sensors [53] and integration of such approaches [54] will increase the robustness of the system. ## 7 Conclusions

Information sharing is a powerful technique which has many potential benefits in path planning of multi-robot systems. A node-map was proposed in our previous work to solve the problems of information sharing in different robots. We extended our previous work by integrating the positional uncertainty of the new obstacles in the confidence decay mechanism which models the transience of the obstacles. This minimizes false map updates and notifications in the system. New experiments were performed to share information about new obstacles in heterogeneous maps. The results shown that using the nodemap allows the robots to smoothly share the information. Moreover, since path planning is also done using the nodemap, efficient trajectories considering the position of new obstacles can be done in real-time. The information sharing mechanism allows the robots to obtain timely information about remote obstacles in the map without having to explicitly visit those areas. In addition, new experiments were performed to test the proposed mechanism in complex environments with moving people in the vicinity of the robots. Due to the tight coupling of uncertainty and decay mechanism, the dynamic obstacles could be filtered and avoided false update of the map. Even if there is some false update, the confidence corresponding to them decays fast due to larger uncertainty. Experiment results confirm that, in the long run in large environments employing multiple robots, the proposed method can improve the efficiency of the system in terms of shorter distance traveled by the robots and shorter planning time by eliminating path re-planning. In future, we will continue to test the

\[S\to n_{2}\to n_{5}\to n_{6}\to n_{7}\]2 Dynamic obstacle experiment with P3 DX navigation from position S to G in \[S\to n_{2}\to n_{5}\to n_{6}\to n_{7}\]3 (**a-j**) Person moved randomly in front of P3 DX for a long time moving in and out of the range of sensors. P3 DX changed trajectories or stopped if the obstacle was dangerously close. (_Supplementary Materials_)

\[S\to n_{2}\to n_{5}\to n_{6}\to n_{7}\]4 Dynamic obstacle experiment with P3 DX navigation from position G to S in \[S\to n_{2}\to n_{5}\to n_{6}\to n_{7}\]5 (**a-j**) To further test the method under pressure, a person moved randomly near P3 DX. (_Supplementary Materials_)

robustness of the proposed method in more complex and realistic environments such as cafeterias and offices. The supplementary materials are available online at [[http://www.mdpi.com/2076-3417/9/13/2753/s1](http://www.mdpi.com/2076-3417/9/13/2753/s1)]([http://www.mdpi.com/2076-3417/9/13/2753/s1](http://www.mdpi.com/2076-3417/9/13/2753/s1)). [PERSON] and [PERSON] conceived the idea, designed, performed experiments, and summarized the research; [PERSON] made valuable suggestions to analyze the data and improve the manuscript.