*Related Works*

There is a plethora of previous works related to sharing information in multi-robot systems. Sharing corresponding matches of an object by two robots to calculate an accurate relative localization over time is proposed in Reference [2]. Work in Reference [3] proposes sharing visual information. In Reference [4], task negotiation between multiple robots by sharing information is proposed to decide the sequence in which the tasks should be performed by different robots. Work in Reference [5,6] proposes a protocol to share the region of interest between robots for efficient task cooperation. In-fact, multi-robot sport activities like Robo-soccer [7,8] heavily relies on meaningful information sharing

between robots to achieve a common goal. Virtual pheromones have been proposed to be used for coordinating master-slave robots in References [9,10]. Path planning of multiple robots using information from external security cameras is proposed in Reference [11]. In addition, a direct obstacle coordinate information sharing was proposed in our previous work [12] without considering the uncertainty. However, this is a limitation as in practical systems there is always some uncertainty associated with robot's localized information and mapped obstacle's position due to sensor errors. RoboEarth [13–15] is another platform which heavily uses information exchange through cloud.

Such information sharing has huge merits in robot path planning. Path planning is an active area of research and in the context of multi-robot systems path-planning has shown promising advantages through information sharing between robots. Multi-robot collision avoidance has been discussed in Reference [16]. Work in Reference [17] presents a mechanism in which robots share information about their remaining battery power and accordingly avoid collision by giving priority to a robot with less battery power over the shortest path. An interesting approach of collaborative navigation through visual-servoing is presented in Reference [18,19] which heavily relies on reliable and efficient inter-robot communication to share information. The proposed work focuses on multiple robots sharing information about the dynamic changes in the remote area of the environment. This enables the robots to use updated and timely information to efficiently plan their paths. Information sharing among multiple robots for efficient path planning usually involves a decentralized approach [20] in which each robot calculates its path individually and decisions to change paths or avoid obstacles is done later based on the received messages from other robots. This is unlike centralized path planners [21] in which all the paths of all the robots are calculated simultaneously. In Reference [22], a motion planner is proposed for multiple robots with limited ranges of sensing and communication to reach the goal in dynamic environments. In Reference [23], a navigational technique for multiple service robots in a robotic wireless network (RWN) is presented in which robots download map information from map servers for safe navigation. Semantic information is used among multiple robots for efficient task coorindation in Reference [24].

In Reference [25], a practical case of multi-robot navigation in warehouse has been discussed. The proposed work also deals with the positional uncertainty of robots and obstacles. In this context, a decentralized approach for collaboration between multiple robots in presence of uncertainty are considered for robot action in Reference [26]. A review of multi-robot navigation strategies can be found in References [27–29].

The proposed work is an extension of our previous work [12]. Our previous work proposed the idea of a 'Node-Map' and obstacle's confidence decay mechanism. However, there were many limitations which are addressed in this extended work. The new major contributions are:


coherently between heterogeneous maps are also discussed. This is discussed in 'Section 6.1 Experiments with Heterogeneous Maps'.

3. **New Experiments in Dynamic Environment with Moving People and Testing Under Pressure:** The previous work only worked with static obstacles. In the extended work, new experiments have been performed to test the method when people are randomly moving in the vicinity of the robot and obstructing its navigation. In this regard, the tight coupling of new obstacle's uncertainty in the confidence decay mechanism plays a vital role to avoid false map-updates corresponding to the dynamic obstacles. This is discussed in 'Section 6.2. Results with Dynamic Entities (Moving Obstacle)'.

The comparison of the previous work with the extended work is summarized in Table 1. In addition, the proposed work discusses the algorithm to generate the T-node map.


**Table 1.** Comparison of this extended work with the previous work [12].

The paper starts by first explaining the correspondence problem in different maps in Section 2. The node-map representation is explained in Section 3. Section 4 briefly explains obstacle removal and update in the nodemap and Section 4.1 explains the integration of positional uncertainty in the confidence decay mechanism. Further, using this coupling with Extended Kalman Filter is explained in Section 5 with detailed algorithm. The experimental results are discussed in Section 6. Section 6.1 explains about the experiments with heterogeneous maps and Section 6.2 discusses the results with dynamic entities (moving people). Finally, Section 7 concludes the paper.

### **2. Correspondence Problem in Different Maps**

In dynamic environments, the new objects in the environment could be the temporary or new permanent obstacles. Both needs to be estimated in the map for correct path planning. A robot estimates the absolute position (*xobs*, *yobs*) of an obstacle in its map through its SLAM module. This estimation also has an uncertainty (Σ*obs*) associated with it which arises mainly from sensor errors. This information about the new obstacle (*xobs*, *yobs*, Σ*obs*) is difficult to be directly shared with other robots.

A common problem occurring in multi-robot systems is information sharing in different types of maps (e.g., 2D grid-map, 3D semantic map, feature map etc.) made from different sensors used by the robots for mapping and localization. Even the maps generated using the same sensor (e.g., Lidar) can vary in scale or rotation and the sensors used might have different specifications like resolution or range. In such scenarios, the 'correspondence problem' in different maps is a critical bottleneck in information sharing. Moreover, the uncertainty of localization also adversely affects the information sharing. In other words, it is important to consider how to easily correspond local spatial information in one map to spatial information in a separate map of different type or scale while considering the uncertainty.

This is graphically explained in Figure 2. There is a scale difference between Map1 and Map2. Whereas, Map2 and Map3 differ by a rotation factor. A spatial obstacle information, for example, position (*x*1, *y*1) will correspond to different spatial coordinates in Map2 and Map3. In most real world scenarios, these scale and rotation differences are generally not known. Some previously proposed

techniques [30] to find the necessary translation and rotation can be applied to transform the spatial information in one map to another. However, the computation costs are expensive and could introduce undesired delays.

Although the example in Figure 2 is simplified for illustration, in actual scenarios different maps may have different levels of noise and even feature dimensions. Moreover, some robots may only have a partial map information. Similarly, Figure 3 discusses the problem of robots having different types of maps [31]. Figure 3a is a map in the form of a graph, Figure 3b is a dense 3D map, while Figure 3c is the gridmap of the same environment. It is difficult to for the robots to correlate spatial information in such different types of maps.

**Figure 2.** Correspondence problem due to the scale and rotational differences between maps.

**Figure 3.** Correspondence problem due to the different types of maps [31] of the same environment. (**a**) Graph map. (**b**) Dense 3D map. (**c**) Grid map.

In the proposed work, it is assumed that the robots work in the common service area whose map is available to the robots. This map itself could be heterogenous, for example, grid-map, RGBD map and so forth, which is built using different sensors mounted on different robots. Moreover, the maps could be build from different anchor points. Thus, different robots could have heterogeneous maps.
