**1. Introduction**

Mobile robots are increasingly being used to automate many tasks; tasks which are mostly dull, dangerous, or demanding are a good fit for autonomous robots. The industrial sector has already benefited a lot from 'factory robots'. Recently, a new class of robots called 'service robots' have been increasing. These robots are used to provide several common services like cleaning and delivering, dispatching and moving items. These service robots are also used for specific tasks like patrolling and escorting people. Generally, multiple robots are used for such tasks in large service areas as there are several advantages. One of the major advantages of using multiple robots is wide area coverage. Multiple robots can cover a large area and perform several tasks simultaneously. Task parallelism is

possible as different robots can perform different tasks at the same time. Some robots may be cleaning, some patrolling, while others may be delivering items to specific locations. Fault tolerance is another advantage of multi-robot systems. Even if one of the robots goes out of service the entire service does not stop as other robots can finish the task. Moreover, with task coordination, multiple robots can perform the task efficiently and quickly.

However, with the introduction of multiple robots in a system, there are several challenges which need to be addressed. Among these problems, effective communication between the multiple robots is a major challenge. Communication forms the basis of other major modules like task coordination, task distribution and collective execution. Accurate and content rich information is important for the successful execution of many tasks.

Although there are many benefits of sharing spatial information in a multi-robot system, in this paper, we consider the case of sharing obstacle information in a multi-robot system. The environments at many service places, like hospitals and warehouses, are very dynamic with moving entities and new obstacles. To navigate autonomously in such environments, robots need a map of the environment and need to localize themselves within it. This is generally achieved through a SLAM (Simultaneous Localization and Mapping) [1] module. Generally, if one robot finds a new obstacle in the environment, it only updates its own map. The other robots do not benefit from this knowledge. However, if the robot shares this knowledge with other robots along with updating its map, other robots can update their maps and plan better paths with the real-time information. This is shown in Figure 1, in which, Robot R1 finds a new obstacle and blocked path at the center passage of the service area and shares the spatial coordinates of the obstacle with other robots R2, ··· , R5 which can use this information in generating optimal trajectories. The extension of use-case scenarios other than obstacle information sharing is straightforward.

**Figure 1.** Robot R1 finds a new obstacle blocking the path and shares this information with other robots (R1, ··· , R5). The blue and red ellipses represents the robot's and obstacle's positional uncertainty, respectively.
