Abstract: This work addresses the problem of application-layer congestion control in multi-robot systems (MRS). It is motivated by the fact that many MRS constrain the amount of transmitted data in order to avoid congestion in the network and ensure that critical messages get delivered. However, such constraints often need to be manually tuned and assume constant network capabilities. We introduce the adaptive goodput constraint, which smoothly adapts to varying communication conditions. It is suitable for long-term communication planning, where rapid changes are undesirable. We analyze the introduced method in a simulation-based study and show its practical applicability using mobile robots.

Abstract: This work addresses the problem of what information is worth sending in a multi-robot system under generic constraints, e.g., limited throughput or energy. Our decision method is based on Monte Carlo Tree Search. It is designed as a transparent middleware that can be integrated into existing systems to optimize communication among robots. Furthermore, we introduce techniques to reduce the decision space of this problem to further improve the performance. We evaluate our approach using a simulation study and demonstrate its feasibility in a real-world environment by realizing a proof of concept in ROS 2 on mobile robots.

Abstract: Most multi-robot systems (MRS) require to coordinate the assignment of tasks to individual robots for efficient missions. Due to the dynamics, incomplete knowledge and changing requirements, the robots need to distribute their local state information within the MRS continuously during the mission. Since communication resources are limited and message transfers may be erroneous, the global state estimated by each robot may become inconsistent. This inconsistency may lead to degraded task assignment and mission performance. In this paper, we explore the effect and cost of communication and exploit information utility for online distributed task assignment. In particular, we model the usefulness of the transferred state information by its information utility and use it for controlling the distribution of local state information and for updating the global state. We compare our distributed, utility-based online task assignment with well-known centralized and auction-based methods and show how substantial reduction of communication effort still leads to successful mission completion. We demonstrate our approach in a wireless communication testbed using ROS2.

Abstract: This work addresses the problem of information distribution in multi-robot systems, with an emphasis on multi-UAV (unmanned aerial vehicle) applications. We present an analytical model that helps evaluate and compare different information distribution schemes in a robotic mission. It serves as a unified framework to represent the usefulness (utility) of each message exchanged by the robots. It can be used either on its own in order to assess the information distribution efficacy or as a building block of solutions aimed at optimizing information distribution. Moreover, we present multiple examples of instantiating the model for specific missions. They illustrate various approaches to defining the utility of different information types. Finally, we introduce a proof of concept showing the applicability of the model in a robotic system by implementing it in Robot Operating System 2 (ROS 2) and performing a simple simulated mission using a network emulator. We believe the introduced model can serve as a basis for further research on generic solutions for assessing or optimizing information distribution.