@InProceedings{Mazdin2020, author = {Petra Mazdin and Michal Barcis and Hellwagner, Hermann and Bernhard Rinner}, booktitle = {2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)}, title = {{Distributed Task Assignment in Multi-Robot Systems based on Information Utility}}, year = {2020}, month = {aug}, pages = {734--740}, publisher = {IEEE}, 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.}, doi = {10.1109/case48305.2020.9216982}, keywords = {Task analysis, Robot kinematics, Mathematical model, Multi-robot systems, Optimization, Heuristic algorithms}, url = {https://doi.org/10.1109/CASE48305.2020.9216982} }