# Area Division Using Affinity Propagation for Multi-Robot Coverage Path Planning
## 7 Conclusions  
The presented research introduces a paradigm shift in the domain of multi-robot CPP by utilizing AP for optimally dividing the operational area among the robots. Instead of using traditional methods, which largely rely on the number of robots and their initial positions, this innovative methodology partitions the area into 'n' clusters using AP and subsequently assigns each cluster to a robot. This model, while functioning under the assumption of an unlimited number of robots, provides a unique flexibility by allowing the modification of the AP algorithm's similarity function factor to control the number of generated clusters.  
One field where the proposed algorithm can find profound applications is precision agriculture. This industry, already substantially automated, requires precision farming, which involves the distribution of multiple tasks, such as seeding, fertilizing, and harvesting, across a fleet of robotic entities. Identifying the optimal number of sub-areas becomes paramount to prevent overlap and redundancy in operations. The proposed algorithm, by facilitating automatic partitioning of farmland into sub-areas based on factors such as crop type and topography paves the way for improved resource management. It ensures optimal task distribution amongst autonomous agricultural machines, enhancing their overall operational efficiency, thereby contributing to a significant reduction in the time and cost associated with agricultural practices.  
Additionally, this algorithm can significantly revolutionize urban search and rescue operations. Typically, these operations are time-sensitive, requiring the division of large, affected areas into smaller manageable sub-areas to enable quick and efficient search strategies. The conventional method of dividing areas based on available rescuers may not be effective, especially in scenarios where the rescuers are robotic entities. By employing this algorithm, we could efficiently partition the search area into the appropriate number of sub-areas (clusters) regardless of the number of robots, optimizing the search strategy and increasing the likelihood of successful rescue operations. Moreover, with the AP algorithm's adaptable similarity function factor, the rescue team has flexibility in regulating the number of generated clusters, facilitating a more efficient and coordinated search operation.  
As a significant progression in multi-robot CPP, this methodology paves the way for novel research directions and practical enhancements in this field. The capability to deliver effective area division and path optimization, without burdening the user with the arbitrary decision of the number of robots or their initial positions, sets a new benchmark for multi-robot CPP implementations. Moreover, our work provides a strong foundation for the development of enhanced strategies that can address the existing complexities of multi-robot CPP and further expedite the deployment of autonomous systems in diverse fields ranging from agriculture to reconnaissance missions. Future work will focus on refining the proposed model, incorporating more complex environmental factors, and exploring the potential of integrating this method with different path planning algorithms for better performance.  
Conceptualization, N.B. and M.D.; methodology, N.B.; writing--original draft preparation, N.B.; writing--review and editing, N.B.; visualization, N.B.; supervision, M.D.; funding acquisition, N.B. All authors have read and agreed to the published version of the manuscript.  
This research was carried out as part of the project <Smart Safe Navigation for Electric Bicycles and Skateboards>> (Project code:KMP6-0292520) under the framework of the Action <Investment Plans of Innovation>> of the Operational Program <<Central Macedonia 2014 2020>>, that is co-funded by the European Regional Development Fund and Greece.  
Not applicable.  
The authors declare no conflict of interest.