Reactive Deployment of Autonomous Drones for Livestock Monitoring Based on Densitybased Clustering * Previous research has established that drones could be an efficient tool for farming. In this paper, we study the problem of livestock tracking and monitoring using a group of drones. The main objective is to deploy a limited number of drones to track and monitor the maximum number of livestock, such as cattle and sheep, in a vast pasture while minimizing the average droneanimal distance. We assume the targeted livestock have been fitted with GPS collars, and the mobility of each animal cannot be neglected. We first introduce a procedure of performing sweep coverage by drones. By deploying drones to accomplish sweep coverage for the entire pasture, the initial locations of all targeted animals can be acquired. Then we applied a densitybased clustering algorithm DBSCAN to find the deployment that places the drones to the centroids of animal clusters. Furthermore, based on the updated animalsu0027 locations, the deployment of the drones can be updated to follow the movement of animal clusters. We demonstrate that our solution can always yield a lower average droneanimal distance and a higher number of covered targeted animals, compared with the standard KMeans clustering algorithm.

Agricultural robotics research applicable to poultry production: A review Abstract   The advent of agricultural robotics research worldwide has brought substantial improvement for various applications. This article provides a comprehensive review of published research and development work, emphasizing robotics enabling machine capabilities. These machine capabilities of perception, reasoning and learning, communication, task planning and execution, and systems integration have opened possibilities for intelligent automation of current and future agricultural operations, including precision livestock farming. We have focused on the Agricultural Intelligent Automation Systems which have a high potential to be applied to agricultural production and processing, especially with applicability to poultry production. Most of the published work on agricultural robotics has been in the areas of perception and reasoning. The emphases have been in the identification of objects, evaluation of product quality, monitoring of plant and animal growth and development, yield prediction, and machine guidance. There has been limited published work on the task execution and systems integration aspects of agricultural robotics. Moreover, we have reviewed agricultural robotics research from 24 universities worldwide. Agricultural robots can be divided into three categories (monitor, harvester, and both) according to various functions. Several tables are presented to summarize the information on the key subject areas reviewed in this article. We have found that there are still many challenges that need to be addressed in robotizing agricultural tasks in general and in poultry production specifically. The most common challenges in robotics applications have been developing robots for specific agricultural tasks. Examples in poultry production include monitoring environmental conditions and chicken health, egg picking, and encouraging chicken movement. The approaches to addressing the technical needs have been creating intelligent movable machines for use alongside the chickens in poultry house. The most noticeable results include Octopus Poultry Safe (OPS) robot for sanitizing poultry houses autonomously, PoultryBot for picking floor eggs, and Spoutnic for training hens to move. This trend of research and development is expected to continue. An emerging research emphasis is systems approach to study the interactions of automated tasks to achieve high efficiency in whole poultry house management.