Digital Twins have gained attention in various industries for simulation, monitoring, and decision-making, relying on ever-improving machine learning models. However, agricultural Digital Twin implementations are limited compared to other industries. Meanwhile, machine learning, particularly reinforcement learning, has shown potential in agricultural applications like optimizing decision-making, task automation, and resource management. A key aspect of Digital Twins is representing physical assets or systems in a virtual environment, which aligns well with reinforcement learning's need for environment representations to learn the best policy for a task. Reinforcement learning in agriculture can thus enable various Digital Twin applications in agricultural domains. This review aims to categorize existing research employing reinforcement learning in agricultural settings by application domains like robotics, greenhouse management, irrigation systems, and crop management, identifying potential future areas for reinforcement learning-based Digital Twins. It also categorizes the reinforcement learning techniques used, including tabular methods, Deep Q-Networks (DQN), Policy Gradient methods, and Actor-Critic algorithms, to overview currently employed models. The review seeks to provide insights into the state-of-the-art in integrating Digital Twins and reinforcement learning in agriculture, identifying gaps and opportunities for future research, and exploring synergies to tackle agricultural challenges and optimize farming, paving the way for more efficient and sustainable farming methodologies.

Unmanned Aerial Vehicles (UAVs) have seen a surge in usage across a multitude of industries, such as aerial photography, military operations, agriculture, mapping, and surveying. The advantages of UAVs over traditional manned aircraft are numerous, including cost-effectiveness, enhanced safety, and superior flexibility. However, the autonomous operation of UAVs, particularly their ability to land on moving platforms like ships, poses a crucial challenge. This capability is of significant importance for industries that depend on maritime transportation or offshore operations. A primary challenge in this context is the estimation of the UAV's relative pose with respect to the ship, which is vital for precise control of the UAV's movements and ensuring a safe landing. Conventionally, the relative pose has been determined using the Real-Time Kinematic (RTK) Global Positioning System (GPS). To receive RTK-GPS, a communication link between the ship and the UAV must be maintained at all times, typically via radio.

Fox News' Greg Palkot on the latest from the war in Ukraine as more weapons are sent from U.S. Ukraine's army has struck missile launch positions in Russia, helping to reduce the number of attacks on the embattled city of Kharkiv, its mayor told Reuters on Tuesday. His comments came after U.S. President Joe Biden late last month approved the use of American weapons to strike targets inside Russia that were being used to attack Kharkiv, Ukraine's second-largest city located close to the Russian border. While missile and drone strikes continue, Ihor Terekhov said the change had helped bring relative "calm." "This has helped," Terekhov said in an interview in Berlin, when asked whether the ability to strike inside Russia had alleviated the situation following weeks of heavy bombardment. "That is why maybe Kharkiv has ... this period of ... calm the last couple of weeks ... that there were no great strikes as it was, for example, in May." He was speaking through a translator.

This article introduces a novel method for converting 3D voxel maps, commonly utilized by robots for localization and navigation, into 2D occupancy maps that can be used for more computationally efficient large-scale navigation, both in the sense of computation time and memory usage. The main aim is to effectively integrate the distinct mapping advantages of 2D and 3D maps to enable efficient path planning for both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The proposed method uses the free space representation in the UFOMap mapping solution to generate 2D occupancy maps with height and slope information. In the process of 3D to 2D map conversion, the proposed method conducts safety checks and eliminates free spaces in the map with dimensions (in the height axis) lower than the robot's safety margins. This allows an aerial or ground robot to navigate safely, relying primarily on the 2D map generated by the method. Additionally, the method extracts height and slope data from the 3D voxel map. The slope data identifies areas too steep for a ground robot to traverse, marking them as occupied, thus enabling a more accurate representation of the terrain for ground robots. The height data is utilized to convert paths generated using the 2D map into paths in 3D space for both UAVs and UGVs. The effectiveness of the proposed method is evaluated in two different environments.

Margarita Chli is a professor of Robotic Vision and the director of the Vision for Robotics Lab, at the University of Cyprus and ETH Zurich. Her work has contributed to the first vision-based autonomous flight of a small drone and the first demonstration of collaborative monocular SLAM for a small swarm of drones. Margarita has given invited keynotes at the World Economic Forum in Davos, TEDx, and ICRA, and she was featured in Robohub's 2016 list of "25 women in Robotics you need to know about". In 2023, she won the ERC Consolidator Grant to research advanced robotic perception.

The demand is to solve the issue of UAV (unmanned aerial vehicle) operating autonomously and implementing practical functions such as search and rescue in complex unknown environments. This paper proposes an autonomous search and rescue UAV system based on an EGO-Planner algorithm, which is improved by innovative UAV body application and takes the methods of inverse motor backstepping to enhance the overall flight efficiency of the UAV and miniaturization of the whole machine. At the same time, the system introduced the EGO-Planner planning tool, which is optimized by a bidirectional A* algorithm along with an object detection algorithm. It solves the issue of intelligent obstacle avoidance and search and rescue. Through the simulation and field verification work, and compared with traditional algorithms, this method shows more efficiency and reliability in the task. In addition, due to the existing algorithm's improved robustness, this application shows good prospection.

As low-quality housing and in particular certain roof characteristics are associated with an increased risk of malaria, classification of roof types based on remote sensing imagery can support the assessment of malaria risk and thereby help prevent the disease. To support research in this area, we release the Nacala-Roof-Material dataset, which contains high-resolution drone images from Mozambique with corresponding labels delineating houses and specifying their roof types. The dataset defines a multi-task computer vision problem, comprising object detection, classification, and segmentation. In addition, we benchmarked various state-of-the-art approaches on the dataset. Canonical U-Nets, YOLOv8, and a custom decoder on pretrained DINOv2 served as baselines. We show that each of the methods has its advantages but none is superior on all tasks, which highlights the potential of our dataset for future research in multi-task learning. While the tasks are closely related, accurate segmentation of objects does not necessarily imply accurate instance separation, and vice versa. We address this general issue by introducing a variant of the deep ordinal watershed (DOW) approach that additionally separates the interior of objects, allowing for improved object delineation and separation. We show that our DOW variant is a generic approach that improves the performance of both U-Net and DINOv2 backbones, leading to a better trade-off between semantic segmentation and instance segmentation.

ZeroMark, a defense startup based in the United States, thinks it has a solution. It wants to turn the rifles of frontline soldiers into "handheld Iron Domes." The idea is simple: Make it easier to shoot a drone out of the sky with a bullet. The problem is that drones are fast and maneuverable, making them hard for even a skilled marksman to hit. ZeroMark's system would add aim assistance to existing rifles, ostensibly helping soldiers put a bullet in just the right place.

I'm taking over the newsletter this week to tell you about the first and largest police drone operation in the country. It's a trend in policing that could hit the skies over your streets soon. Since 2018, police in a border city in California called Chula Vista have been dispatching drones to investigate thousands of 911 calls. The drones are equipped with high-resolution cameras and powerful zoom lenses, recording everything in their path. They routinely fly over back yards, public pools, schools, hospitals, mosques, and even Planned Parenthood, in the process amassing hundreds of hours of footage above residents who have nothing to do with a crime.

Abstract-- Achieving safe and precise landings for a swarm of drones poses a significant challenge, primarily attributed to conventional control and planning methods. This paper presents the implementation of multi-agent deep reinforcement learning (MADRL) techniques for the precise landing of a drone swarm at relocated target locations. The system is trained in a realistic simulated environment with a maximum velocity of 3 m/s in training spaces of 4 x 4 x 4 m and deployed utilizing Crazyflie drones with a Vicon indoor localization system. This research highlights drone landing technologies that eliminate the need for analytical centralized systems, potentially offering scalability and revolutionizing applications in logistics, safety, and rescue missions. Swarm drones, characterized by their collaborative behavior, are driving research due to their disruptive potential across industries like agriculture, construction, entertainment, and logistics [1], [2].