Virginia farmer John Boyd Jr., weighs in on a watchdog's satellite tracking methane emissions and a provision in the omnibus bill that allocates funds for electronically tracking livestock. There is one thing that distinguishes 60-year-old Vo Van Van's rice fields from a mosaic of thousands of other emerald fields across Long An province in southern Vietnam's Mekong Delta: It isn't entirely flooded. Using less water and using a drone to fertilize are new techniques that Van is trying and Vietnam hopes will help solve a paradox at the heart of growing rice: The finicky crop isn't just vulnerable to climate change but also contributes uniquely to it. Rice must be grown separately from other crops and seedlings have to be individually planted in flooded fields; backbreaking, dirty work requiring a lot of labor and water that generates a lot of methane, a potent planet-warming gas that can trap more than 80-times more heat in the atmosphere in the short term than carbon dioxide. A large drone carrying fertilizer flies over Vo Van Van's rice fields in Long An province in southern Vietnam's Mekong Delta, on Jan. 23, 2024.

Amazon customers in California won't be able to get drone deliveries anymore. The e-commerce company has closed its delivery site in Lockeford, which has been operational since 2022, and will now offer its personnel in the area opportunities at other sites. Amazon made the revelation almost as an aside in an announcement that it's launching drone deliveries in the West Valley Phoenix Metro area later this year. Its drones will be deployed from facilities near its Tolleson fulfillment center. Amazon says it's the first time drone deliveries will be fully integrated into its network, and it will allow the company to fulfill and deliver purchases more quickly.

The execution of flight missions by unmanned aerial vehicles (UAV) primarily relies on navigation. In particular, the navigation pipeline has traditionally been divided into positioning and control, operating in a sequential loop. However, the existing navigation pipeline, where the positioning and control are decoupled, struggles to adapt to ubiquitous uncertainties arising from measurement noise, abrupt disturbances, and nonlinear dynamics. As a result, the navigation reliability of the UAV is significantly challenged in complex dynamic areas. For example, the ubiquitous global navigation satellite system (GNSS) positioning can be degraded by the signal reflections from surrounding high-rising buildings in complex urban areas, leading to significantly increased positioning uncertainty. An additional challenge is introduced to the control algorithm due to the complex wind disturbances in urban canyons. Given the fact that the system positioning and control are highly correlated with each other, this research proposes a **tightly joined positioning and control model (JPCM) based on factor graph optimization (FGO)**. In particular, the proposed JPCM combines sensor measurements from positioning and control constraints into a unified probabilistic factor graph. Specifically, the positioning measurements are formulated as the factors in the factor graph. In addition, the model predictive control (MPC) is also formulated as the additional factors in the factor graph. By solving the factor graph contributed by both the positioning-related factors and the MPC-based factors, the complementariness of positioning and control can be deeply exploited. Finally, we validate the effectiveness and resilience of the proposed method using a simulated quadrotor system which shows significantly improved trajectory following performance.

Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices. This expansion facilitates the implementation of large-scale "connected things" within edge networks. The advent of applications necessitating real-time, high-quality service presents several challenges, such as low latency, high data rate, reliability, efficiency, and security, all of which demand resolution. The incorporation of reinforcement learning (RL) methodologies within MEC networks promotes a deeper understanding of mobile user behaviors and network dynamics, thereby optimizing resource use in computing and communication processes. This paper offers an exhaustive survey of RL applications in MEC networks, initially presenting an overview of RL from its fundamental principles to the latest advanced frameworks. Furthermore, it outlines various RL strategies employed in offloading, caching, and communication within MEC networks. Finally, it explores open issues linked with software and hardware platforms, representation, RL robustness, safe RL, large-scale scheduling, generalization, security, and privacy. The paper proposes specific RL techniques to mitigate these issues and provides insights into their practical applications.

In order to clear the world of the threat posed by landmines and other explosive devices, robotic systems can play an important role. However, the development of such field robots that need to operate in hazardous conditions requires the careful consideration of multiple aspects related to the perception, mobility, and collaboration capabilities of the system. In the framework of a European challenge, the Artificial Intelligence for Detection of Explosive Devices - eXtended (AIDEDeX) project proposes to design a heterogeneous multi-robot system with advanced sensor fusion algorithms. This system is specifically designed to detect and classify improvised explosive devices, explosive ordnances, and landmines. This project integrates specialised sensors, including electromagnetic induction, ground penetrating radar, X-Ray backscatter imaging, Raman spectrometers, and multimodal cameras, to achieve comprehensive threat identification and localisation. The proposed system comprises a fleet of unmanned ground vehicles and unmanned aerial vehicles. This article details the operational phases of the AIDEDeX system, from rapid terrain exploration using unmanned aerial vehicles to specialised detection and classification by unmanned ground vehicles equipped with a robotic manipulator. Initially focusing on a centralised approach, the project will also explore the potential of a decentralised control architecture, taking inspiration from swarm robotics to provide a robust, adaptable, and scalable solution for explosive detection.

We present a solution for autonomous forest inventory with a legged robotic platform. Compared to their wheeled and aerial counterparts, legged platforms offer an attractive balance of endurance and low soil impact for forest applications. In this paper, we present the complete system architecture of our forest inventory solution which includes state estimation, navigation, mission planning, and real-time tree segmentation and trait estimation. We present preliminary results for three campaigns in forests in Finland and the UK and summarize the main outcomes, lessons, and challenges. Our UK experiment at the Forest of Dean with the ANYmal D legged platform, achieved an autonomous survey of a 0.96 hectare plot in 20 min, identifying over 100 trees with typical DBH accuracy of 2 cm.

This paper presents a novel control strategy for drone networks to improve the quality of 3D structures reconstructed from aerial images by drones. Unlike the existing coverage control strategies for this purpose, our proposed approach simultaneously controls both the camera orientation and drone translational motion, enabling more comprehensive perspectives and enhancing the map's overall quality. Subsequently, we present a novel problem formulation, including a new performance function to evaluate the drone positions and camera orientations. We then design a QP-based controller with a control barrier-like function for a constraint on the decay rate of the objective function. The present problem formulation poses a new challenge, requiring significantly greater computational efforts than the case involving only translational motion control. We approach this issue technologically, namely by introducing JAX, utilizing just-in-time (JIT) compilation and Graphical Processing Unit (GPU) acceleration. We finally conduct extensive verifications through simulation in ROS (Robot Operating System) and show the real-time feasibility of the controller and the superiority of the present controller to the conventional method.

This paper addresses the problem of controlling multiple unmanned aerial vehicles (UAVs) cooperating in a formation to carry out a complex task such as surface inspection. We first use the virtual leader-follower model to determine the topology and trajectory of the formation. A double-loop control system combining backstepping and sliding mode control techniques is then designed for the UAVs to track the trajectory. A radial basis function neural network (RBFNN) capable of estimating external disturbances is developed to enhance the robustness of the controller. The stability of the controller is proven by using the Lyapunov theorem. A number of comparisons and software-in-the-loop (SIL) tests have been conducted to evaluate the performance of the proposed controller. The results show that our controller not only outperforms other state-of-the-art controllers but is also sufficient for complex tasks of UAVs such as collecting surface data for inspection. The source code of our controller can be found at https://github.com/duynamrcv/rbf_bsmc

Russia's Ministry of Defence reported Ukrainian drone strikes overnight and into Saturday. It said 26 drones were detected over the Belgorod region, 10 over Bryansk, and eight over Kursk, among several other regions. The strikes killed two people in Russia's Belgorod region, Governor Vyacheslav Gladkov said on Saturday. The governors of Kursk, Kaluga and Bryansk, all in western Russia, reported strikes in their regions as well. Ukraine's air force said it shot down a Russian strategic bomber with antiaircraft missiles for the first time since the war began in 2022.

This article presents Persistence Administered Collective Navigation (PACNav) as an approach for achieving decentralized collective navigation of Unmanned Aerial Vehicle (UAV) swarms. The technique is inspired by the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. PACNav relies solely on local observations of relative positions of UAVs, making it suitable for large swarms deprived of communication capabilities and external localization systems. We introduce the novel concepts of path persistence and path similarity, which allow each swarm member to analyze the motion of others. PACNav is grounded on two main principles: (1) UAVs with little variation in motion direction exhibit high path persistence and are considered reliable leaders by other UAVs; (2) groups of UAVs that move in a similar direction demonstrate high path similarity, and such groups are assumed to contain a reliable leader. The proposed approach also incorporates a reactive collision avoidance mechanism to prevent collisions with swarm members and environmental obstacles. The method is validated through simulated and real-world experiments conducted in a natural forest.