Optimal maintenance of sensor nodes in a Wireless Rechargeable Sensor Network (WRSN) requires effective scheduling of power delivery vehicles by solving the Charging Scheduling Problem (CSP). Deploying Unmanned Aerial Vehicles (UAVs) as mobile chargers has emerged as a promising solution due to their mobility and flexibility. The CSP can be formulated as a Mixed-Integer Non-Linear Programming problem whose optimization objective is maximizing the recharged energy of sensor nodes within the UAV battery constraint. While many studies have demonstrated satisfactory performance of heuristic algorithms in addressing specific routing problems, few studies explore online updating (i.e., mission re-planning `on the fly') in the CSP context. Here we present a new offline and online mission planner leveraging a first-principles power consumption model that uses real-time state information and environmental information. The planner, namely Rapid Online Metaheuristic-based Planner (ROMP), supplements solutions from a Guided Local Search (GLS) with our Context-aware Black Hole Algorithm. Our results demonstrate that ROMP outperforms GLS in most cases tested. We developed and proposed FastROMP to speed up the online mission (re-)planning algorithm by introducing a new online adjustment operator that uses the latest state information as input, eliminating the need for re-initialization. FastROMP not only provides a better quality route, but it also significantly reduces computational time. The reduction ranges from 39.57% in sparse deployment to 93.3% in denser deployments.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Six people are accused in a federal indictment of conspiring to use a drone to fly contraband such as cell phones and marijuana into the U.S. Penitentiary in Leavenworth. The indictment was unsealed Wednesday after all the suspects were arrested, according to court records in the U.S. District of Kansas. Two prisoners, Dale Gaver III and Melvin Edwards, allegedly arranged with four people outside the prison to deliver items requested by other inmates into the prison yard between August 2020 and May 2021, The Wichita Eagle reported.

Immediately, drone and digicam know-how chief DJI introduced the DJI Encourage 3, a full-frame 8K cinema drone designed for top-level film productions. Its built-in design contains a 161 ultra-wide FOV night-vision FPV and the O3 Professional transmission and management system. DJI's first and solely cinema-grade drone, the Encourage 3 helps each RTK-powered Waypoint Professional and omnidirectional sensing to conduct safer and extra correct flight missions. "The Encourage 3 is the professional-level aerial platform all filmmakers have been ready for," mentioned DJI Artistic Director Ferdinand Wolf. "It empowers customers to totally maximize the potential of any shot as they'll report in cinematic-grade picture high quality beforehand solely out there with massive and clunky digicam programs. We're wanting ahead to seeing how the Encourage 3 will push aerial cinematography to a totally new stage."

The war in Ukraine, now into its 14th grueling month, has displaced millions, sparked global food shortages, and threatened to spiral into wider conflict. It has also highlighted how new technologies--especially ones drawn from the commercial sector--are upending conventional military doctrine. Ukraine has resisted and repelled Russia's much larger military force, thanks in large part to a willingness, borne of necessity, to adopt and experiment with novel technologies, not all of them originally designed for military use. I recently spoke with Ukraine's 32-year-old minister of digital transformation, Mykhailo Fedorov, about the country's interest in tapping new technology to boost the war effort. Fedorov spoke over Zoom, via an interpreter, from an undisclosed location in Ukraine, about plans to produce more sophisticated drones and other autonomous systems, and to incubate military startups.

Rebekah Koffler discusses if the U.S. is prepared to simultaneously provide aid to Ukraine and Taiwan. An open letter recently signed by Elon Musk, researchers from the Massachusetts Institute of Technology and Harvard University, and more than a thousand other prominent people set off alarm bells on advances in artificial intelligence (AI). The letter urged the world's leading labs to hit the brakes on this powerful technology for six months because of the "profound risks to society and humanity." A pause to consider the ramifications of this unpredictable new technology may have benefits. But our enemies will not wait while the U.S. engages in teleological discourse.

DJI unveiled its latest high-end drone for professional filmmakers today. The Inspire 3 is a full-frame 8K cinema drone in a "highly portable form factor" that can be yours this summer for a mere $16,499. The DJI Inspire 3 has a Zenmuse X9-8K Air Gimbal Camera with a wide range of dynamic colors and compatibility with various lenses. Its camera system has dual native ISO for clear low-light footage while covering over 14 stops of dynamic range to help capture highlights and shadows in sunrises and sunsets. It has a Tilt Boost and 360-degree Pan structures.

Warning: Some readers may find some of the scenes described in this article disturbing. Kyiv, Ukraine – Ivan Ukraintsev, a stern-faced insurance broker turned director of a wartime charity providing crucial aid to Ukraine's military forces, is on a mission: to help Ukraine win the drone war. He is a polite but no-nonsense character, and he is here to talk about drones. "If we [Ukraine] had enough drones, we could end this war in two months," he says firmly. Ivan, who heads up the charity Starlife, had recently returned from overseeing a drone delivery to Bakhmut, a city in eastern Ukraine that has become the focal point for months of bloody battles between Ukrainian and Russian forces. Trench warfare, pockmarked and corpse-ridden swathes of no man's land, and constant artillery bombardments have drawn comparisons to battlefield conditions during World War I.

Unmanned aerial vehicles (UAV) or drones play many roles in a modern smart city such as the delivery of goods, mapping real-time road traffic and monitoring pollution. The ability of drones to perform these functions often requires the support of machine learning technology. However, traditional machine learning models for drones encounter data privacy problems, communication costs and energy limitations. Federated Learning, an emerging distributed machine learning approach, is an excellent solution to address these issues. Federated learning (FL) allows drones to train local models without transmitting raw data. However, existing FL requires a central server to aggregate the trained model parameters of the UAV. A failure of the central server can significantly impact the overall training. In this paper, we propose two aggregation methods: Commutative FL and Alternate FL, based on the existing architecture of decentralised Federated Learning for UAV Networks (DFL-UN) by adding a unique aggregation method of decentralised FL. Those two methods can effectively control energy consumption and communication cost by controlling the number of local training epochs, local communication, and global communication. The simulation results of the proposed training methods are also presented to verify the feasibility and efficiency of the architecture compared with two benchmark methods (e.g. standard machine learning training and standard single aggregation server training). The simulation results show that the proposed methods outperform the benchmark methods in terms of operational stability, energy consumption and communication cost.

This paper studies the problem of safe and optimal continuum deformation of a large-scale multi-agent system (MAS). We present a novel approach for MAS continuum deformation coordination that aims to achieve safe and efficient agent movement using a leader-follower multi-layer hierarchical optimization framework with a single input layer, multiple hidden layers, and a single output layer. The input layer receives the reference (material) positions of the primary leaders, the hidden layers compute the desired positions of the interior leader agents and followers, and the output layer computes the nominal position of the MAS configuration. By introducing a lower bound on the major principles of the strain field of the MAS deformation, we obtain linear inequality safety constraints and ensure inter-agent collision avoidance. The continuum deformation optimization is formulated as a quadratic programming problem. It consists of the following components: (i) decision variables that represent the weights in the first hidden layer; (ii) a quadratic cost function that penalizes deviation of the nominal MAS trajectory from the desired MAS trajectory; and (iii) inequality safety constraints that ensure inter-agent collision avoidance. To validate the proposed approach, we simulate and present the results of continuum deformation on a large-scale quadcopter team tracking a desired helix trajectory, demonstrating improvements in safety and efficiency.

The use of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs) has seen significant growth in the research community, industry, and society. Many of these agents are equipped with communication systems that are essential for completing certain tasks successfully. This has led to the emergence of a new interdisciplinary field at the intersection of robotics and communications, which has been further driven by the integration of UAVs into 5G and 6G communication networks. However, one of the main challenges in this research area is how many researchers tend to oversimplify either the robotics or the communications aspects, hindering the full potential of this new interdisciplinary field. In this paper, we present some of the necessary modeling tools for addressing these problems from both a robotics and communications perspective, using the UAV communications relay as an example.