Quick, efficient and with a bird's eye view of any scene, more police departments are embracing the use of drones to carry out law enforcement work, with some blue cities now even using them to respond to 911 calls. Around 1,500 police departments across the country are currently using drones in some form, according to a report by the Electronic Frontier Foundation, a digital privacy group, with agencies deploying the technology for crowd control purposes, missing people searches, tracking fleeing suspects or mapping crime scenes. Steep budget cuts and dwindling staff numbers in blue cities, in particular, make drones both an effective and cost-saving tool for police in Democratic strongholds. A law enforcement official sets up a drone during a manhunt for suspect Robert Card following a mass shooting on Oct. 27, 2023, in Monmouth, Maine. Today's police drones are much bigger than regular drones commonly used for recreational purposes, with much longer battery lives and features such as thermal sensors, loudspeakers, spotlights or beacons.

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.

Employing Unmanned Aircraft Systems (UAS) beyond visual line of sight (BVLOS) is an endearing and challenging task. While UAS have the potential to significantly enhance today's logistics and emergency response capabilities, unmanned flying objects above the heads of unprotected pedestrians induce similarly significant safety risks. In this work, we make strides towards improved safety and legal compliance in applying UAS in two ways. First, we demonstrate navigation within the Probabilistic Mission Design (ProMis) framework. To this end, our approach translates Probabilistic Mission Landscapes (PML) into a navigation graph and derives a cost from the probability of complying with all underlying constraints. Second, we introduce the clearance, explanation, and optimization (CEO) cycle on top of ProMis by leveraging the declaratively encoded domain knowledge, legal requirements, and safety assertions to guide the mission design process. Based on inaccurate, crowd-sourced map data and a synthetic scenario, we illustrate the application and utility of our methods in UAS navigation.

The report's authors detail a number of ways that use of drones in any South China Sea conflict would differ starkly from current practices, most notably in the war in Ukraine, often called the first full-scale drone war. Since Russia invaded Ukraine in 2022, drones have been aiding in what military experts describe as the first three steps of the "kill chain"--finding, targeting, and tracking a target--as well as in delivering explosives. The drones have a short life span, since they are often shot down or made useless by frequency jamming devices that prevent pilots from controlling them. Quadcopters--the commercially available drones often used in the war--last just three flights on average, according to the report. Drones like these would be far less useful in a possible invasion of Taiwan.

Vision-and-language navigation (VLN) aims to guide autonomous agents through real-world environments by integrating visual and linguistic cues. While substantial progress has been made in understanding these interactive modalities in ground-level navigation, aerial navigation remains largely underexplored. This is primarily due to the scarcity of resources suitable for real-world, city-scale aerial navigation studies. To bridge this gap, we introduce CityNav, a new dataset for language-goal aerial navigation using a 3D point cloud representation from real-world cities. CityNav includes 32,637 natural language descriptions paired with human demonstration trajectories, collected from participants via a new web-based 3D simulator developed for this research. Each description specifies a navigation goal, leveraging the names and locations of landmarks within real-world cities. We also provide baseline models of navigation agents that incorporate an internal 2D spatial map representing landmarks referenced in the descriptions. We benchmark the latest aerial navigation baselines and our proposed model on the CityNav dataset. The results using this dataset reveal the following key findings: (i) Our aerial agent models trained on human demonstration trajectories outperform those trained on shortest path trajectories, highlighting the importance of human-driven navigation strategies; (ii) The integration of a 2D spatial map significantly enhances navigation efficiency at city scale. Our dataset and code are available at https://water-cookie.github.io/city-nav-proj/

Future urban air mobility systems are expected to be operated by rideshare companies as fleets, which will require fully autonomous air traffic control systems and an order of magnitude increase in airspace capacity. Such a system must not only be safe, but also highly responsive to customer demand. This paper proposes the air traffic network problem (ATNP), which models the optimization problem of future cooperative air taxi networks. We propose a three-phase decision making model that efficiently assigns vehicles to passengers, determines flight levels to reduce collision risk, and resolves aircraft conflicts by selectively applying Monte Carlo tree search. We develop a simulator for the ATNP and show that our approach has increased safety and reduced passenger waiting time compared to greedy and first-dispatch protocols over potential vertiport layouts across the Bay Area and New York City.

Hezbollah chief Hassan Nasrallah has issued a stern warning to Israel, threatening a war with "no restraint and no rules and no ceilings" in case of a major Israeli offensive against Lebanon. Nasrallah's remarks on Wednesday come amid soaring tensions at the Lebanon-Israel border after Israeli officials reiterated that the country is ready for an all-out war against Hezbollah. "All what the enemy says and the threats and warnings the mediators bring – and what is being said in the Israeli media – about a war in Lebanon does not scare us," Nasrallah said in a speech via video feed. He said that Israel is the party that should be "scared". Israeli Foreign Israeli Foreign Minister Israel Katz on Tuesday raised the prospect of a major conflict with the Lebanese group after Hezbollah released surveillance drone footage showing major infrastructure and military sites in northern Israel.

Former Secretary of State Mike Pompeo joins'The Story' to discuss what the U.S. can do to mitigate the threat Hezbollah poses to Israel. The Israel Defense Forces' announcement that it has approved plans for an "offensive in Lebanon" is "another step towards a significant escalation" against the terrorist group Hezbollah, a former IDF spokesperson tells Fox News Digital. The IDF released a photo this week showing a meeting of top generals discussing war strategy, noting that "as part of the situational assessment, operational plans for an offensive in Lebanon were approved and validated, and decisions were taken on the continuation of increasing the readiness of troops in the field." "What this means is another step towards a significant escalation. The IDF has been preparing troops for combat scenarios in Lebanon for months and what it means that now it has also approved plans, strategic maneuver plans, of how to attack Hezbollah in Lebanon on the ground and of course in the air," Jonathan Conricus, a senior fellow at the Foundation for Defense of Democracies and a former IDF spokesman, told Fox News Digital on Wednesday.

Israel is ready for an "all-out war" in Lebanon and has plans approved for an offensive targeting Hezbollah, officials have said. The claims from Israel's foreign minister and military late on Tuesday followed Hezbollah's release of threatening drone footage. The climbing tension conflicts with United States efforts to avert an escalation amid months of low-level hostilities across the Israel-Lebanon border. The nine-minute drone footage of the Israeli port city of Haifa filmed in daytime, showed civilian and military areas, including malls and residential quarters, in addition to a weapons manufacturing complex and missile defence batteries. Israel's Foreign Minister Israel Katz responded vehemently in a post on X, calling out Hezbollah chief Hassan Nasrallah for boasting about filming the ports of Haifa, which are operated by foreign companies from China and India.

The U.S. on Tuesday approved the sale of 360 million worth of armed drones and loitering munitions to Taiwan as Washington doubles down on helping Taipei counter a potential Chinese attack on the self-ruled island. The move reflects U.S. efforts to not only bolster the self-ruled island's asymmetric capabilities but also to use drone swarms to offset any Chinese military advantages in personnel and equipment in a possible conflict -- a strategy also embraced by Taipei as it draws lessons from the war in Ukraine. Announced by U.S. Defense Security Cooperation Agency, the potential sale, which must be approved by Congress, includes 291 warhead-equipped Altius-600M drones and 720 Switchblade 300 anti-personnel and anti-armor loitering munitions, marking the 15th arms package approved by U.S. President Joe Biden's administration.