The Department of Defense (DoD) has awarded BlueHalo Titan Defense a $24M contract to procure multiple Titan C-UAS systems. Titan, BlueHalo's AI/ML-powered RF-based Counter-Unmanned Aerial System (C-UAS) solution, is the DoD's recent selection as a Program of Record (POR) capability. Incorporation into the POR and this subsequent award serves as a milestone achievement for the Titan system after providing uncompromised C-UAS force protection to America's servicemen and women abroad in all Combatant Commands, as well as critical homeland defense in the United States for over five years. The undisclosed government customer will use Titan systems for pre-deployment activities, mobile security, fixed site protection, and dismounted operations while deployed. "Titan acts as a force multiplier for our troops," said Jonathan Moneymaker, BlueHalo's Chief Executive Officer.

The Russian Embassy in Beijing has pulled down a controversial post on Weibo, praising China's top drone maker DJI for its products, which allegedly helped Kremlin in "modern warfare." The post went missing after DJI rebuffed the claim, stating its drones were not meant for military specifications. The embassy's post on Weibo cited a report from Russian state media Sputnik, based on a new book by Army General Yuri Baluyevsky, the former Chief of the General Staff of the Armed Forces of the Russian Federation, according to South China Morning Post. In his book, Baluyevsky said Chinese commercial drones have brought "a real revolution" to traditional artillery weapons. "When drones hover over a target area to guide the artillery, its pinpoint accuracy and efficiency are comparable to precision-guided missiles, according to the Russian embassy's Weibo post quoting Baluyevsky. "The Mavic quadcopter drone made by China's DJI has become a true symbol of modern warfare," he said. However, the post soon snowballed into a major controversy, as many netizens called out Russian Embassy for uploading something with "malicious intent." "What do you want by saying this?

Tehran, Iran – A military drone tournament has been launched in central Iran with Russia, Belarus and Armenia in attendance. Iranian state television showed footage from a ceremony on Monday in the city of Kashan, where dozens of representatives from the four countries gathered to inaugurate the tournament that is judged by members from all delegations. The 2022 "Falcon Hunting" unmanned aerial vehicle (UAV) competition, part of the seventh iteration of wider annual military games launched by Russia in 2015, was hosted by the aerospace division of Iran's Islamic Revolutionary Guard Corps (IRGC) in a city where many of the elite force's drone tests are conducted. Ali Balali, top adviser to IRGC aerospace chief Amir Ali Hajizadeh and the tournament's spokesman, said the competition would be judged based on performance and consistency in aerial reconnaissance during both day and night, in addition to how the UAVs could help guide precise artillery fire. The more than 70 military personnel who participated will also undergo physical readiness and shooting tests during the competition, which is expected to end on August 28, Balali told the state-affiliated Tasnim news website.

Low-light environments have posed a formidable challenge for robust unmanned aerial vehicle (UAV) tracking even with state-of-the-art (SOTA) trackers since the potential image features are hard to extract under adverse light conditions. Besides, due to the low visibility, accurate online selection of the object also becomes extremely difficult for human monitors to initialize UAV tracking in ground control stations. To solve these problems, this work proposes a novel enhancer, i.e., HighlightNet, to light up potential objects for both human operators and UAV trackers. By employing Transformer, HighlightNet can adjust enhancement parameters according to global features and is thus adaptive for the illumination variation. Pixel-level range mask is introduced to make HighlightNet more focused on the enhancement of the tracking object and regions without light sources. Furthermore, a soft truncation mechanism is built to prevent background noise from being mistaken for crucial features. Evaluations on image enhancement benchmarks demonstrate HighlightNet has advantages in facilitating human perception. Experiments on the public UAVDark135 benchmark show that HightlightNet is more suitable for UAV tracking tasks than other SOTA low-light enhancers. In addition, real-world tests on a typical UAV platform verify HightlightNet's practicability and efficiency in nighttime aerial tracking-related applications. The code and demo videos are available at https://github.com/vision4robotics/HighlightNet.

Unmanned Aerial Vehicle (UAV) has gained significant traction in the recent years, particularly the context of surveillance. However, video datasets that capture violent and non-violent human activity from aerial point-of-view is scarce. To address this issue, we propose a novel, baseline simulator which is capable of generating sequences of photo-realistic synthetic images of crowds engaging in various activities that can be categorized as violent or non-violent. The crowd groups are annotated with bounding boxes that are automatically computed using semantic segmentation. Our simulator is capable of generating large, randomized urban environments and is able to maintain an average of 25 frames per second on a mid-range computer with 150 concurrent crowd agents interacting with each other. We also show that when synthetic data from the proposed simulator is augmented with real world data, binary video classification accuracy is improved by 5% on average across two different models.

With the advancement of modern robotics, autonomous agents are now capable of hosting sophisticated algorithms, which enables them to make intelligent decisions. But developing and testing such algorithms directly in real-world systems is tedious and may result in the wastage of valuable resources. Especially for heterogeneous multi-agent systems in battlefield environments where communication is critical in determining the system's behavior and usability. Due to the necessity of simulators of separate paradigms (co-simulation) to simulate such scenarios before deploying, synchronization between those simulators is vital. Existing works aimed at resolving this issue fall short of addressing diversity among deployed agents. In this work, we propose \textit{SynchroSim}, an integrated co-simulation middleware to simulate a heterogeneous multi-robot system. Here we propose a velocity difference-driven adjustable window size approach with a view to reducing packet loss probability. It takes into account the respective velocities of deployed agents to calculate a suitable window size before transmitting data between them. We consider our algorithm-specific simulator agnostic but for the sake of implementation results, we have used Gazebo as a Physics simulator and NS-3 as a network simulator. Also, we design our algorithm considering the Perception-Action loop inside a closed communication channel, which is one of the essential factors in a contested scenario with the requirement of high fidelity in terms of data transmission. We validate our approach empirically at both the simulation and system level for both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Our approach achieves a noticeable improvement in terms of reducing packet loss probability ($\approx$11\%), and average packet delay ($\approx$10\%) compared to the fixed window size-based synchronization approach.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Texas man was arrested after allegedly flying a drone loaded with drugs, prepaid phones and mp3 players into a Fort Worth prison yard. Bryant LeRay Henderson, 42, was arrested at his home in Smithville, Texas and charged with one count of attempting to provide contraband in prison, one count of serving as an airman without an airman's certificate, and one count of possession with intent to distribute a controlled substance. "Contraband drone deliveries are quickly becoming the bane of prison officials' existence. Illicit goods pose a threat to guards and inmates alike – and when it comes to cell phones, the threat often extends outside prison walls. We are determined to stop this trend in its tracks," said U.S. Attorney Chad Meacham in a press release on Friday.

UK pharmacy Boots has completed a test flight from Portsmouth to the Isle of Wight which involved prescription-only medicines being delivered by drone. The flight departed from the British Army's Baker Barracks on Thorney Island near Portsmouth and arrived at St. Mary's Hospital on the Isle of Wight. Boots collected the medicines and transported them to its pharmacies across the island, where they will be distributed to patients with prescriptions for them. Rich Corbridge, chief information officer at Boots, said: "Drones have huge potential in the delivery of medicines, and it is incredibly exciting to be the first community pharmacy in the UK to transport them in this way. "An island location like the Isle of Wight seemed like a sensible place to start a trial of drones and their value to the delivery of medicines to more remote locations is very clear.

In recent years, unmanned aerial vehicles (UAVs) are used for numerous inspection and video capture tasks. Manually controlling UAVs in the vicinity of obstacles is challenging, however, and poses a high risk of collisions. Even for autonomous flight, global navigation planning might be too slow to react to newly perceived obstacles. Disturbances such as wind might lead to deviations from the planned trajectories. In this work, we present a fast predictive obstacle avoidance method that does not depend on higher-level localization or mapping and maintains the dynamic flight capabilities of UAVs. It directly operates on LiDAR range images in real time and adjusts the current flight direction by computing angular potential fields within the range image. The velocity magnitude is subsequently determined based on a trajectory prediction and time-to-contact estimation. Our method is evaluated using Hardware-in-the-Loop simulations. It keeps the UAV at a safe distance to obstacles, while allowing higher flight velocities than previous reactive obstacle avoidance methods that directly operate on sensor data.

This paper introduces a testbed to study distributed sensing problems of Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Several Smart City applications, such as transport and disaster response, require efficient collection of sensor data by a swarm of intelligent and cooperative UAVs. This often proves to be too complex and costly to study systematically and rigorously without compromising scale, realism and external validity. With the proposed testbed, this paper sets a stepping stone to emulate, within small laboratory spaces, large sensing areas of interest originated from empirical data and simulation models. Over this sensing map, a swarm of low-cost drones can fly allowing the study of a large spectrum of problems such as energy consumption, charging control, navigation and collision avoidance. The applicability of a decentralized multi-agent collective learning algorithm (EPOS) for UAV swarm intelligence along with the assessment of power consumption measurements provide a proof-of-concept and validate the accuracy of the proposed testbed.