The United States military says it has killed a leader of the ISIL (ISIS) group in eastern Syria in a drone strike. The strike on Friday resulted in the death of Osama al-Muhajer, the US Central Command said in a statement on Sunday. "We have made it clear that we remain committed to the defeat of ISIS throughout the region," US Central Command (CENTCOM) chief General Michael Kurilla said, using another acronym for the ISIL armed group. "ISIS remains a threat, not only to the region but well beyond," he added. According to CENTCOM, no civilians were killed in the operation but coalition forces are "assessing reports of a civilian injury".

We consider the problem of routing a team of energy-constrained Unmanned Aerial Vehicles (UAVs) to drop unmovable sensors for monitoring a task area in the presence of stochastic wind disturbances. In prior work on mobile sensor routing problems, sensors and their carrier are one integrated platform, and sensors are assumed to be able to take measurements at exactly desired locations. By contrast, airdropping the sensors onto the ground can introduce stochasticity in the landing locations of the sensors. We focus on addressing this stochasticity in sensor locations from the path-planning perspective. Specifically, we formulate the problem (Multi-UAV Sensor Drop) as a variant of the Submodular Team Orienteering Problem with one additional constraint on the number of sensors on each UAV. The objective is to maximize the Mutual Information between the phenomenon at Points of Interest (PoIs) and the measurements that sensors will take at stochastic locations. We show that such an objective is computationally expensive to evaluate. To tackle this challenge, we propose a surrogate objective with a closed-form expression based on the expected mean and expected covariance of the Gaussian Process. We propose a heuristic algorithm to solve the optimization problem with the surrogate objective. The formulation and the algorithms are validated through extensive simulations.

We study the problem of deploying a fleet of mobile robots to service tasks that arrive stochastically over time and at random locations in an environment. This is known as the Dynamic Vehicle Routing Problem (DVRP) and requires robots to allocate incoming tasks among themselves and find an optimal sequence for each robot. State-of-the-art approaches only consider average wait times and focus on high-load scenarios where the arrival rate of tasks approaches the limit of what can be handled by the robots while keeping the queue of unserviced tasks bounded, i.e., stable. To ensure stability, these approaches repeatedly compute minimum distance tours over a set of newly arrived tasks. This paper is aimed at addressing the missing policies for moderate-load scenarios, where quality of service can be improved by prioritizing long-waiting tasks. We introduce a novel DVRP policy based on a cost function that takes the $p$-norm over accumulated wait times and show it guarantees stability even in high-load scenarios. We demonstrate that the proposed policy outperforms the state-of-the-art in both mean and $95^{th}$ percentile wait times in moderate-load scenarios through simulation experiments in the Euclidean plane as well as using real-world data for city scale service requests.

With the advancement of robotics and AI technologies in the past decade, we have now entered the age of autonomous machines. In this new age of information technology, autonomous machines, such as service robots, autonomous drones, delivery robots, and autonomous vehicles, rather than humans, will provide services. In this article, through examining the technical challenges and economic impact of the digital economy, we argue that scalability is both highly necessary from a technical perspective and significantly advantageous from an economic perspective, thus is the key for the autonomy industry to achieve its full potential. Nonetheless, the current development paradigm, dubbed Autonomy 1.0, scales with the number of engineers, instead of with the amount of data or compute resources, hence preventing the autonomy industry to fully benefit from the economies of scale, especially the exponentially cheapening compute cost and the explosion of available data. We further analyze the key scalability blockers and explain how a new development paradigm, dubbed Autonomy 2.0, can address these problems to greatly boost the autonomy industry.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A drone strike believed to have been carried out by the U.S.-led coalition in northern Syria Friday killed one man with Islamic State links and wounded a passerby, a paramedic group and an opposition war monitor said. The Syrian Civil Defense, also known as the White Helmets, said the man was killed while riding a motorcycle. It added that a passerby was also wounded.

George Gorman, Long Island regional director for the NY State office of Parks and Recreation, explains how drones are being used to track sharks and ensure swimmers' safety. Authorities are using drones to monitor Long Island waters following a flurry of recent incidents with sharks off New York shores. Earlier this week, five people reported being bitten by sharks at popular beaches. In response to encounters there and in other police jurisdictions, the Suffolk County Police Department said it would increase its shark patrols, using drones for an aerial view. "While residents are encouraged to enjoy the summer at the beach, swimmers should remain vigilant when in the water. If you see a shark, or a pod of bunker fish that attract the predators, calmly exit the water and alert the lifeguard on duty or a local official," the department said on Facebook.

Fox News chief national security correspondent Jennifer Griffin has more on Russian aggression in international skies after three warplanes reportedly harassed three American drones on'Special Report.' Russian fighter jets harassed United States Air Force drones over Syria for a second time in 24 hours, U.S. Air Forces Central said Thursday. A new video released Thursday showed the Russian aircraft flying dangerously close to and deploying flares near several U.S. drones. It was released the day after the U.S. military released similar footage on Wednesday. "Russian military aircraft engaged in unsafe and unprofessional behavior Thursday, 9:30 A.M. local time, while interacting with U.S. MQ-9 drones carrying out our D-ISIS mission in Syria," said Lt Gen Alexus Grynkewich, Commander, 9th AF and CFACC for CENTCOM.

Russian fighter jets have again flown dangerously close to several US MQ-9 Reaper drones operating over Syria – the second such incident of harassment in 24 hours – setting off flares and forcing Washington's unmanned aerial vehicles to take evasive manoeuvres, the United States air force said. The protest from the US air forces came as the French military said that two of its fighter jets on patrol over the Iraq-Syria border area were forced to manoeuvre "to control the risk of accident" involving a Russian Sukhoi SU-35 warplane on Thursday. The Russian aircraft had engaged in "non-professional interaction" with two of France's Rafale planes deployed to the region as part of "Operation Chammal", which seeks to contain the ISIL (ISIS) group in Iraq and Syria, the French military said. Two separate incidents on Wednesday and Thursday involving Russian warplanes and US Reaper drones were captured on video, the US said. "The events represent a new level of unprofessional and unsafe action by Russian air forces operating in Syria," the US military said.

Fast moving unmanned aerial vehicles (UAVs) are well suited for aerial surveillance, but are limited by their battery capacity. To increase their endurance UAVs can be refueled on slow moving unmanned ground vehicles (UGVs). The cooperative routing of UAV-UGV multi-agent system to survey vast regions within their speed and fuel constraints is a computationally challenging problem, but can be simplified with heuristics. Here we present multiple heuristics to enable feasible and sufficiently optimal solutions to the problem. Using the UAV fuel limits and the minimum set cover algorithm, the UGV refueling stops are determined. These refueling stops enable the allocation of mission points to the UAV and UGV. A standard traveling salesman formulation and a vehicle routing formulation with time windows, dropped visits, and capacity constraints is used to solve for the UGV and UAV route, respectively. Experimental validation on a small-scale testbed (http://tiny.cc/8or8vz) underscores the effectiveness of our multi-agent approach.

U.S. Air Forces Central released new video appearing to show Russian fighter jets harassing American drones over Syria on July 5. Information about where the incidents took place were not provided. The United States military released new aerial footage on Wednesday that showed Russian fighter jets flying dangerously close to several U.S. drones over Syria. U.S. Air Forces Central said the video of the incident shows Russian SU-35 fighters moving into the drone's flight path and setting off so-called parachute flares and forcing the MQ-9 Reapers to take evasive maneuvers. Lt. Gen. Alex Grynkewich, commander of 9th Air Force in the Middle East, said three of the U.S. drones were operating over Syria, conducting a mission on the Islamic State terror group, when the Russian aircraft "began harassing the drones" after 10:30 a.m. "Russian military aircraft engaged in unsafe and unprofessional behavior while interacting with U.S. aircraft in Syria," he said, describing the actions as threatening to the safety of the U.S. and Russian forces.