Autonomous robots can benefit greatly from human-provided semantic characterizations of uncertain task environments and states. However, the development of integrated strategies which let robots model, communicate, and act on such soft data remains challenging. Here, a framework is presented for active semantic sensing and planning in human-robot teams which addresses these gaps by formally combining the benefits of online sampling-based POMDP policies, multi-modal semantic interaction, and Bayesian data fusion. This approach lets humans opportunistically impose model structure and extend the range of semantic soft data in uncertain environments by sketching and labeling arbitrary landmarks across the environment. Dynamic updating of the environment while searching for a mobile target allows robotic agents to actively query humans for novel and relevant semantic data, thereby improving beliefs of unknown environments and target states for improved online planning. Target search simulations show significant improvements in time and belief state estimates required for interception versus conventional planning based solely on robotic sensing. Human subject studies demonstrate a average doubling in dynamic target capture rate compared to the lone robot case, employing reasoning over a range of user characteristics and interaction modalities. Video of interaction can be found at https://youtu.be/Eh-82ZJ1o4I.

Localization of low-cost Unmanned Aerial Vehicles (UAVs) often relies on Global Navigation Satellite Systems (GNSS). GNSS are susceptible to both natural disruptions to radio signal and intentional jamming and spoofing by an adversary. A typical way to provide georeferenced localization without GNSS for small UAVs is to have a downward-facing camera and match camera images to a map. The downward-facing camera adds cost, size, and weight to the UAV platform and the orientation limits its usability for other purposes. In this work, we propose a Monte-Carlo localization method for georeferenced localization of an UAV requiring no infrastructure using only inertial measurements, a camera facing an arbitrary direction, and an orthoimage map. We perform orthorectification of the UAV image, relying on a local planarity assumption of the environment, relaxing the requirement of downward-pointing camera. We propose a measure of goodness for the matching score of an orthorectified UAV image and a map. We demonstrate that the system is able to localize globally an UAV with modest requirements for initialization and map resolution.

Agriculture is a sector that is always in the hype. It is one of the most essential parts to keep us all alive. As farmers deal with tough times in monitoring and harvesting crops, new technological trends such as drones are making their work hustle-free. Let's see the top 5 agriculture drones start-ups to know in 2021 Aerobotics is one of the agriculture drones start-ups that are based on farm management and pest management solutions. It offers AI-enabled pest detection, drone imagery services, disease detection, orchard, and yield management.

At the outbreak of World War I, the French army was mobilised in the fashion of Napoleonic times. On horseback and equipped with swords, the cuirassiers wore bright tricolour uniforms topped with feathers--the same get-up as when they swept through Europe a hundred years earlier. Vast fields were filled with trenches, barbed wire, poison gas and machine gun fire--plunging the ill-equipped soldiers into a violent hellscape of industrial-scale slaughter. Only three decades after the first World War I bayonet charge across no man's land, the US was able to incinerate entire cities with a single (nuclear) bomb blast. And since the destruction of Hiroshima and Nagasaki in 1945, our rulers' methods of war have been made yet more deadly and "efficient".

We address the problem of routing a team of drones and trucks over large-scale urban road networks. To conserve their limited flight energy, drones can use trucks as temporary modes of transit en route to their own destinations. Such coordination can yield significant savings in total vehicle distance traveled, i.e., truck travel distance and drone flight distance, compared to operating drones and trucks independently. But it comes at the potentially prohibitive computational cost of deciding which trucks and drones should coordinate and when and where it is most beneficial to do so. We tackle this fundamental trade-off by decoupling our overall intractable problem into tractable sub-problems that we solve stage-wise. The first stage solves only for trucks, by computing paths that make them more likely to be useful transit options for drones. The second stage solves only for drones, by routing them over a composite of the road network and the transit network defined by truck paths from the first stage. We design a comprehensive algorithmic framework that frames each stage as a multi-agent path-finding problem and implement two distinct methods for solving them. We evaluate our approach on extensive simulations with up to $100$ agents on the real-world Manhattan road network containing nearly $4500$ vertices and $10000$ edges. Our framework saves on more than $50\%$ of vehicle distance traveled compared to independently solving for trucks and drones, and computes solutions for all settings within $5$ minutes on commodity hardware.

The United States has offered unspecified condolence payments to the families of the 10 civilians, including seven children, who were mistakenly killed in the Aug. 29 drone strike in Kabul that took place shortly before American troops withdrew from Afghanistan. The Pentagon also said it's working with the State Department to support family members who may want to relocate to the United States. The U.S. military insisted for almost three weeks that the drone strike was justified, claiming it had stopped an attack planned for Kabul's airport. But it later changed its tune amid an overwhelming amount of evidence. Weeks after the Pentagon acknowledged the strike had hit civilians, Colin H. Kahl, the under secretary of defense for policy, held a virtual meeting with Steven Kwon, the founder and president of Nutrition & Education International.

The Pentagon has offered unspecified condolence payments to the family of 10 civilians who were killed in a botched U.S. drone attack in Afghanistan in August during the final days before American troops withdrew from the country. The U.S. Defense Department said it made a commitment that included offering "ex-gratia condolence payments," in addition to working with the U.S. State Department in support of the family members who were interested in relocation to the United States. Under Secretary of Defense for Policy, held a virtual meeting on Thursday with Steven Kwon, the founder and president of Nutrition & Education International, the aid organization that employed Zemari Ahmadi, who was killed in the Aug. 29 drone attack, Pentagon Press Secretary John Kirby said late on Friday. Ahmadi and others who were killed in the strike were innocent victims who bore no blame and were not affiliated with Islamic State Khorasan or threats to U.S. forces, Kirby said. The drone strike in Kabul killed as many as 10 civilians, including seven children.

The compensation offer was made on Thursday in a meeting between Colin Kahl, the under-secretary of defence for policy, and Steven Kwon, the founder and president of an aid group active in Afghanistan called Nutrition and Education International, the Pentagon said in a statement.

The Pentagon has offered unspecified condolence payments to the family of 10 civilians who were killed in a botched US drone attack in Afghanistan in August during the final days before American troops withdrew from the country. The US Department of Defense said it made a commitment that included offering "ex-gratia condolence payments", in addition to working with the US Department of State in support of the family members who were interested in relocation to the United States. Colin Kahl, the US under-secretary of defense for policy, held a virtual meeting on Thursday with Steven Kwon, the founder and president of Nutrition & Education International, the aid organisation that employed Zemari Ahmadi, who was killed in the August 29 drone attack, Pentagon press secretary John Kirby said late on Friday. Ahmadi and others who were killed in the drone raid were innocent victims who bore no blame and were not affiliated with Islamic State in Khorasan Province, ISKP (ISIS-K) or threats to US forces, Kirby said. The drone raid in Kabul killed as many as 10 civilians, including seven children.

To efficiently navigate their surrounding environments and complete missions, unmanned aerial systems (UASs) should be able to detect multiple objects in their surroundings and track their movements over time. So far, however, enabling multi-object tracking in unmanned aerial vehicles has proved to be fairly challenging. Researchers at Lockheed Martin AI Center have recently developed a new deep learning technique that could allow UASs to track multiple objects in their surroundings. Their technique, presented in a paper pre-published on arXiv, could aid the development of better performing and more responsive autonomous flying systems. "We present a robust object tracking architecture aimed to accommodate for the noise in real-time situations," the researchers wrote in their paper.