This paper presents a novel end-to-end Unmanned Aerial System (UAS) navigation approach for long-range visual navigation in the real world. Inspired by dual-process visual navigation system of human's instinct: environment understanding and landmark recognition, we formulate the UAS navigation task into two same phases. Our system combines the reinforcement learning (RL) and image matching approaches. First, the agent learns the navigation policy using RL in the specified environment. To achieve this, we design an interactive UASNAV environment for the training process. Once the agent learns the navigation policy, which means 'familiarized themselves with the environment', we let the UAS fly in the real world to recognize the landmarks using image matching method and take action according to the learned policy. During the navigation process, the UAS is embedded with single camera as the only visual sensor. We demonstrate that the UAS can learn navigating to the destination hundreds meters away from the starting point with the shortest path in the real world scenario.

The United States has announced nearly $3bn in new military aid to Ukraine, with President Joe Biden saying the assistance aims to help the country defend against Russia's invasion "over the long term" as the war entered its seventh month. In a statement on Wednesday, as Ukraine marked its independence from the Soviet Union, Biden said the $2.98bn package would allow Kyiv to acquire air defence systems, "artillery systems and munitions, counter-unmanned aerial systems, and radars". This is the single largest US aid package for Ukraine since Russian forces began their full-scale military invasion of the country in February. "I know this independence day is bittersweet for many Ukrainians as thousands have been killed or wounded, millions have been displaced from their homes, and so many others have fallen victim to Russian atrocities and attacks," Biden said in the statement. "But six months of relentless attacks have only strengthened Ukrainians' pride in themselves, in their country, and in their thirty-one years of independence."

Visual inertial odometry and SLAM algorithms are widely used in various fields, such as service robots, drones, and autonomous vehicles. Most of the SLAM algorithms are based on assumption that landmarks are static. However, in the real-world, various dynamic objects exist, and they degrade the pose estimation accuracy. In addition, temporarily static objects, which are static during observation but move when they are out of sight, trigger false positive loop closings. To overcome these problems, we propose a novel visual-inertial SLAM framework, called DynaVINS, which is robust against both dynamic objects and temporarily static objects. In our framework, we first present a robust bundle adjustment that could reject the features from dynamic objects by leveraging pose priors estimated by the IMU preintegration. Then, a keyframe grouping and a multi-hypothesis-based constraints grouping methods are proposed to reduce the effect of temporarily static objects in the loop closing. Subsequently, we evaluated our method in a public dataset that contains numerous dynamic objects. Finally, the experimental results corroborate that our DynaVINS has promising performance compared with other state-of-the-art methods by successfully rejecting the effect of dynamic and temporarily static objects. Our code is available at https://github.com/url-kaist/dynaVINS.

Virtual reality (VR) technology is commonly used in entertainment applications; however, it has also been deployed in practical applications in more serious aspects of our lives, such as safety. To support people working in dangerous industries, VR can ensure operators manipulate standardized tasks and work collaboratively to deal with potential risks. Surprisingly, little research has focused on how people can collaboratively work in VR environments. Few studies have paid attention to the cognitive load of operators in their collaborative tasks. Once task demands become complex, many researchers focus on optimizing the design of the interaction interfaces to reduce the cognitive load on the operator. That approach could be of merit; however, it can actually subject operators to a more significant cognitive load and potentially more errors and a failure of collaboration. In this paper, we propose a new collaborative VR system to support two teleoperators working in the VR environment to remote control an uncrewed ground vehicle. We use a compared experiment to evaluate the collaborative VR systems, focusing on the time spent on tasks and the total number of operations. Our results show that the total number of processes and the cognitive load during operations were significantly lower in the two-person group than in the single-person group. Our study sheds light on designing VR systems to support collaborative work with respect to the flow of work of teleoperators instead of simply optimizing the design outcomes.

The logistics of transporting a package from a storage facility to the consumer's front door usually employs highly specialized robots often times splitting sub-tasks up to different systems, e.g., manipulator arms to sort and wheeled vehicles to deliver. More recent endeavors attempt to have a unified approach with legged and humanoid robots. These solutions, however, occupy large amounts of space thus reducing the number of packages that can fit into a delivery vehicle. As a result, these bulky robotic systems often reduce the potential for scalability and task parallelization. In this paper, we introduce LIMMS (Latching Intelligent Modular Mobility System) to address both the manipulation and delivery portion of a typical last-mile delivery while maintaining a minimal spatial footprint. LIMMS is a symmetrically designed, 6 degree of freedom (DoF) appendage-like robot with wheels and latching mechanisms at both ends. By latching onto a surface and anchoring at one end, LIMMS can function as a traditional 6-DoF manipulator arm. On the other hand, multiple LIMMS can latch onto a single box and behave like a legged robotic system where the package is the body. During transit, LIMMS folds up compactly and takes up much less space compared to traditional robotic systems. A large group of LIMMS units can fit inside of a single delivery vehicle, opening the potential for new delivery optimization and hybrid planning methods never done before. In this paper, the feasibility of LIMMS is studied and presented using a hardware prototype as well as simulation results for a range of sub-tasks in a typical last-mile delivery.

Republican Rep. Greg Steube weighs in on the U.S.'s announcement for an additional $775 million in new Ukraine military aid, along with the impact of illegal immigration on Florida's coastal border. The U.S. reportedly is set to provide Ukraine with M982 Excalibur munitions. "These precision-guided munitions will aid Ukraine's counter-offensive against Russia, which is focused on targeting ammunition depots and military installations," foreign relations expert, Samuel Ramani, tweeted. "Scan Eagle surveillance drones, which the U.S. also provides, will amplify their impact." The Ukrainian outlet appeared to draw from reporting by Politico, tweeting: "A source close to the deliberations of the latest military aid package shared with Politico that the M982 Excalibur rounds, traveling up to 70km, will be sent to Ukraine'at some point in the future.'"

The area coverage problem is the task of efficiently servicing a given two-dimensional surface using sensors mounted on robots such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). We present a novel formulation for generating coverage routes for multiple capacity-constrained robots, where capacity can be specified in terms of battery life or flight time. Traversing the environment incurs demands on the robot resources, which have capacity limits. The central aspect of our approach is transforming the area coverage problem into a line coverage problem (i.e., coverage of linear features), and then generating routes that minimize the total cost of travel while respecting the capacity constraints. We define two modes of travel: (1) servicing and (2) deadheading, which correspond to whether a robot is performing task-specific actions or not. Our formulation allows separate and asymmetric travel costs and demands for the two modes. Furthermore, the cells computed from cell decomposition, aimed at minimizing the number of turns, are not required to be monotone polygons. We develop new procedures for cell decomposition and generation of service tracks that can handle non-monotone polygons with or without holes. We establish the efficacy of our algorithm on a ground robot dataset with 25 indoor environments and an aerial robot dataset with 300 outdoor environments. The algorithm generates solutions whose costs are 10% lower on average than state-of-the-art methods. We additionally demonstrate our algorithm in experiments with UAVs.

The attack on the Black Sea fleet in Sevastopol is the latest in a string of strikes against Russia.

A drone has been shot down over the headquarters of Russia's Black Sea Fleet in annexed Crimea, a local official said, in the second attempted strike on the command in Sevastopol in less than a month. "The drone was shot down just above the fleet headquarters" in the city of Sevastopol, city Governor Mikhail Razvojaev wrote on Telegram on Saturday, blaming the attempt on Ukrainian forces. "It fell on the roof and caught fire," he said, adding that there was no significant damage or victim. The first reported attack came on July 31, when a presumed Ukrainian drone attacked the Black Sea Fleet on Russia's Navy Day, wounding five people. Russia also reported Ukrainian drone attacks late on Friday.

Ding dong, the drone is dead. Pixy, Snap's petite flying selfie camera, is no more. Technically you can still buy one, but The Wall Street Journal reported this week that the device is done for. Snap CEO Evan Spiegel has told employees that the company would soon stop making the $230 gizmo. The little drone had a short life.