Cognizant today announced that it has signed a memorandum of understanding (MoU) with Garuda Aerospace, one of India's leading drone startups, to power its drones with advanced digital capabilities and bring innovative solutions at scale for enterprises aiming to achieve greater agility, productivity, and overall outcomes. "We are excited to collaborate with Garuda Aerospace, combine our deep industry knowledge with capabilities such as advanced data analytics, real-time insights and software development to elevate drone-based solutions and new use cases for enterprises across sectors," said Achal Kataria, Vice President and India Country Head, Cognizant. "Drone services are one of the fastest growing technology segments with the potential to provide extraordinary value to industries such as agriculture, manufacturing, energy and utilities, retail and logistics," Kataria added. Cognizant and Garuda Aerospace are collectively set to bring a plethora of end-to-end drone-based management and monitoring offerings for businesses across sectors. For the agriculture sector, a new offering provides intelligent water and soil management, crop spraying, and aerial planting, among others.

This article proposes a method to diminish the pose (position plus attitude) drift experienced by an SVO (Semi-Direct Visual Odometry) based visual navigation system installed onboard a UAV (Unmanned Air Vehicle) by supplementing its pose estimation non linear optimizations with priors based on the outputs of a GNSS (Global Navigation Satellite System) Denied inertial navigation system. The method is inspired in a PI (Proportional Integral) control system, in which the attitude, altitude, and rate of climb inertial outputs act as targets to ensure that the visual estimations do not deviate far from their inertial counterparts. The resulting IA-VNS (Inertially Assisted Visual Navigation System) achieves major reductions in the horizontal position drift inherent to the GNSS-Denied navigation of autonomous fixed wing low SWaP (Size, Weight, and Power) UAVs. Additionally, the IA-VNS can be considered as a virtual incremental position (ground velocity) sensor capable of providing observations to the inertial filter. Stochastic high fidelity Monte Carlo simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results and to analyze their sensitivity to the terrain type overflown by the aircraft as well as to the quality of the onboard sensors on which the priors are based. The author releases the C ++ implementation of both the navigation algorithms and the high fidelity simulation as open-source software.

In South Africa, it is a common practice for people to leave their vehicles beside the road when traveling long distances for a short comfort break. This practice might increase human encounters with wildlife, threatening their security and safety. Here we intend to create awareness about wildlife fencing, using drone technology and computer vision algorithms to recognize and detect wildlife fences and associated features. We collected data at Amakhala and Lalibela private game reserves in the Eastern Cape, South Africa. We used wildlife electric fence data containing single and double fences for the classification task. Additionally, we used aerial and still annotated images extracted from the drone and still cameras for the segmentation and detection tasks. The model training results from the drone camera outperformed those from the still camera. Generally, poor model performance is attributed to (1) over-decompression of images and (2) the ability of drone cameras to capture more details on images for the machine learning model to learn as compared to still cameras that capture only the front view of the wildlife fence. We argue that our model can be deployed on client-edge devices to inform people about the presence and significance of wildlife fencing, which minimizes human encounters with wildlife, thereby mitigating wildlife-vehicle collisions.

Rep. Brian Fitzpatrick, R-Pa., on U.S. aid delivered to Ukraine. EXCLUSIVE: The snow was piling up and blizzard-like conditions were mounting as Anastasiya Koval, an American Ukrainian, crossed into the recently liberated city of Kharkiv in early December while on a humanitarian mission to deliver aid to the front lines. "I didn't realize how massive the city was. It was my first time there," she described in an interview with Fox News Digital. "What really impacted me was when we finally crossed the bridge where the Russian soldiers had entered the city."

Quadrotors with the ability to perch on moving inclined surfaces can save energy and extend their travel distance by leveraging ground vehicles. Achieving dynamic perching places high demands on the performance of trajectory planning and terminal state accuracy in SE(3). However, in the perching process, uncertainties in target surface prediction, tracking control and external disturbances may cause trajectory planning failure or lead to unacceptable terminal errors. To address these challenges, we first propose a trajectory planner that considers adaptation to uncertainties in target prediction and tracking control. To facilitate this work, the reachable set of quadrotors' states is first analyzed. The states whose reachable sets possess the largest coverage probability for uncertainty targets, are defined as optimal waypoints. Subsequently, an approach to seek local optimal waypoints for static and moving uncertainty targets is proposed. A real-time trajectory planner based on optimized waypoints is developed accordingly. Secondly, thrust regulation is also implemented in the terminal attitude tracking stage to handle external disturbances. When a quadrotor's attitude is commanded to align with target surfaces, the thrust is optimized to minimize terminal errors. This makes the terminal position and velocity be controlled in closed-loop manner. Therefore, the resistance to disturbances and terminal accuracy is improved. Extensive simulation experiments demonstrate that our methods can improve the accuracy of terminal states under uncertainties. The success rate is approximately increased by $50\%$ compared to the two-end planner without thrust regulation. Perching on the rear window of a car is also achieved using our proposed heterogeneous cooperation system outdoors. This validates the feasibility and practicality of our methods.

Filming sport videos from an aerial view has always been a hard and an expensive task to achieve, especially in sports that require a wide open area for its normal development or the ones that put in danger human safety. Recently, a new solution arose for aerial filming based on the use of Unmanned Aerial Vehicles (UAVs), which is substantially cheaper than traditional aerial filming solutions that require conventional aircrafts like helicopters or complex structures for wide mobility. In this paper, we describe the design process followed for building a customized UAV suitable for sports aerial filming. The process includes the requirements definition, technical sizing and selection of mechanical, hardware and software technologies, as well as the whole integration and operation settings. One of the goals is to develop technologies allowing to build low cost UAVs and to manage them for a wide range of usage scenarios while achieving high levels of flexibility and automation. This work also shows some technical issues found during the development of the UAV as well as the solutions implemented.

The deployment flexibility and maneuverability of Unmanned Aerial Vehicles (UAVs) increased their adoption in various applications, such as wildfire tracking, border monitoring, etc. In many critical applications, UAVs capture images and other sensory data and then send the captured data to remote servers for inference and data processing tasks. However, this approach is not always practical in real-time applications due to the connection instability, limited bandwidth, and end-to-end latency. One promising solution is to divide the inference requests into multiple parts (layers or segments), with each part being executed in a different UAV based on the available resources. Furthermore, some applications require the UAVs to traverse certain areas and capture incidents; thus, planning their paths becomes critical particularly, to reduce the latency of making the collaborative inference process. Specifically, planning the UAVs trajectory can reduce the data transmission latency by communicating with devices in the same proximity while mitigating the transmission interference. This work aims to design a model for distributed collaborative inference requests and path planning in a UAV swarm while respecting the resource constraints due to the computational load and memory usage of the inference requests. The model is formulated as an optimization problem and aims to minimize latency. The formulated problem is NP-hard so finding the optimal solution is quite complex; thus, this paper introduces a real-time and dynamic solution for online applications using deep reinforcement learning. We conduct extensive simulations and compare our results to the-state-of-the-art studies demonstrating that our model outperforms the competing models.

A Russian tank commander deliberately attacked his comrades after getting into an argument on the battlefield, according to an investigative report. The tank commander, whose identity was not revealed, drove his T-90 tank toward a group of Russian national guard troops following an argument. He then fired at the checkpoint and blew it up. The event, which happened in the Zaporizhzhia region over the summer, was recounted to The New York Times by Russian drone operator Fidar Khubaev. "Those types of things happen there," Khubaev told the outlet, adding that he escaped from the war in the fall.

Looking to get a free second phone number? CyberGuy shows how to get a second number at no additional cost. On one side of the coin, the invasion of privacy that can be the result of a neighbor hovering overhead with their new drone is unsettling. The other perspective is apparent the moment you put your own new drone into flight. Drones are incredibly useful for more than spying on neighbors.

Ukraine's capital has been targeted by a wave of drone attacks by Russia's military that again struck "critical infrastructure". About 20 drones were deployed to Kyiv and the surrounding area early Monday, according to officials, with air defence systems destroying about 15 of the unmanned aerial vehicles. Air raid sirens blasted in the early morning before the sky was declared clear at 5:50am (07:50 GMT). The Kyiv city administration said on its Telegram account that a critical infrastructure point was hit, but did not provide further details. "The enemy is attacking the capital," the administration said.