This letter presents a versatile trajectory planning pipeline for aerial tracking. The proposed tracker is capable of handling various chasing settings such as complex unstructured environments, crowded dynamic obstacles and multiple-target following. Among the entire pipeline, we focus on developing a predictor for future target motion and a chasing trajectory planner. For rapid computation, we employ the sample-check-select strategy: modules sample a set of candidate movements, check multiple constraints, and then select the best trajectory. Also, we leverage the properties of Bernstein polynomials for quick calculations. The prediction module predicts the trajectories of the targets, which do not overlap with static and dynamic obstacles. Then the trajectory planner outputs a trajectory, ensuring various conditions such as occlusion and collision avoidance, the visibility of all targets within a camera image and dynamical limits. We fully test the proposed tracker in simulations and hardware experiments under challenging scenarios, including dual-target following, environments with dozens of dynamic obstacles and complex indoor and outdoor spaces.

Microscopic traffic simulations are used to evaluate the impact of infrastructure modifications and evolving vehicle technologies, such as connected and automated driving. Simulated vehicles are controlled via car-following, lane-changing and junction models, which are designed to imitate human driving behavior. However, physics-based car-following models (CFMs) cannot fully replicate measured vehicle trajectories. Therefore, we present model extensions for the Intelligent Driver Model (IDM), of which some are already included in the Extended Intelligent Driver Model (EIDM), to improve calibration and validation results. They consist of equations based on vehicle dynamics and drive off procedures. In addition, parameter selection plays a decisive role. Thus, we introduce a framework to calibrate CFMs using drone data captured at a signalized intersection in Stuttgart, Germany. We compare the calibration error of the Krauss Model with the IDM and EIDM. In this setup, the EIDM achieves a 17.78 % lower mean error than the IDM, based on the distance difference between real world and simulated vehicles. Adding vehicle dynamics equations to the EIDM further improves the results by an additional 18.97 %. The calibrated vehicle-driver combinations are then investigated by simulating the traffic in three different scenarios: at the original intersection, in a closed loop and in a stop-and-go wave. The data shows that the improved calibration process of individual vehicles, openly available at https://www.github.com/stepeos/pycarmodel_calibration, also provides more accurate macroscopic results.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. handed over its last military base in Niger -- one of two crucial hubs for American counterterrorism operations in the country -- to local authorities, the U.S. Department of Defense and Niger's Ministry of Defense announced in a joint statement on Monday. The handing over of Airbase 201 in the city of Agadez came after the U.S. troops withdrew earlier this month from Airbase 101, a small drone base in Niger's capital of Niamey. U.S. troops have until Sept. 15 to leave the Sahel country following an agreement with Nigerien authorities.

In burgeoning domains, like urban goods distribution, the advent of aerial cargo transportation necessitates the development of routing solutions that prioritize safety. This paper introduces Larp, a novel path planning framework that leverages the concept of restrictive potential fields to forge routes demonstrably safer than those derived from existing methods. The algorithm achieves it by segmenting a potential field into a hierarchy of cells, each with a designated restriction zone determined by obstacle proximity. While the primary impetus behind Larp is to enhance the safety of aerial pathways for cargo-carrying Unmanned Aerial Vehicles (UAVs), its utility extends to a wide array of path planning scenarios. Comparative analyses with both established and contemporary potential field-based methods reveal Larp's proficiency in maintaining a safe distance from restrictions and its adeptness in circumventing local minima.

Whether you're watching one of your buddies or YouTuber Casey Neistat fly a quadcopter around, it's hard not to be a little envious of what they're seeing above. And we can help you do that--times two--with this BOGO drone deal. But you only have until midnight tonight (August 4) to get the Ninja Dragon Phantom K Pro and a free Blade X Pro for 139.97 (reg. You know what a drone can do, or you wouldn't be here, but if you need more facts about these Ninja models before you're convinced, have at it. They're not top-of-the-line, but they are great beginner drones since they have simple controls, altitude hold mode, and obstacle avoidance (so you don't hit a tree or something).

A marsupial robotics system comprises three components: an Unmanned Ground Vehicle (UGV), an Unmanned Aerial Vehicle (UAV), and a tether connecting both robots. Marsupial systems are highly beneficial in industry as they extend the UAV's battery life during flight. This paper introduces a novel strategy for a specific path planning problem in marsupial systems, where each of the components must avoid collisions with ground and aerial obstacles modeled as 3D cuboids. Given an initial configuration in which the UAV is positioned atop the UGV, the goal is to reach an aerial target with the UAV. We assume that the UGV first moves to a position from which the UAV can take off and fly through a vertical plane to reach an aerial target. We propose an approach that discretizes the space to approximate an optimal solution, minimizing the sum of the lengths of the ground and air paths. First, we assume a taut tether and use a novel algorithm that leverages the convexity of the tether and the geometry of obstacles to efficiently determine the locus of feasible take-off points for the UAV. We then apply this result to scenarios that involve loose tethers. The simulation test results show that our approach can solve complex situations in seconds, outperforming a baseline planning algorithm based on RRT* (Rapidly exploring Random Trees).

The Israeli military has stepped up near-daily raids on Palestinian cities and towns in the West Bank since the Oct. 7 Hamas-led attack on Israel. Israeli forces have also increasingly carried out deadly airstrikes in the territory, a rise in aerial attacks not seen since the second Palestinian uprising against Israeli occupation in the early 2000s. Before Oct. 7, airstrikes and drone attacks on the West Bank were rare and far more limited than in Gaza. But since the war in Gaza began, Israel has stepped up its aerial attacks on Palestinian areas. The two Palestinian territories have long been under separate governments.

Former DEA Chief of Operations Ray Donovan joins'America's Newsroom' to discuss Texas Gov. Greg Abbott's warning that cartels are utilizing drones along the southern border. The Mexican army has confirmed that drug cartel-operated bomb-dropping drones have killed soldiers in the western state of Michoacan. Defense Secretary Gen. Luis Cresencio Sandoval did not provide exact figures on the number of casualties suffered in the attacks, according to the Associated Press. Sandoval stated on Friday that attacks targeted patrol units and included over 260 drone-bomb incidents in 2023 alone. "Our personnel have suffered wounds, and some of our troops have even died" in the attacks, Sandoval said.

A mother and her daughter were killed by Russian shelling that hit the town of Nikopol in Ukraine's eastern Dnipropetrovsk region. Local governor Serhiy Lysak said private houses, a fire station, a college, a school and buses were damaged. Nikopol sits on the right bank of the Dnipro River Two people were injured by debris as Ukraine repelled a Russian drone attack on the region outside Kyiv. One of those hurt was Ilya Ponomaryov, a former Russian lawmaker who has lived in Ukraine for years and is a critic of the Kremlin. He wrote on Facebook that a drone exploded outside his front door, inflicting shrapnel wounds and causing a fire.

The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a Flexible REsource Scheduling (FRES) framework by employing a novel deep progressive reinforcement learning which includes the following innovations: Firstly, a novel multi-task agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multi-task agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Secondly, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework which can make real-time and optimal resource scheduling even in dynamic MEC systems.