David Oh, the Republican candidate for mayor of Philadelphia, shared how years of failed city policies have eliminated police officers' power in Kensington. WARNING: This story contains graphic images. PHILADELPHIA -- David Oh is frustrated with widespread, open-air drug use and high crime in the Kensington neighborhood. That's why the mayoral nominee has formed a plan aiming to clean up the streets and to save and protect its residents, helpless to stop addicts from stumbling through the streets in a stupor. "If we get rid of Kensington Avenue as a place that exists in this region, the better off people will be," Oh, a Republican, said.

Ukraine has increased its frequency of drone attacks on Russia in recent weeks, a tactic American officials say is intended to demonstrate to the Ukrainian public that Kyiv can still strike back, especially as the counteroffensive against entrenched Russian troops moves slowly. Over the past week, Ukrainian drones near Moscow forced the Kremlin to temporarily shut down airports serving the capital. And Friday, the Russian Ministry of Defense said Ukraine had launched 42 drones at the occupied Crimean Peninsula and fired a missile that was intercepted not far from Moscow, in what could be one of the biggest known aerial assaults on Russian-held territory since the war began. Throughout the summer, the intensifying strikes -- many of which have been carried out with Ukrainian-made drones -- have hit a building in central Moscow, an international airport and a supersonic bomber stationed south of St. Petersburg.

Taiwan's Defense Ministry said Saturday morning that it had detected 20 Chinese military aircraft entering the island's air defense identification zone over the last 24 hours, including a rare public mention of combat and spy drone flights along Taiwan's eastern coast. In addition to a TB-001 combat drone, known as the "twin-tailed scorpion" in China, and a BZK-005 surveillance drone, the ministry said the Chinese aircraft included J-10 and Su-30 fighter jets, as well as anti-submarine and reconnaissance aircraft. The TB-001, which has a maximum range of 6,000 kilometers and can carry missiles and precision guided bombs, flew to the north of Taiwan before heading southeast and then out into the Pacific and returning along rough the same path, a map released by the Taiwanese Defense Ministry showed.

Russian air defence has repelled a new drone attack on Moscow, the city's mayor said, the latest of several attempts to attack the Russian capital with unmanned aerial vehicles (UAVs) this week. Moscow Mayor Sergei Sobyanin said early on Saturday that a drone was destroyed by air defence systems over the Istra district west of Moscow. Emergency services were at the scene and there have been no initial reports of damage or casualties, Sobyanin said on the Telegram messaging app. Russia's military has reported that Ukrainian drones and a missile attack have repeatedly targeted Russian territory over the course of the past week, and three Moscow's airports – Sheremetyevo, Domodedovo and Vnukovo – suspended flights temporarily as a result. Flights were disrupted at Moscow's airports on Tuesday, Wednesday and Friday, according to reports.

As drones become increasingly prevalent in human life, they also raises security concerns such as unauthorized access and control, as well as collisions and interference with manned aircraft. Therefore, ensuring the ability to accurately detect and identify between different drones holds significant implications for coverage extension. Assisted by machine learning, radio frequency (RF) detection can recognize the type and flight mode of drones based on the sampled drone signals. In this paper, we first utilize Short-Time Fourier. Transform (STFT) to extract two-dimensional features from the raw signals, which contain both time-domain and frequency-domain information. Then, we employ a Convolutional Neural Network (CNN) built with ResNet structure to achieve multi-class classifications. Our experimental results show that the proposed ResNet-STFT can achieve higher accuracy and faster convergence on the extended dataset. Additionally, it exhibits balanced performance compared to other baselines on the raw dataset.

Russia's defence ministry has said its air defence forces destroyed a large-scale Ukrainian-launched drone attack on the Crimean Peninsula, which Moscow annexed from Ukraine in 2014. Crimea has been targeted by Kyiv since Moscow launched its full-scale invasion of Ukraine in February 2022, but has come under more intense, increased attacks in recent weeks. The Russian Ministry of Defence said early on Friday its forces shot down nine drones, while 33 others "were suppressed by electronic warfare and crashed without reaching the target". It did not elaborate on whether there had been any damage or casualties. It added that it had also shot down a Ukraine-launched missile over the Kaluga region, which borders the Moscow region.

In this paper, we investigate the operation of an aerial manipulator system, namely an Unmanned Aerial Vehicle (UAV) equipped with a controllable arm with two degrees of freedom to carry out actuation tasks on the fly. Our solution is based on employing a Q-learning method to control the trajectory of the tip of the arm, also called end-effector. More specifically, we develop a motion planning model based on Time To Collision (TTC), which enables a quadrotor UAV to navigate around obstacles while ensuring the manipulator's reachability. Additionally, we utilize a model-based Q-learning model to independently track and control the desired trajectory of the manipulator's end-effector, given an arbitrary baseline trajectory for the UAV platform. Such a combination enables a variety of actuation tasks such as high-altitude welding, structural monitoring and repair, battery replacement, gutter cleaning, skyscrapper cleaning, and power line maintenance in hard-to-reach and risky environments while retaining compatibility with flight control firmware. Our RL-based control mechanism results in a robust control strategy that can handle uncertainties in the motion of the UAV, offering promising performance. Specifically, our method achieves 92% accuracy in terms of average displacement error (i.e. the mean distance between the target and obtained trajectory points) using Q-learning with 15,000 episodes

Path Planning methods for autonomous control of Unmanned Aerial Vehicle (UAV) swarms are on the rise because of all the advantages they bring. There are more and more scenarios where autonomous control of multiple UAVs is required. Most of these scenarios present a large number of obstacles, such as power lines or trees. If all UAVs can be operated autonomously, personnel expenses can be decreased. In addition, if their flight paths are optimal, energy consumption is reduced. This ensures that more battery time is left for other operations. In this paper, a Reinforcement Learning based system is proposed for solving this problem in environments with obstacles by making use of Q-Learning. This method allows a model, in this particular case an Artificial Neural Network, to self-adjust by learning from its mistakes and achievements. Regardless of the size of the map or the number of UAVs in the swarm, the goal of these paths is to ensure complete coverage of an area with fixed obstacles for tasks, like field prospecting. Setting goals or having any prior information aside from the provided map is not required. For experimentation, five maps of different sizes with different obstacles were used. The experiments were performed with different number of UAVs. For the calculation of the results, the number of actions taken by all UAVs to complete the task in each experiment is taken into account. The lower the number of actions, the shorter the path and the lower the energy consumption. The results are satisfactory, showing that the system obtains solutions in fewer movements the more UAVs there are. For a better presentation, these results have been compared to another state-of-the-art approach.

This paper proposes a nonlinear stochastic complementary filter design for inertial navigation that takes advantage of a fusion of Ultra-wideband (UWB) and Inertial Measurement Unit (IMU) technology ensuring semi-global uniform ultimate boundedness (SGUUB) of the closed loop error signals in mean square. The proposed filter estimates the vehicle's orientation, position, linear velocity, and noise covariance. The filter is designed to mimic the nonlinear navigation motion kinematics and is posed on a matrix Lie Group, the extended form of the Special Euclidean Group $\mathbb{SE}_{2}\left(3\right)$. The Lie Group based structure of the proposed filter provides unique and global representation avoiding singularity (a common shortcoming of Euler angles) as well as non-uniqueness (a common limitation of unit-quaternion). Unlike Kalman-type filters, the proposed filter successfully addresses IMU measurement noise considering unknown upper-bounded covariance. Although the navigation estimator is proposed in a continuous form, the discrete version is also presented. Moreover, the unit-quaternion implementation has been provided in the Appendix. Experimental validation performed using a publicly available real-world six-degrees-of-freedom (6 DoF) flight dataset obtained from an unmanned Micro Aerial Vehicle (MAV) illustrating the robustness of the proposed navigation technique. Keywords: Sensor-fusion, Inertial navigation, Ultra-wideband ranging, Inertial measurement unit, Stochastic differential equation, Stability, Localization, Observer design.

This paper proposes a novel observer-based controller for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive measurements from a Vision-Aided Inertial Navigation System (VA-INS) and produce the required thrust and rotational torque inputs. The VA-INS is composed of a vision unit (monocular or stereo camera) and a typical low-cost 6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a gyroscope. A major benefit of this approach is its applicability for environments where the Global Positioning System (GPS) is inaccessible. The proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately estimate attitude (orientation), gyroscope bias, position, and linear velocity. Ability to use VA-INS measurements directly makes the proposed observer design more computationally efficient as it obviates the need for attitude and position reconstruction. Once the motion components are estimated, the observer-based controller is used to control the VTOL-UAV attitude, angular velocity, position, and linear velocity guiding the vehicle along the desired trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and the control errors of the observer-based controller are proven to be exponentially stable starting from almost any initial condition. To achieve global and unique VTOL-UAV representation in 6 DoF, the proposed approach is posed on the Lie Group and the design in unit-quaternion is presented. Although the proposed approach is described in a continuous form, the discrete version is provided and tested. Keywords: Vision-aided inertial navigation system, unmanned aerial vehicle, vertical take-off and landing, stochastic, noise, Robotics, control systems, air mobility, observer-based controller algorithm, landmark measurement, exponential stability.