In this work, we present an autonomous inspection framework for remote sensing tasks in active open-pit mines. Specifically, the contributions are focused towards developing a methodology where an initial approximate operator-defined inspection plan is exploited by an online view-planner to predict an inspection path that can adapt to changes in the current mine-face morphology caused by route mining activities. The proposed inspection framework leverages instantaneous 3D LiDAR and localization measurements coupled with modelled sensor footprint for view-planning satisfying desired viewing and photogrammetric conditions. The efficacy of the proposed framework has been demonstrated through simulation in Feiring-Bruk open-pit mine environment and hardware-based outdoor experimental trials. The video showcasing the performance of the proposed work can be found here: https://youtu.be/uWWbDfoBvFc

In the field of sensor fusion and state estimation for object detection and localization, ensuring accurate tracking in dynamic environments poses significant challenges. Traditional methods like the Kalman Filter (KF) often fail when measurements are intermittent, leading to rapid divergence in state estimations. To address this, we introduce SMART (Sensor Measurement Augmentation and Reacquisition Tracker), a novel approach that leverages high-frequency state estimates from the KF to guide the search for new measurements, maintaining tracking continuity even when direct measurements falter. This is crucial for dynamic environments where traditional methods struggle. Our contributions include: 1) Versatile Measurement Augmentation Using KF Feedback: We implement a versatile measurement augmentation system that serves as a backup when primary object detectors fail intermittently. This system is adaptable to various sensors, demonstrated using depth cameras where KF's 3D predictions are projected into 2D depth image coordinates, integrating nonlinear covariance propagation techniques simplified to first-order approximations. 2) Open-source ROS2 Implementation: We provide an open-source ROS2 implementation of the SMART-TRACK framework, validated in a realistic simulation environment using Gazebo and ROS2, fostering broader adaptation and further research. Our results showcase significant enhancements in tracking stability, with estimation RMSE as low as 0.04 m during measurement disruptions, advancing the robustness of UAV tracking and expanding the potential for reliable autonomous UAV operations in complex scenarios. The implementation is available at https://github.com/mzahana/SMART-TRACK.

This study proposes a novel artificial intelligence (AI) driven flight computer, integrating an online free-retraining-prediction model, a swarm control, and an obstacle avoidance strategy, to track dynamic targets using a distributed drone swarm for military applications. To enable dynamic target tracking the swarm requires a trajectory prediction capability to achieve intercept allowing for the tracking of rapid maneuvers and movements while maintaining efficient path planning. Traditional predicative methods such as curve fitting or Long ShortTerm Memory (LSTM) have low robustness and struggle with dynamic target tracking in the short term due to slow convergence of single agent-based trajectory prediction and often require extensive offline training or tuning to be effective. Consequently, this paper introduces a novel robust adaptive bidirectional fuzzy brain emotional learning prediction (BFBEL-P) methodology to address these challenges. The controller integrates a fuzzy interface, a neural network enabling rapid adaption, predictive capability and multi-agent solving enabling multiple solutions to be aggregated to achieve rapid convergence times and high accuracy in both the short and long term. This was verified through the use of numerical simulations seeing complex trajectory being predicted and tracked by a swarm of drones. These simulations show improved adaptability and accuracy to state of the art methods in the short term and strong results over long time domains, enabling accurate swarm target tracking and predictive capability.

Israeli rescue services say nearly 60 people were injured, some critically, in a drone strike in Binyamina on Sunday, which Hezbollah has claimed responsibility for. Rescue services in Israel said over 60 people were wounded, some of them critically, in a drone strike in Binyamina, Israel, which the Lebanon-based Hezbollah militant group has claimed responsibility for, according to reports. Israeli media reported that two drones were launched from Lebanon, one of which was intercepted. Who was hurt – whether military members or civilians – or what was struck was not immediately clear. On Thursday, Israel conducted two strikes in Beirut that killed 22 people, and Hezbollah said it was retaliating for the strikes by targeting an Israeli military training camp.

At least 67 people have been wounded in a drone attack in northern Israel, according to Israeli emergency services and local media, as the Lebanese armed group Hezbollah said it had targeted an Israeli military camp with a "swarm" of drones. Israeli Army Radio reported that at least four people were critically wounded in the attack on Sunday in the town of Binyamina, south of Haifa. According to Israel's Channel 12, no warning sirens were heard before the attack. Hezbollah claimed responsibility for the strike. In a statement, the Iran-aligned group said it launched a "swarm of drones" at a Golani Brigade camp.

The group said it targeted the camp in northern Israel using a "swarm of drones". Israeli censorship rules prevent media outlets saying exactly where or what was targeted, but some media outlets say the location was hit by a low-level drone launched from Lebanon - a relatively unsophisticated weapon that appears not to have activated early warning alarms. Footage carried by Israeli media showed those wounded being helped into emergency vehicles, including helicopters. Israeli media reported that 67 people have been injured - with four in a critical condition and five others seriously wounded. Many of the wounded have been evacuated to Hillel Yaffe Medical Centre in nearby Hadera - with others being taken to hospitals in Tel Hashomer, Haifa, Afula and Netanya.

Ukraine's human rights ombudsman has urged international organisations to respond to an allegation that several Ukrainian prisoners of war were executed in Russia's Kursk region. DeepState, a Ukrainian battlefield analysis site close to Ukraine's Defence Ministry, said that Russian troops killed nine Ukrainian "drone operators and contractors" on October 10 after they had surrendered. Dmytro Lubinets said on Sunday that the international community "must not turn a blind eye" to the alleged executions. The ombudsman wrote on Telegram that he had sent letters to the United Nations and the International Red Cross about the incident, referring to it as "another crime committed by the Russians". Ukrainian Prosecutor-General Andriy Kostin also said his office had opened a criminal investigation into the alleged execution and said killing prisoners of war was a "gross violation" of the Geneva Convention.

A mysterious fleet of drones entered restricted airspace and swarmed a U.S. military base along the Virginia coast for 17 days late last year, stumping the Pentagon, according to a new report. For several nights last December, U.S. military personnel reported witnessing a fleet of unknown unmanned aircraft breach restricted airspace over a stretch of land at Langley Air Force Base along Virginia's shore, the Wall Street Journal first reported. The drones would start to arrive about 45 minutes to an hour after sunset each night, one official reportedly told U.S. Air Force Gen. Mark Kelly, who joined several other officers responsible for the country's most advanced jet fighters, including F-22 Raptors, on a squadron rooftop. Kelly described the first drone he saw as roughly 20 feet long and flying at more than 100 miles an hour, at an altitude of roughly 3,000 to 4,000 feet. As many as a dozen or more drones followed, flying across Chesapeake Bay, and then traveling toward Norfolk, Virginia, and through a space overlooking the base for the Navy's SEAL Team Six and Naval Station Norfolk, the world's largest naval port, according to the Journal.

Motion planning is an essential process for the navigation of unmanned aerial vehicles (UAVs) where they need to adapt to obstacles and different structures of their operating environment to reach the goal. This paper presents an optimal motion planner for UAVs operating in unknown complex environments. The motion planner receives point cloud data from a local range sensor and then converts it into a voxel grid representing the surrounding environment. A local trajectory guiding the UAV to the goal is then generated based on the voxel grid. This trajectory is further optimized using model predictive control (MPC) to enhance the safety, speed, and smoothness of UAV operation. The optimization is carried out via the definition of several cost functions and constraints, taking into account the UAV's dynamics and requirements. A number of simulations and comparisons with a state-of-the-art method have been conducted in a complex environment with many obstacles to evaluate the performance of our method. The results show that our method provides not only shorter and smoother trajectories but also faster and more stable speed profiles. It is also energy efficient making it suitable for various UAV applications.

Abstract-- We present an integrated UAV-hexapod robotic system designed for GNSS-denied maritime operations, capable of autonomous deployment and retrieval of a hexapod robot via a winch mechanism installed on a UAV. This system is intended to address the challenges of localization, control, and mobility in dynamic maritime environments. Experimental results demonstrate the effectiveness of this system in real-world scenarios, validating its performance during field tests in both controlled and operational conditions in the MBZIRC 2023 Maritime Challenge. I. INTRODUCTION Unmanned Aerial Vehicles (UAVs) have become an essential component of modern robotics, widely used in various applications, including surveillance, inspection, search and Figure 1: UAV-Hexapod system executing its mission in a rescue, and transportation. Their ability to fly over challenging GNSS-denied maritime environment. Team KAIST won 2nd terrains and access remote areas has expanded the place in the MBZIRC 2023 Maritime Challenge.