The application of Multiple Unmanned Aerial Vehicles (Multi-UAV) in Wilderness Search and Rescue (WiSAR) significantly enhances mission success due to their rapid coverage of search areas from high altitudes and their adaptability to complex terrains. This capability is particularly crucial because time is a critical factor in searching for a lost person in the wilderness; as time passes, survival rates decrease and the search area expands. The probability of success in such searches can be further improved if UAVs leverage terrain features to predict the lost person's position. In this paper, we aim to enhance search missions by proposing a smart agent-based probability model that combines Monte Carlo simulations with an agent strategy list, mimicking the behavior of a lost person in the wildness areas. Furthermore, we develop a distributed Multi-UAV receding horizon search strategy with dynamic partitioning, utilizing the generated probability density model as prior information to prioritize locations where the lost person is most likely to be found. Simulated search experiments across different terrains have been conducted to validate the search efficiency of the proposed methods compared to other benchmark methods.

Autonomous multi-agent systems such as hospital robots and package delivery drones often operate in highly uncertain environments and are expected to achieve complex temporal task objectives while ensuring safety. While learning-based methods such as reinforcement learning are popular methods to train single and multi-agent autonomous systems under user-specified and state-based reward functions, applying these methods to satisfy trajectory-level task objectives is a challenging problem. Our first contribution is the use of weighted automata to specify trajectory-level objectives, such that, maximal paths induced in the weighted automaton correspond to desired trajectory-level behaviors. We show how weighted automata-based specifications go beyond timeliness properties focused on deadlines to performance properties such as expeditiousness. Our second contribution is the use of evolutionary game theory (EGT) principles to train homogeneous multi-agent teams targeting homogeneous task objectives. We show how shared experiences of agents and EGT-based policy updates allow us to outperform state-of-the-art reinforcement learning (RL) methods in minimizing path length by nearly 30\% in large spaces. We also show that our algorithm is computationally faster than deep RL methods by at least an order of magnitude. Additionally our results indicate that it scales better with an increase in the number of agents as compared to other methods.

The New York police department (NYPD) announced it will begin using drones to respond to reports of robberies and alerts from a city-wide gunshot detection system. The drones will fly to the scene, piloted by an NYPD officer, and record video and audio that will be sent to police officers' smartphones in real time, according to a press release. The integration of these two surveillance technologies is part of a broader "Drone as First Responder" program that has existed since 2018. The New York city mayor, Eric Adams, and the city's interim police commissioner, Tom Donlan, announced the expansion on Wednesday afternoon. It will be initially rolled out to five precincts in Brooklyn, the Bronx and Manhattan.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Canada Soccer has parted ways with Bev Priestman. The decision to relieve Priestman of her coaching duties comes after an independent review was launched into her role in a drone surveillance scandal at this past summer's Olympics in Paris. Assistant coach Jasmine Mander and analyst Joseph Lombardi were also relieved of duty as Canada Soccer released the findings of the investigation.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Canada Soccer has parted ways with Bev Priestman. The decision to relieve Priestman of her coaching duties comes after an independent review was launche into her role in a drone surveillance scandal at this past summer's Olympics in Paris. Assistant coach Jasmine Mander and analyst Joseph Lombardi were also relieved of duty as Canada Soccer released the findings of the investigation.

US warships came under sustained missile and drone attack from Houthi fighters as they sailed off the coast of Yemen, the Pentagon has confirmed, with the armed group claiming it attacked the US aircraft carrier Abraham Lincoln and two US destroyers. Pentagon spokesperson Air Force Major General Patrick Ryder said on Tuesday that the United States military's Central Command (CENTCOM) forces "successfully repelled multiple Iranian backed Houthi attacks during a transit of the Bab al-Mandeb strait", which connects the Red Sea to the Gulf of Aden. Ryder told reporters at a news conference that two US-guided missile destroyers – the USS Stockdale and USS Spruance – were attacked by at least eight one-way attack drones, five antiship ballistic missiles and three antiship cruise missiles. All the Houthi drones and missiles "were successfully engaged and defeated", and neither of the US Navy ships were damaged or personnel hurt, he said. Ryder added that he was not aware of any attacks against the aircraft carrier USS Abraham Lincoln.

Russia is launching mass drone strikes on Ukraine. A 14-year-old girl was one of the latest victims.

Drone detection has benefited from improvements in deep neural networks, but like many other applications, suffers from the availability of accurate data for training. Synthetic data provides a potential for low-cost data generation and has been shown to improve data availability and quality. However, models trained on synthetic datasets need to prove their ability to perform on real-world data, known as the problem of sim-to-real transferability. Here, we present a drone detection Faster-RCNN model trained on a purely synthetic dataset that transfers to real-world data. We found that it achieves an AP_50 of 97.0% when evaluated on the MAV-Vid - a real dataset of flying drones - compared with 97.8% for an equivalent model trained on real-world data. Our results show that using synthetic data for drone detection has the potential to reduce data collection costs and improve labelling quality. These findings could be a starting point for more elaborate synthetic drone datasets. For example, realistic recreations of specific scenarios could de-risk the dataset generation of safety-critical applications such as the detection of drones at airports. Further, synthetic data may enable reliable drone detection systems, which could benefit other areas, such as unmanned traffic management systems. The code is available https://github.com/mazqtpopx/cranfield-synthetic-drone-detection alongside the datasets https://huggingface.co/datasets/mazqtpopx/cranfield-synthetic-drone-detection.

Vision-and-Language Navigation (VLN), as a widely discussed research direction in embodied intelligence, aims to enable embodied agents to navigate in complicated visual environments through natural language commands. Most existing VLN methods focus on indoor ground robot scenarios. However, when applied to UAV VLN in outdoor urban scenes, it faces two significant challenges. First, urban scenes contain numerous objects, which makes it challenging to match fine-grained landmarks in images with complex textual descriptions of these landmarks. Second, overall environmental information encompasses multiple modal dimensions, and the diversity of representations significantly increases the complexity of the encoding process. To address these challenges, we propose NavAgent, the first urban UAV embodied navigation model driven by a large Vision-Language Model. NavAgent undertakes navigation tasks by synthesizing multi-scale environmental information, including topological maps (global), panoramas (medium), and fine-grained landmarks (local). Specifically, we utilize GLIP to build a visual recognizer for landmark capable of identifying and linguisticizing fine-grained landmarks. Subsequently, we develop dynamically growing scene topology map that integrate environmental information and employ Graph Convolutional Networks to encode global environmental data. In addition, to train the visual recognizer for landmark, we develop NavAgent-Landmark2K, the first fine-grained landmark dataset for real urban street scenes. In experiments conducted on the Touchdown and Map2seq datasets, NavAgent outperforms strong baseline models. The code and dataset will be released to the community to facilitate the exploration and development of outdoor VLN.

From prehistoric encirclement for hunting to GPS orbiting the earth for positioning, target encirclement has numerous real world applications. However, encircling multiple non-cooperative targets in GPS-denied environments remains challenging. In this work, multiple targets encirclement by using a minimum of two tasking agents, is considered where the relative distance measurements between the agents and the targets can be obtained by using onboard sensors. Based on the measurements, the center of all the targets is estimated directly by a fuzzy wavelet neural network (FWNN) and the least squares fit method. Then, a new distributed anti-synchronization controller (DASC) is designed so that the two tasking agents are able to encircle all targets while staying opposite to each other. In particular, the radius of the desired encirclement trajectory can be dynamically determined to avoid potential collisions between the two agents and all targets. Based on the Lyapunov stability analysis method, the convergence proofs of the neural network prediction error, the target-center position estimation error, and the controller error are addressed respectively. Finally, both numerical simulations and UAV flight experiments are conducted to demonstrate the validity of the encirclement algorithms. The flight tests recorded video and other simulation results can be found in https://youtu.be/B8uTorBNrl4.