The United States has hundreds of soldiers in Syria, mostly in the northeast, part of its mission to fight the remnants of the Islamic State alongside its ally, Kurdish-led forces. The Syrian government of President Bashar al-Assad has long demanded that the United States withdraw from all parts of Syria. The Syrian Army's general command said it "considers this cowardly terrorist act an unprecedented criminal act and affirms that it will respond with full force and decisiveness to these terrorist organizations wherever they are found," according to the Syrian state media. Syrian government forces carried out artillery and missile attacks after the drone strike on Thursday, targeting several towns in the country's northwestern Idlib Province and killing at least eight people, according to the Syrian Observatory for Human Rights. That part of the country is under the control of armed groups not backed by the United States.

Syrians have begun burying the dozens of people killed in a large-scale drone attack on a military academy in the western city of Homs. Coffins draped in Syrian flags on Friday lined the streets outside the Homs military hospital as a military band played somber music and soldiers saluted. On Thursday, several drones attacked a graduation ceremony in the academy's courtyard, where families had gathered with the new officers. Syria's Ministry of Health said at least 89 people had been killed, including 31 women and five children. The Syrian Observatory for Human Rights, which monitors the Syrian conflict, put the toll at above 120.

Unmanned Aerial Vehicles (UAVs) are considered cutting-edge technology with highly cost-effective and flexible usage scenarios. Although many papers have reviewed the application of UAVs in agriculture, the review of the application for tree detection is still insufficient. This paper focuses on tree detection methods applied to UAV data collected by UAVs. There are two kinds of data, the point cloud and the images, which are acquired by the Light Detection and Ranging (LiDAR) sensor and camera, respectively. Among the detection methods using point-cloud data, this paper mainly classifies these methods according to LiDAR and Digital Aerial Photography (DAP). For the detection methods using images directly, this paper reviews these methods by whether or not to use the Deep Learning (DL) method. Our review concludes and analyses the comparison and combination between the application of LiDAR-based and DAP-based point cloud data. The performance, relative merits, and application fields of the methods are also introduced. Meanwhile, this review counts the number of tree detection studies using different methods in recent years. From our statics, the detection task using DL methods on the image has become a mainstream trend as the number of DL-based detection researches increases to 45% of the total number of tree detection studies up to 2022. As a result, this review could help and guide researchers who want to carry out tree detection on specific forests and for farmers to use UAVs in managing agriculture production.

The Close Enough Traveling Salesman Problem (CETSP) is a well-known variant of the classic Traveling Salesman Problem whereby the agent may complete its mission at any point within a target neighborhood. Heuristics based on overlapped neighborhoods, known as Steiner Zones (SZ), have gained attention in addressing CETSPs. While SZs offer effective approximations to the original graph, their inherent overlap imposes constraints on the search space, potentially conflicting with global optimization objectives. Here we present the Close Enough Orienteering Problem with Non-uniform Neighborhoods (CEOP-N), which extends CETSP by introducing variable prize attributes and non-uniform cost considerations for prize collection. To tackle CEOP-N, we develop a new approach featuring a Randomized Steiner Zone Discretization (RSZD) scheme coupled with a hybrid algorithm based on Particle Swarm Optimization (PSO) and Ant Colony System (ACS) - CRaSZe-AntS. The RSZD scheme identifies sub-regions for PSO exploration, and ACS determines the discrete visiting sequence. We evaluate the RSZD's discretization performance on CEOP instances derived from established CETSP instances, and compare CRaSZe-AntS against the most relevant state-of-the-art heuristic focused on single-neighborhood optimization for CEOP. We also compare the performance of the interior search within SZs and the boundary search on individual neighborhoods in the context of CEOP-N. Our results show CRaSZe-AntS can yield comparable solution quality with significantly reduced computation time compared to the single-neighborhood strategy, where we observe an averaged 140.44% increase in prize collection and 55.18% reduction of execution time. CRaSZe-AntS is thus highly effective in solving emerging CEOP-N, examples of which include truck-and-drone delivery scenarios.

The remarkable growth of unmanned aerial vehicles (UAVs) has also sparked concerns about safety measures during their missions. To advance towards safer autonomous aerial robots, this work presents a vision-based solution to ensuring safe autonomous UAV landings with minimal infrastructure. During docking maneuvers, UAVs pose a hazard to people in the vicinity. In this paper, we propose the use of a single omnidirectional panoramic camera pointing upwards from a landing pad to detect and estimate the position of people around the landing area. The images are processed in real-time in an embedded computer, which communicates with the onboard computer of approaching UAVs to transition between landing, hovering or emergency landing states. While landing, the ground camera also aids in finding an optimal position, which can be required in case of low-battery or when hovering is no longer possible. We use a YOLOv7-based object detection model and a XGBooxt model for localizing nearby people, and the open-source ROS and PX4 frameworks for communication, interfacing, and control of the UAV. We present both simulation and real-world indoor experimental results to show the efficiency of our methods.

In a separate development in Syria on Thursday, at least 10 people were reportedly killed in Turkish drone strikes in a Kurdish-controlled region of north-eastern Syria that were prompted by a bomb attack in Ankara claimed by Kurdish militants.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. At least seven people have reportedly been killed in Syria Thursday after a drone attack targeted a military academy graduation ceremony in Homs. "Seven dead and more than 20 regime officers wounded in a violent explosion in the area of the military academy in Homs, caused by an attack by drones during a graduation ceremony," the Syrian Observatory for Human Rights said, according to the AFP. The source of the attack is unclear.

A drone attack on a military college in Syria's Homs province during a graduation ceremony has killed and wounded civilians and military personnel, according to Syrian officials. Drones laden with explosives targeted the ceremony on Thursday as it came to an end, Syria's military said in a statement. They accused fighters "backed by known international forces" for the attack. The statement did not specify the number of casualties but said some of the wounded were in critical condition, including women and children. There was no immediate claim of responsibility.

We propose a motion planner for cable-driven payload transportation using multiple unmanned aerial vehicles (UAVs) in an environment cluttered with obstacles. Our planner is kinodynamic, i.e., it considers the full dynamics model of the transporting system including actuation constraints. Due to the high dimensionality of the planning problem, we use a hierarchical approach where we first solve the geometric motion planning using a sampling-based method with a novel sampler, followed by constrained trajectory optimization that considers the full dynamics of the system. Both planning stages consider inter-robot and robot/obstacle collisions. We demonstrate in a software-in-the-loop simulation that there is a significant benefit in kinodynamic motion planning for such payload transport systems with respect to payload tracking error and energy consumption compared to the standard methods of planning for the payload alone. Notably, we observe a significantly higher success rate in scenarios where the team formation changes are needed to move through tight spaces.

We present an online centralized path planning algorithm to cover a large, complex, unknown workspace with multiple homogeneous mobile robots. Our algorithm is horizon-based, synchronous, and on-demand. The recently proposed horizon-based synchronous algorithms compute all the robots' paths in each horizon, significantly increasing the computation burden in large workspaces with many robots. As a remedy, we propose an algorithm that computes the paths for a subset of robots that have traversed previously computed paths entirely (thus on-demand) and reuses the remaining paths for the other robots. We formally prove that the algorithm guarantees complete coverage of the unknown workspace. Experimental results on several standard benchmark workspaces show that our algorithm scales to hundreds of robots in large complex workspaces and consistently beats a state-of-the-art online centralized multi-robot coverage path planning algorithm in terms of the time needed to achieve complete coverage. For its validation, we perform ROS+Gazebo simulations in five 2D grid benchmark workspaces with 10 Quadcopters and 10 TurtleBots, respectively. Also, to demonstrate its practical feasibility, we conduct one indoor experiment with two real TurtleBot2 robots and one outdoor experiment with three real Quadcopters.