The hype around delivery robots has fizzled, but DoorDash is still determined to launch Dot, an adorable red bot. It can ride on roads and in bike lanes, where it will face daunting challenges. DoorDash's new delivery robot is named Dot. When we first got close to Dot, DoorDash's new delivery robot, we looked right into its big blue, pixelated eyes and gave it a kick. It's not WIRED's policy to be mean to 350-pound hunks of plastic on wheels.

An entire family - a married couple and their two young sons - have been killed in an overnight Russian drone attack in Ukraine's north-eastern Sumy region, local officials have said. Regional head Oleh Hryhorov said a residential building was hit in the village of Chernechchyna. The bodies of the two children, aged four and six, and their parents were later recovered from the wreckage. Ukraine's air force said its units shot down 46 out of 65 Russian drones across the country - but there were 19 direct hits in six locations. Russia's military has not commented.

The FAA is set to loosen rules to let people fly drones beyond their "line of sight. On Thursday, I published a story about the police-tech giant Flock Safety selling its drones to the private sector to track shoplifters. Keith Kauffman, a former police chief who now leads Flock's drone efforts, described the ideal scenario: A security team at a Home Depot, say, launches a drone from the roof that follows shoplifting suspects to their car. The drone tracks their car through the streets, transmitting its live video feed directly to the police. It's a vision that, unsurprisingly, alarms civil liberties advocates. They say it will expand the surveillance state created by police drones, license-plate readers, and other crime tech, which has allowed law enforcement to collect massive amounts of private data without warrants.

Can Ukraine restore its pre-war borders? Why are Tomahawk missiles for Ukraine a'red line' for Russia? Is Russia testing NATO with aerial incursions in Europe? At least 4 killed in major Russian drone, missile attack on Ukraine's Kyiv Russia's President Vladimir Putin said his forces are prevailing in what he described as a "righteous battle" in Ukraine . "Our fighters and commanders go on the attack, and the entire country, all of Russia, is waging this righteous battle and working hard," he said.

Prime Minister Shigeru Ishiba (right) attends a joint news conference with Sri Lankan President Anura Kumara Dissanayake at the Prime Minister's Office on Monday. Prime Minister Shigeru Ishiba and Sri Lankan President Anura Kumara Dissanayake met in Tokyo on Monday and agreed that Japan will provide about 10 surveillance drones, worth about ¥500 million ($3.36 million), to the South Asian nation's navy. This will be Japan's first provision of defense equipment to Sri Lanka under its official security assistance program. The stability and development of Sri Lanka, which is located at a strategic point in the Indian Ocean, is extremely important, Ishiba said at a joint news conference after the meeting. In response, the president voiced his commitment to creating a peaceful and stable Indo-Pacific region.

Abstract--In this paper, we propose a new modeling approach and a fast algorithm for 3D motion planning, applicable for fixed-wing unmanned aerial vehicles. The goal is to construct the shortest path connecting given initial and final configurations subject to motion constraints. Our work differs from existing literature in two ways. First, we consider full vehicle orientation using a body-attached frame, which includes roll, pitch, and yaw angles. However, existing work uses only pitch and/or heading angle, which is insufficient to uniquely determine orientation. Second, we use two control inputs to represent bounded pitch and yaw rates, reflecting control by two separate actuators. In contrast, most previous methods rely on a single input, such as path curvature, which is insufficient for accurately modeling the vehicle's kinematics in 3D. We use a rotation minimizing frame to describe the vehicle's configuration and its evolution, and construct paths by concatenating optimal Dubins paths on spherical, cylindrical, or planar surfaces. Numerical simulations show our approach generates feasible paths within 10 seconds on average and yields shorter paths than existing methods in most cases. HE use of Unmanned Aerial V ehicles (UA Vs) is rapidly growing in civilian and military applications, including search and rescue and surveillance. Fixed-wing UA Vs are of particular interest due to longer flight times, larger payload capacity, and the ability to fly at higher altitudes [1], [2]. However, they are persistently in motion, i.e., cannot stop or hover mid-air, and cannot change their heading angle instantaneously. Hence, they have a bound on the rate of change of their heading/orientation, which manifests itself as curvature constraints on the path.

Multispectral object detection is an important application for unmanned aerial vehicles (UAVs). However, it faces several challenges. First, low-light RGB images weaken the multispectral fusion due to details loss. Second, the interference information is introduced to local target modeling during multispectral fusion. Third, computational cost poses deployment challenge on UAV platforms, such as transformer-based methods with quadratic complexity. To address these issues, a framework named DEPFusion consisting of two designed modules, Dual-Domain Enhancement (DDE) and Priority-Guided Mamba Fusion (PGMF) , is proposed for UAV multispectral object detection. Firstly, considering the adoption of low-frequency component for global brightness enhancement and frequency spectra features for texture-details recovery, DDE module is designed with Cross-Scale Wavelet Mamba (CSWM) block and Fourier Details Recovery (FDR) block. Secondly, considering guiding the scanning of Mamba from high priority score tokens, which contain local target feature, a novel Priority-Guided Serialization is proposed with theoretical proof. Based on it, PGMF module is designed for multispectral feature fusion, which enhance local modeling and reduce interference information. Experiments on DroneVehicle and VEDAI datasets demonstrate that DEPFusion achieves good performance with state-of-the-art methods.

The deployment of unmanned aerial vehicle (UAV) swarm-assisted communication networks has become an increasingly vital approach for remediating coverage limitations in infrastructure-deficient environments, with especially pressing applications in temporary scenarios, such as emergency rescue, military and security operations, and remote area coverage. However, complex geographic environments lead to unpredictable and highly dynamic wireless channel conditions, resulting in frequent interruptions of air-to-ground (A2G) links that severely constrain the reliability and quality of service in UAV swarm-assisted mobile communications. To improve the quality of UAV swarm-assisted communications in complex geographic environments, we propose an integrated communication and control co-design mechanism. Given the stringent energy constraints inherent in UAV swarms, our proposed mechanism is designed to optimize energy efficiency while maintaining an equilibrium between equitable communication rates for mobile ground users (GUs) and UAV energy expenditure. We formulate the joint resource allocation and 3D trajectory control problem as a Markov decision process (MDP), and develop a multi-agent reinforcement learning (MARL) framework to enable real-time coordinated actions across the UAV swarm. To optimize the action policy of UAV swarms, we propose a novel multi-agent hybrid proximal policy optimization with action masking (MAHPPO-AM) algorithm, specifically designed to handle complex hybrid action spaces. The algorithm incorporates action masking to enforce hard constraints in high-dimensional action spaces. Experimental results demonstrate that our approach achieves a fairness index of 0.99 while reducing energy consumption by up to 25% compared to baseline methods.

Search and rescue operations require unmanned aerial vehicles to both traverse unknown unstructured environments at high speed and track targets once detected. Achieving both capabilities under degraded sensing and without global localization remains an open challenge. Recent works on relative navigation have shown robust tracking by anchoring planning and control to a visible detected object, but cannot address navigation when no target is in the field of view. We present HUNT (High-speed UAV Navigation and Tracking), a real-time framework that unifies traversal, acquisition, and tracking within a single relative formulation. HUNT defines navigation objectives directly from onboard instantaneous observables such as attitude, altitude, and velocity, enabling reactive high-speed flight during search. Once a target is detected, the same perception-control pipeline transitions seamlessly to tracking. Outdoor experiments in dense forests, container compounds, and search-and-rescue operations with vehicles and mannequins demonstrate robust autonomy where global methods fail.

Abstract-- We present a cooperative aerial-ground search-and-rescue (SAR) framework that pairs two unmanned aerial vehicles (UA Vs) with an unmanned ground vehicle (UGV) to achieve rapid victim localization and obstacle-aware navigation in unknown environments. In our framework, a goal-searching UA V executes real-time onboard victim detection and georeferencing to nominate goals for the ground platform, while a terrain-scouting UA V flies ahead of the UGV's planned route to provide mid-level traversability updates. The UGV fuses aerial cues with local sensing to perform time-efficient A* planning and continuous replanning as information arrives. Additionally, we present a hardware demonstration (using a GEM e6 golf cart as the UGV and two X500 UA Vs) to evaluate end-to-end SAR mission performance and include simulation ablations to assess the planning stack in isolation from detection. Empirical results demonstrate that explicit role separation across UA Vs, coupled with terrain scouting and guided planning, improves reach time and navigation safety in time-critical SAR missions. Search and rescue (SAR) operations stand to benefit from recent advances in autonomous aerial and ground robotics. Unmanned Aerial V ehicles (UA Vs) enable rapid, large-area coverage due to their agility and mobility. The adoption of drones across civilian and military applications has highlighted advantages in speed and perspective.