In disaster response scenarios, deploying robotic teams effectively is crucial for improving situational awareness and enhancing search and rescue operations. The use of robots in search and rescue has been studied but the question of where to start robot deployments has not been addressed. This work addresses the problem of optimally selecting starting locations for robots with heterogeneous capabilities by formulating a joint optimization problem. To determine start locations, this work adds a constraint to the ergodic optimization framework whose minimum assigns robots to start locations. This becomes a little more challenging when the robots are heterogeneous (equipped with different sensing and motion modalities) because not all robots start at the same location, and a more complex adaptation of the aforementioned constraint is applied. Our method assumes access to potential starting locations, which can be obtained from expert knowledge or aerial imagery. We experimentally evaluate the efficacy of our joint optimization approach by comparing it to baseline methods that use fixed starting locations for all robots. Our experimental results show significant gains in coverage performance, with average improvements of 35.98% on synthetic data and 31.91% on real-world data for homogeneous and heterogeneous teams, in terms of the ergodic metric.

A House Democrat is moving to block the Trump administration from being able to use military-grade drones to surveil protests in the U.S. Rep. Jimmy Gomez, D-Calif., introduced the bill in response to the Department of Homeland Security (DHS) reportedly using MQ-9 Reaper drones to monitor the protests in Los Angeles earlier this year. "The U.S. government should never use military drones to spy on its own people. Not under anyone," Gomez told Fox News Digital in a statement. "This bill would stop Trump's abuse of power and get these combat drones out of our neighborhoods." An MQ-9 Reaper flies by on a training mission at Creech Air Force Base in Indian Springs, Nevada.

Russia launched a barrage of missile and drone attacks on Kyiv, killing at least six people – including a six-year-old boy and his mother – and injuring 52, according to Ukrainian officials. President Volodymyr Zelenskyy said on Thursday that Russian forces launched more than 300 drones and eight missiles, targeting residential buildings throughout the capital. "Today the world has once again seen Russia's response to our desire for peace with America and Europe. Therefore, peace without strength is impossible," Zelenskyy said on the Telegram app. Kyiv mayor Vitali Klitschko confirmed nine children were wounded in the attack, the highest number of child casualties in a single night in the city since Russia's full-scale invasion began nearly three and a half years ago.

A Russian drone and missile attack on Ukraine's capital has killed at least six people, including a six-year-old boy, according to Ukrainian President Volodymyr Zelenskyy and other officials. The overnight attack wounded at least 52 people and caused damage at 27 locations across four districts of Kyiv, city military administrator Tymur Tkachenko said on Thursday as casualty numbers are expected to rise. Rescue teams were at the scene to search for people trapped under the rubble. Russia's latest deadly attack on Ukraine came after United States President Donald Trump on Monday issued a 10- or 12-day ultimatum to Moscow to halt its invasion of Ukraine, now in its fourth year, or face sanctions. Zelenskyy said on Thursday that Russia had used more than 300 drones and eight missiles in the attack as he posted a video of burning ruins on social media.

Russian drone strikes on Ukraine's capital, Kyiv, on Wednesday night killed several people, including a six-year-old boy, as multiple locations across the city were hit including a hospital and a residential building.

A Russian drone attack in the Ukrainian capital Kyiv has killed at least four people, the Ground Forces of the Armed Forces of Ukraine said on Telegram. The debris from downed Russian drones fell near a garage cooperative, which led to the ignition of a gas pipe in a three-storey residential building. A Russian missile strike on a Ukrainian military training unit on Tuesday killed three servicemen and injured 18 more, Ukraine's Ground Forces also announced on Telegram. Russian forces shelled Ukrainian emergency service workers who had just put out a fire in the city of Orikhiv in southern Ukraine's Zaporizhia region, the State Emergency Service of Ukraine said in a post on Facebook. Ukraine's domestic security agency has detained an air force officer holding the rank of major on charges of having spied for Russia by leaking the location and suggesting strike tactics on prized, Western-donated F-16 and Mirage 2000 fighter jets.

The tiny "stealth" Carbon Voyager 1, fast-moving Black Tide I, and explosives-carrying Sea Shark 800 were the highlight of an expo for companies vying to help Taiwan build up a maritime drone force. Taipei believes drones could be pivotal in repelling China in the event its forces attempt to invade the self-ruled island, which Beijing has threatened to annex by force if necessary. Su'ao is just 60km (37 miles) from Fulong, one of the so-called "red beaches" identified by defence experts as potential landing sites for the People's Liberation Army (PLA) due to their unique topography. Whereas Russia sent tanks across land borders to launch its war on Ukraine in 2022, a Chinese invasion of Taiwan would involve Beijing sending vessels across the 180-km- (112-mile-)wide Taiwan Strait. While the Taiwan Strait's choppy waters and Taiwan's mountainous geography and shallow beaches pose formidable challenges to an amphibious invasion, technological advances and a decades-long modernisation campaign by the PLA have steadily chipped away at the island's natural defences.

A drone trajectory planner should be able to dynamically adjust the safety-efficiency trade-off according to varying mission requirements in unknown environments. Although traditional polynomial-based planners offer computational efficiency and smooth trajectory generation, they require expert knowledge to tune multiple parameters to adjust this trade-off. Moreover, even with careful tuning, the resulting adjustment may fail to achieve the desired trade-off. Similarly, although reinforcement learning-based planners are adaptable in unknown environments, they do not explicitly address the safety-efficiency trade-off. To overcome this limitation, we introduce a Decision Transformer-based trajectory planner that leverages a single parameter, Return-to-Go (RTG), as a \emph{temperature parameter} to dynamically adjust the safety-efficiency trade-off. In our framework, since RTG intuitively measures the safety and efficiency of a trajectory, RTG tuning does not require expert knowledge. We validate our approach using Gazebo simulations in both structured grid and unstructured random environments. The experimental results demonstrate that our planner can dynamically adjust the safety-efficiency trade-off by simply tuning the RTG parameter. Furthermore, our planner outperforms existing baseline methods across various RTG settings, generating safer trajectories when tuned for safety and more efficient trajectories when tuned for efficiency. Real-world experiments further confirm the reliability and practicality of our proposed planner.

-- This paper presents Virtual T each and Repeat (VirT&R): an extension of the T each and Repeat (T&R) framework that enables GPS-denied, zero-shot autonomous ground vehicle navigation in untraversed environments. VirT&R leverages aerial imagery captured for a target environment to train a Neural Radiance Field (NeRF) model so that dense point clouds and photo-textured meshes can be extracted. The NeRF mesh is used to create a high-fidelity simulation of the environment for piloting an unmanned ground vehicle (UGV) to virtually define a desired path. The mission can then be executed in the actual target environment by using NeRF-generated point cloud submaps associated along the path and an existing LiDAR T each and Repeat (L T&R) framework. We benchmark the repeatability of VirT&R on over 12 km of autonomous driving data using physical markings that allow a sim-to-real lateral path-tracking error to be obtained and compared with L T&R. VirT&R achieved measured root mean squared errors (RMSE) of 19.5 cm and 18.4 cm in two different environments, which are slightly less than one tire width (24 cm) on the robot used for testing, and respective maximum errors were 39.4 cm and 47.6 cm. This was done using only the NeRF-derived teach map, demonstrating that VirT&R has similar closed-loop path-tracking performance to L T&R but does not require a human to manually teach the path to the UGV in the actual environment. I. INTRODUCTION Enabling a higher level of autonomous navigation in remote, harsh, and potentially hazardous environments is a critical objective for many Unmanned Ground V ehicle (UGV) operations, as minimizing human presence in such scenarios reduces risk and lowers costs. Visual Teach and Repeat (VT&R) [1], is a complete autonomy stack that enables long-range navigation along previously taught routes, demonstrated on a UGV with 3D-LiDAR [2]-[4], Radar [5], and RGB vision sensors [1], as well as on a UA V with an RGB vision sensor [6], [7]. While Teach and Repeat (T&R) has demonstrated considerable success, it currently requires a human operator to manually guide the vehicle in the environment during the teaching phase to create a map and ensure traversability.

Kyiv, Ukraine – As luck would have it, emergency doctor Elina Dovzhenko was far enough from her vehicle when a Russian drone struck it, breaking the windshield and splattering pieces of shrapnel around. It was getting dark on July 9 in the bombed-out, nearly-abandoned city of Kupiansk which sits less than 5km (3 miles) from the front line in the northeastern Ukrainian region of Kharkiv – and just 40km (25 miles) west of the Russian border. But there was definitely enough light left for the Russian drone operator on the front line's opposite side to see that Dovzhenko's vehicle was a white ambulance with red stripes parked near a shelling-damaged hospital where she and her colleagues were. "We heard the drone move, it swirled and swirled around [the building], then we heard the blast," Dovzhenko, 29, told Al Jazeera. She and her colleagues were shocked and angry – but not surprised.