Coverage path planning is a fundamental challenge in robotics, with diverse applications in aerial surveillance, manufacturing, cleaning, inspection, agriculture, and more. The main objective is to devise a trajectory for an agent that efficiently covers a given area, while minimizing time or energy consumption. Existing practical approaches often lack a solid theoretical foundation, relying on purely heuristic methods, or overly abstracting the problem to a simple Traveling Salesman Problem in Grid Graphs. Moreover, the considered cost functions only rarely consider turn cost, prize-collecting variants for uneven cover demand, or arbitrary geometric regions. In this paper, we describe an array of systematic methods for handling arbitrary meshes derived from intricate, polygonal environments. This adaptation paves the way to compute efficient coverage paths with a robust theoretical foundation for real-world robotic applications. Through comprehensive evaluations, we demonstrate that the algorithm also exhibits low optimality gaps, while efficiently handling complex environments. Furthermore, we showcase its versatility in handling partial coverage and accommodating heterogeneous passage costs, offering the flexibility to trade off coverage quality and time efficiency.

State-of-the-art object detection methods applied to satellite and drone imagery largely fail to identify small and dense objects. One reason is the high variability of content in the overhead imagery due to the terrestrial region captured and the high variability of acquisition conditions. Another reason is that the number and size of objects in aerial imagery are very different than in the consumer data. In this work, we propose a small object detection pipeline that improves the feature extraction process by spatial pyramid pooling, cross-stage partial networks, heatmap-based region proposal network, and object localization and identification through a novel image difficulty score that adapts the overall focal loss measure based on the image difficulty. Next, we propose novel contrastive learning with progressive domain adaptation to produce domain-invariant features across aerial datasets using local and global components. We show we can alleviate the degradation of object identification in previously unseen datasets. We create a first-ever domain adaptation benchmark using contrastive learning for the object detection task in highly imbalanced satellite datasets with significant domain gaps and dominant small objects. The proposed method results in a 7.4% increase in mAP performance measure over the best state-of-art.

Russia said Ukrainian drones damaged a nuclear waste storage facility at the Kursk Nuclear Power Plant on Thursday evening. This comes after the press service for the plant told journalists on Friday that there had been no significant damage from the attacks and that operations were continuing as normal. Intense fighting continued close to the city of Avdiivka, in the Donetsk region of eastern Ukraine. Russia's Ministry of Defence said its air defence systems destroyed 36 Ukraine-launched drones over the Black Sea off the Crimean Peninsula overnight. A statement from the ministry on Telegram did not provide much additional detail.

We propose a 3D simulator tailored for the Drone-as-a-Service framework. The simulator enables employing dynamic algorithms for addressing realistic delivery scenarios. We present the simulator's architectural design and its use of an energy consumption model for drone deliveries. We introduce two primary operational modes within the simulator: the edit mode and the runtime mode. Beyond its simulation capabilities, our simulator serves as a valuable data collection resource, facilitating the creation of datasets through simulated scenarios. Our simulator empowers researchers by providing an intuitive platform to visualize and interact with delivery environments. Moreover, it enables rigorous algorithm testing in a safe simulation setting, thus obviating the need for real-world drone deployments.

Just hours after U.S. fighter jets bombed facilities used by Iran's Islamic Revolutionary Guards Corps and its proxies in Syria early Friday, the proxies fired back -- launching an attack drone at U.S. forces in western Iraq. American air defenses shot down the drone a few miles from Al Asad Air Base, causing no injuries or damage on the ground, U.S. officials said on Friday. Pentagon officials also said that rockets were fired into northern Syria on Friday but landed far from American troops. Pentagon officials have attributed the attacks to Iran-backed militias. But the tit-for-tat raised questions about whether the airstrikes that were carried out after a flurry of rocket and drone attacks against U.S. forces in Iraq and Syria can achieve one of their major goals: to deter further attacks.

Israeli security forces restricted young Palestinians from entering the Al-Aqsa Mosque in Jerusalem for prayers on Friday and deployed in strength across the Old City and beyond to quell any unrest spilling over from the conflict in Gaza. In the occupied West Bank, Israeli troops killed four Palestinians during raids, the official Palestinian news agency WAFA said. Two of the dead were identified by fighter groups as their members. Large numbers of Israeli police kept guard around Al-Aqsa, a flashpoint and often the scene of clashes, as Palestinians gathered for Friday prayers, reports said. At one point, the police fired tear gas at the Palestinians, according to Reuters.

Russian drone strikes near a nuclear power plant in western Ukraine this week have revived anxiety among Ukrainian officials and civilians over one of the most oppressive hardships of the war: a winter assault on their nation's energy grid. The strikes Wednesday, which landed near the Khmelnytsky nuclear facility, drew an angry response from President Volodymyr Zelenskyy of Ukraine, who said it was "highly likely" that the power plant was the target. They also prompted another warning from the head of the United Nations' nuclear watchdog agency about the precarious nuclear safety situation in Ukraine. Zelenskyy vowed Wednesday night that Ukraine would hit back at targets inside Russia if Moscow tried once again to plunge his nation into cold and darkness.

Monitoring plantations is crucial for crop management and producing healthy harvests. Unmanned Aerial Vehicles (UAVs) have been used to collect multispectral images that aid in this monitoring. However, given the number of hectares to be monitored and the limitations of flight, plant disease signals become visually clear only in the later stages of plant growth and only if the disease has spread throughout a significant portion of the plantation. This limited amount of relevant data hampers the prediction models, as the algorithms struggle to generalize patterns with unbalanced or unrealistic augmented datasets effectively. To address this issue, we propose PlantPlotGAN, a physics-informed generative model capable of creating synthetic multispectral plot images with realistic vegetation indices. These indices served as a proxy for disease detection and were used to evaluate if our model could help increase the accuracy of prediction models. The results demonstrate that the synthetic imagery generated from PlantPlotGAN outperforms state-of-the-art methods regarding the Fr\'echet inception distance. Moreover, prediction models achieve higher accuracy metrics when trained with synthetic and original imagery for earlier plant disease detection compared to the training processes based solely on real imagery.

Drones aren't new tools for live sports production, but when the World Series begins this Friday, Fox Sports will use a fleet of three compact aircraft during the Fall Classic for the first time ever. Previously, the network used drones during baseball games for coverage of the All-Star and Field of Dreams games. Fox also employs drones for its broadcasts of USFL and first began using them for production in 2015. For the World Series, Fox plans to use the trio of drones to capture moments like relief pitchers coming in from the bullpen, warm-ups between innings and pitchers leaving the mound. The network collaborated with Beverly Hills Aerials on the customized fleet and that company will operate them.

The leaders of the United States and Australia agreed Wednesday to expand defense cooperation with Japan to include unmanned aerial vehicles as Washington continues to bolster relations with its Asia-Pacific allies and partners to maintain its edge in the face of China's growing military might. Following a meeting at the White House, U.S. President Joe Biden and Australian Prime Minister Anthony Albanese said the three-way partnership aims to enhance interoperability and accelerate technology transfer in the rapidly emerging field of "collaborative combat aircraft and autonomy," -- a U.S. Air Force concept referring to autonomous drone operations and manned-unmanned teaming. No further details were provided, but the announcement comes after the Pentagon unveiled its "Replicator" initiative last month: a radical new strategy focused on fielding thousands of cheap autonomous drones within 18 to 24 months to counter China's military advantage in personnel and manned equipment.