Retrofitting and thermographic survey (TS) companies in Scotland collaborate with social housing providers to tackle fuel poverty. They employ ground-level infrared (IR) camera-based-TSs (GIRTSs) for collecting thermal images to identi-fy the heat loss sources resulting from poor insulation. However, this identifica-tion process is labor-intensive and time-consuming, necessitating extensive data processing. To automate this, an AI-driven approach is necessary. Therefore, this study proposes a deep learning (DL)-based segmentation framework using the Mask Region Proposal Convolutional Neural Network (Mask RCNN) to validate its applicability to these thermal images. The objective of the framework is to au-tomatically identify, and crop heat loss sources caused by weak insulation, while also eliminating obstructive objects present in those images. By doing so, it min-imizes labor-intensive tasks and provides an automated, consistent, and reliable solution. To validate the proposed framework, approximately 2500 thermal imag-es were collected in collaboration with industrial TS partner. Then, 1800 repre-sentative images were carefully selected with the assistance of experts and anno-tated to highlight the target objects (TO) to form the final dataset. Subsequently, a transfer learning strategy was employed to train the dataset, progressively aug-menting the training data volume and fine-tuning the pre-trained baseline Mask RCNN. As a result, the final fine-tuned model achieved a mean average precision (mAP) score of 77.2% for segmenting the TO, demonstrating the significant po-tential of proposed framework in accurately quantifying energy loss in Scottish homes.

The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GUs), holds significant promise for advancing smart city applications. However, resource management of the SAGIN is a challenge requiring urgent study in that inappropriate resource management will cause poor data transmission, and hence affect the services in smart cities. In this paper, we develop a comprehensive SAGIN system that encompasses five distinct communication links and propose an efficient cooperative multi-type multi-agent deep reinforcement learning (CMT-MARL) method to address the resource management issue. The experimental results highlight the efficacy of the proposed CMT-MARL, as evidenced by key performance indicators such as the overall transmission rate and transmission success rate. These results underscore the potential value and feasibility of future implementation of the SAGIN.

Robotics and automation are poised to change the landscape of home and work in the near future. Robots are adept at deliberately moving, sensing, and interacting with their environments. The pervasive use of this technology promises societal and economic payoffs due to its capabilities - conversely, the capabilities of robots to move within and sense the world around them is susceptible to abuse. Robots, unlike typical sensors, are inherently autonomous, active, and deliberate. Such automated agents can become AI double agents liable to violate the privacy of coworkers, privileged spaces, and other stakeholders. In this work we highlight the understudied and inevitable threats to privacy that can be posed by the autonomous, deliberate motions and sensing of robots. We frame the problem within broader sociotechnological questions alongside a comprehensive review. The privacy-aware motion planning problem is formulated in terms of cost functions that can be modified to induce privacy-aware behavior - preserving, agnostic, or violating. Simulated case studies in manipulation and navigation, with altered cost functions, are used to demonstrate how privacy-violating threats can be easily injected, sometimes with only small changes in performance (solution path lengths). Such functionality is already widely available. This preliminary work is meant to lay the foundations for near-future, holistic, interdisciplinary investigations that can address questions surrounding privacy in intelligent robotic behaviors determined by planning algorithms.

The footprint of Russian President Vladimir Putin's war on Ukraine is growing fast after a weekend in which sea drones crippled a Russian naval vessel and oil tanker. For the first time, the attacks put at risk Russia's commodity exports via the Black Sea, a route that accounts for most of the grain and 15% to 20% of the oil Russia sells daily on global markets. Significantly higher insurance and shipping costs are likely to follow for Moscow, but there are risks to European and global markets, too. The expansion comes as Ukraine's counteroffensive advances more slowly than Kyiv officials planned, and as Saudi Arabia's attempt to catalyze peace talks by hosting a multinational conference showed just how hard it is likely to be to end the bloodshed on terms both sides can accept.

Formation flight has a vast potential for aerial robot swarms in various applications. However, existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To bridge the gap, we present a complete formation flight system that effectively integrates real-world constraints into aerial formation navigation. This paper proposes a differentiable graph-based metric to quantify the overall similarity error between formations. This metric is invariant to rotation, translation, and scaling, providing more freedom for formation coordination. We design a distributed trajectory optimization framework that considers formation similarity, obstacle avoidance, and dynamic feasibility. The optimization is decoupled to make large-scale formation flights computationally feasible. To improve the elasticity of formation navigation in highly constrained scenes, we present a swarm reorganization method that adaptively adjusts the formation parameters and task assignments by generating local navigation goals. A novel swarm agreement strategy called global-remap-local-replan and a formation-level path planner is proposed in this work to coordinate the global planning and local trajectory optimizations. To validate the proposed method, we design comprehensive benchmarks and simulations with other cutting-edge works in terms of adaptability, predictability, elasticity, resilience, and efficiency. Finally, integrated with palm-sized swarm platforms with onboard computers and sensors, the proposed method demonstrates its efficiency and robustness by achieving the largest scale formation flight in dense outdoor environments.

We present a novel optimization algorithm called DroNeRF for the autonomous positioning of monocular camera drones around an object for real-time 3D reconstruction using only a few images. Neural Radiance Fields or NeRF, is a novel view synthesis technique used to generate new views of an object or scene from a set of input images. Using drones in conjunction with NeRF provides a unique and dynamic way to generate novel views of a scene, especially with limited scene capabilities of restricted movements. Our approach focuses on calculating optimized pose for individual drones while solely depending on the object geometry without using any external localization system. The unique camera positioning during the data-capturing phase significantly impacts the quality of the 3D model. To evaluate the quality of our generated novel views, we compute different perceptual metrics like the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure(SSIM). Our work demonstrates the benefit of using an optimal placement of various drones with limited mobility to generate perceptually better results.

A Russian tanker was damaged in a Ukrainian drone attack in the Kerch Strait, briefly halting traffic on the strategic bridge linking Crimea to Russia on Saturday, a day after one of Moscow's warships was hit in the Black Sea. The number of attacks in the Black Sea has increased from both sides since Moscow exited a deal last month that had allowed Ukrainian grain exports via the shipping hub during the conflict between the two countries. The Russian tanker SIG was hit around 11:20 p.m. Friday south of the Kerch Strait, Russia's Federal Agency for Sea and Inland Water Transport said.

Ukrainian sea drones have attacked a key Russian port on the Black Sea, damaging a naval ship, according to a Ukrainian official, speaking about the latest in a series of strikes inside Russia after Kyiv promised to bring the fight home to the Kremlin. Moscow said it repelled Friday's attack on Novorossiysk, which marked the first time a commercial Russian port has been targeted in the 18-month war. Olenegorsky Gornyak, a landing ship, suffered a serious breach in the attack, carried out by Ukraine's navy and security service, according to a security service official. As a result, the ship is unable to carry out its combat missions, said the official who spoke on the condition of anonymity because he was not authorised to give the information to the media. Ukrainian news agencies carried footage from social media channels that they suggested showed the Olenegorsky Gornyak listing to one side. The ship is designed to transport troops and heavy equipment and was sent for repairs in 2014, according to Russian media reports.

This is based on announcements by Russian and Ukrainian authorities, and local media reports. Ukrainian defence sources have told CNN that sea drones had also been used in an attack on the Kerch Bridge to Crimea in July.

Ukrainian President Volodymyr Zelenskyy warned this week that war is coming to Russia after kamikaze drone attacks targeted skyscrapers in Moscow's financial district, as his country's forces continued to score small-scale territorial successes against Russian troops in Ukraine's east and south. Here is a round-up of the main battlefield events during the 75th week of the war. On July 30, a suspected Ukrainian long-range drone hit a Moscow high-rise building that houses the Ministry of Digital Development, the Economy Ministry and the Ministry of Industrial Development, responsible for military industry. Two days later, another pair of drones was shot down outside Moscow, but a third made it through to the city where it was intercepted by electronic jammers and crashed into a skyscraper, damaging the facade. The attacks came just days after a previous drone raid on the centre of the Russian capital.