This paper explores the use of an extended neuroevolutionary approach, based on NeuroEvolution of Augmenting Topologies (NEAT), for autonomous robots in dynamic environments associated with hazardous tasks like firefighting, urban search-and-rescue (USAR), and industrial inspections. Building on previous research, it expands the simulation environment to larger and more complex settings, demonstrating NEAT's adaptability across different applications. By integrating recent advancements in NEAT and reinforcement learning, the study uses modern simulation frameworks for realism and hybrid algorithms for optimization. Experimental results show that NEAT-evolved controllers achieve success rates comparable to state-of-the-art deep reinforcement learning methods, with superior structural adaptability. The agents reached ~80% success in outdoor tests, surpassing baseline models. The paper also highlights the benefits of transfer learning among tasks and evaluates the effectiveness of NEAT in complex 3D navigation. Contributions include evaluating NEAT for diverse autonomous applications and discussing real-world deployment considerations, emphasizing the approach's potential as an alternative or complement to deep reinforcement learning in autonomous navigation tasks.

A suspected drone attack by Sudan's Rapid Support Forces (RSF) paramilitary has killed at least 11 people at a displacement camp in River Nile state, authorities said. In a statement late on Friday, the local governor said the attack knocked out a nearby power station for the fourth time since the war between the RSF and the Sudanese army began two years ago. The attack marks a deadly escalation in the ongoing conflict, with a further 23 people injured, a medical official said. Witnesses said at least nine children were among the wounded. "My son, my cousin, my daughter's husband and two children, my cousin's children are dead. The boy is 10 years old and the girl is about two years old," witness Haleema told Al Jazeera.

United States President Donald Trump's envoy Steve Witkoff has arrived in Moscow for a meeting with President Vladimir Putin, hours after Russia killed at least five people in Ukraine. A child was among three people killed overnight on Friday in Russian drone attacks on central Ukraine's industrial city of Pavlohrad, according to Serhiy Lysak, governor of the Dnipropetrovsk region. He said 14 people were also wounded in the attack on a five-storey building, including a six-year-old boy and teenagers, aged 15 and 17. Five of the wounded remained in hospital, he added. Two more people were killed on Friday morning in Donetsk region's Yarova settlement, where an aerial bomb was dropped on a residential building, according to Donetsk regional prosecutors.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS Flying through cluttered and dynamic environments with LiDAR Huajie Wu, Wenyi Liu, Y unfan Ren, Zheng Liu, Hairuo Wei, Fangcheng Zhu, Haotian Li, and Fu Zhang Abstract --Navigating unmanned aerial vehicles (UAVs) through cluttered and dynamic environments remains a significant challenge, particularly when dealing with fast-moving or sudden-appearing obstacles. This paper introduces a complete LiDAR-based system designed to enable UAVs to avoid various moving obstacles in complex environments. Benefiting the high computational efficiency of perception and planning, the system can operate in real time using onboard computing resources with low latency. For dynamic environment perception, we have integrated our previous work, M-detector, into the system. M-detector ensures that moving objects of different sizes, colors, and types are reliably detected. For dynamic environment planning, we incorporate dynamic object predictions into the integrated planning and control (IPC) framework, namely DynIPC. This integration allows the UAV to utilize predictions about dynamic obstacles to effectively evade them. We validate our proposed system through both simulations and real-world experiments. In simulation tests, our system outperforms state-of-the-art baselines across several metrics, including success rate, time consumption, average flight time, and maximum velocity. Index Terms --LiDAR-based UAV, dynamic obstacle avoidance, cluttered and dynamic environment I. I NTRODUCTION I N recent years, the development of lightweight and high-precision sensors, such as Light Detection and Ranging sensors (LiDAR), event cameras, and depth cameras, has significantly advanced the autonomous flight capabilities of unmanned aerial vehicles (UA Vs) or drones. This technological progress has facilitated the widespread application of drones across various industries, including agricultural spraying [1], product delivery [2], inspection [3], and search and rescue [4]. These applications have notably enhanced production efficiency, reduced costs, and driven economic growth within these sectors.

Rapid progress in intelligent unmanned systems has presented new opportunities for mobile crowd sensing (MCS). Today, heterogeneous air-ground collaborative multi-agent framework, which comprise unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), have presented superior flexibility and efficiency compared to traditional homogeneous frameworks in complex sensing tasks. Within this context, task allocation among different agents always play an important role in improving overall MCS quality. In order to better allocate tasks among heterogeneous collaborative agents, in this paper, we investigated two representative complex multi-agent task allocation scenarios with dual optimization objectives: (1) For AG-FAMT (Air-Ground Few Agents More Tasks) scenario, the objectives are to maximize the task completion while minimizing the total travel distance; (2) For AG-MAFT (Air-Ground More Agents Few Tasks) scenario, where the agents are allocated based on their locations, has the optimization objectives of minimizing the total travel distance while reducing travel time cost. To achieve this, we proposed a Multi-Task Minimum Cost Maximum Flow (MT-MCMF) optimization algorithm tailored for AG-FAMT, along with a multi-objective optimization algorithm called W-ILP designed for AG-MAFT, with a particular focus on optimizing the charging path planning of UAVs. Our experiments based on a large-scale real-world dataset demonstrated that the proposed two algorithms both outperform baseline approaches under varying experimental settings, including task quantity, task difficulty, and task distribution, providing a novel way to improve the overall quality of mobile crowdsensing tasks.

With the growing popularity of deep reinforcement learning (DRL), human-in-the-loop (HITL) approach has the potential to revolutionize the way we approach decision-making problems and create new opportunities for human-AI collaboration. In this article, we introduce a novel multi-layered hierarchical HITL DRL algorithm that comprises three types of learning: self learning, imitation learning and transfer learning. In addition, we consider three forms of human inputs: reward, action and demonstration. Furthermore, we discuss main challenges, trade-offs and advantages of HITL in solving complex problems and how human information can be integrated in the AI solution systematically. To verify our technical results, we present a real-world unmanned aerial vehicles (UAV) problem wherein a number of enemy drones attack a restricted area. The objective is to design a scalable HITL DRL algorithm for ally drones to neutralize the enemy drones before they reach the area. To this end, we first implement our solution using an award-winning open-source HITL software called Cogment. We then demonstrate several interesting results such as (a) HITL leads to faster training and higher performance, (b) advice acts as a guiding direction for gradient methods and lowers variance, and (c) the amount of advice should neither be too large nor too small to avoid over-training and under-training. Finally, we illustrate the role of human-AI cooperation in solving two real-world complex scenarios, i.e., overloaded and decoy attacks.

A'flame in the sky,' eerie red glowing objects and swarms of UFOs over military bases are just some of the many sightings that have gravely concerned the US government. There are dozens of unsolved cases going back to the 1960s that occurred over nuclear missile installations, Navy ships and a desert in New Mexico. The FBI, CIA, and other government branches have spent years looking into these reports, but have yet to determine what the objects were and where they came from. One report in 2019 detailed how'drones' appeared over Colorado, Nebraska, Wyoming, and Kansas as locals reported spying a mothership hanging in the sky. In just the last few months, the skies over New Jersey were filled with unidentified aircraft and drones that required a formal response from both the Biden and Trump presidencies.

Kyiv, Ukraine – Finishing a cigarette with a final deep puff outside a hospital building in central Kyiv, a wounded Ukrainian drone operator sums up Russian President Vladimir Putin's readiness to end the Ukraine war along the current front lines. "Don't trust these leaks, the … vampire is just dragging the talks out," Arseny, a 31-year-old recovering from a cranial wound that left him blind in one eye, told Al Jazeera while standing near a blossoming apple tree. He referred to a Financial Times report on Tuesday that suggested that Putin could "relinquish" Moscow's claims on four partly-occupied Ukrainian regions. In September 2022, seven months after Russia's full-scale invasion of Ukraine began, Moscow recognised the regions as part of Russia even though it did not fully control them – and began losing some occupied areas within weeks. In return for the Kremlin's concession, the US may recognise Crimea, a Black Sea peninsula Moscow annexed in 2014, as part of Russia, and "acknowledge" the Kremlin's de facto control over the four regions' occupied parts, the Financial Times claimed, citing officials familiar with the talks.

Tech expert Kurt Knutsson discusses how robots and drones are revolutionizing fruit farming with faster picking and smarter handling. Farming is undergoing a remarkable transformation thanks to cutting-edge technologies reshaping how fruit is picked and handled. While autonomous drones like Tevel's Flying Robots are already harvesting fruit globally, innovations like UC San Diego's GRIP-tape gripper represent the next frontier in gentle produce handling. Together, these advancements promise to make fruit production more efficient and precise, though one is a proven solution and the other is a glimpse into farming's future. GET SECURITY ALERTS & EXPERT TECH TIPS – SIGN UP FOR KURT'S'THE CYBERGUY REPORT' NOW Tevel's Flying Autonomous Robots (FARs) are redefining fruit harvesting by combining artificial intelligence with advanced computer vision.

Nine people have been killed after a Russian drone hit a bus transporting workers in Ukraine, officials say. The attack occurred on Wednesday morning in the south-central city of Marhanets. Serhiy Lysak, regional chief of Dnipropetrovsk, said at least 30 people were injured, adding that "the number of victims is constantly growing". The attack comes as diplomats from the UK, France, Germany, the US and Ukraine are preparing to hold talks in London aimed at securing a ceasefire in the conflict. Russia launched a full-scale invasion of Ukraine on 24 February 2022.