Safe Periodic Trochoidal Paths for Fixed-Wing UA Vs in Confined Windy Environments Jaeyoung Lim 1, David Rohr 1, Thomas Stastny 1, Roland Siegwart 1 Abstract -- Due to their energy-efficient flight characteristics, fixed-wing type uncrewed aerial vehicles (UA Vs) are useful robotic tools for long-range and duration flight applications in large-scale environments. However, flying fixed-wing UA V in confined environments, such as mountainous regions, can be challenging due to their limited maneuverability and sensitivity to uncertain wind conditions. In this work, we first analyze periodic trochoidal paths that can be used to define wind-aware terminal loitering states. We then propose a wind-invariant safe set of trochoidal paths along with a switching strategy for selecting the corresponding minimum-extent periodic path type. Finally, we show that planning with this minimum-extent set allows us to safely reach up to 10 times more locations in mountainous terrain compared to planning with a single, conservative loitering maneuver . I. INTRODUCTION Uncrewed aerial vehicles (UA Vs) have become crucial tools for information-gathering applications, such as surveying and inspection [1], search and rescue [2], and environment monitoring [3], [4]. For large-scale coverage or long-range applications, fixed-wing type UA Vs are preferred over rotary-wing type systems due to their high endurance and speed. While the wing-borne aerodynamic lift enables energy-efficient flight, it also poses challenges for operating safely.

A long-cherished vision of drones is to autonomously traverse through clutter to reach every corner of the world using onboard sensing and computation. In this paper, we combine onboard 3D lidar sensing and sim-to-real reinforcement learning (RL) to enable autonomous flight in cluttered environments. Compared to vision sensors, lidars appear to be more straightforward and accurate for geometric modeling of surroundings, which is one of the most important cues for successful obstacle avoidance. On the other hand, sim-to-real RL approach facilitates the realization of low-latency control, without the hierarchy of trajectory generation and tracking. We demonstrate that, with design choices of practical significance, we can effectively combine the advantages of 3D lidar sensing and RL to control a quadrotor through a low-level control interface at 50Hz. The key to successfully learn the policy in a lightweight way lies in a specialized surrogate of the lidar's raw point clouds, which simplifies learning while retaining a fine-grained perception to detect narrow free space and thin obstacles. Simulation statistics demonstrate the advantages of the proposed system over alternatives, such as performing easier maneuvers and higher success rates at different speed constraints. With lightweight simulation techniques, the policy trained in the simulator can control a physical quadrotor, where the system can dodge thin obstacles and safely traverse randomly distributed obstacles.

One morning last month, while I was waiting at a bus stop on the western edge of the western Ukrainian city of Lviv, I struck up a conversation with a man in his early forties named Mykola Hryhoryan. Across from the bus stop was a bombed-out museum. I asked if he knew what had happened to it. "It was hit by a Russian drone," he said. Mykola was wearing jeans and a black parka with the hood pulled over his head. He told me that he was a soldier.

Technologies such as robotics, Artificial Intelligence (AI), and Computer Vision (CV) can be applied to crime scene analysis (CSA) to help protect lives, facilitate justice, and deter crime, but an overview of the tasks that can be automated has been lacking. Here we follow a speculate prototyping approach: First, the STAIR tool is used to rapidly review the literature and identify tasks that seem to have not received much attention, like accessing crime sites through a window, mapping/gathering evidence, and analyzing blood smears. Secondly, we present a prototype of a small drone that implements these three tasks with 75%, 85%, and 80% performance, to perform a minimal analysis of an indoor crime scene. Lessons learned are reported, toward guiding next work in the area.

A security camera in eastern Lebanon captured the moments a car was hit in a suspected Israeli drone attack. Lebanon's health ministry said one person was killed and another injured.

JTB and two other Japanese companies said Wednesday that they will launch services employing drones to shoot videos from the sky of customers at tourist spots. The travel agency, drone business operator Fly and Japan Airlines expect their services to help spread the attractions of tourist spots. Drones will start video-recording after customers scan quick response codes prepared at tourist spots and complete payments via a website. Customers will receive one or two minutes of video footage edited automatically by artificial intelligence about 30 minutes after recording. The services will cost 2,000 per use.

This paper proposes a holistic framework for autonomous guidance, navigation, and task distribution among multi-drone systems operating in Global Navigation Satellite System (GNSS)-denied indoor settings. We advocate for a Deep Reinforcement Learning (DRL)-based guidance mechanism, utilising the Twin Delayed Deep Deterministic Policy Gradient algorithm. To improve the efficiency of the training process, we incorporate an Artificial Potential Field (APF)-based reward structure, enabling the agent to refine its movements, thereby promoting smoother paths and enhanced obstacle avoidance in indoor contexts. Furthermore, we tackle the issue of task distribution among cooperative UAVs through a DRL-trained Graph Convolutional Network (GCN). This GCN represents the interactions between drones and tasks, facilitating dynamic and real-time task allocation that reflects the current environmental conditions and the capabilities of the drones. Such an approach fosters effective coordination and collaboration among multiple drones during search and rescue operations or other exploratory endeavours. Lastly, to ensure precise odometry in environments lacking GNSS, we employ Light Detection And Ranging Simultaneous Localisation and Mapping complemented by a depth camera to mitigate the hallway problem. This integration offers robust localisation and mapping functionalities, thereby enhancing the systems dependability in indoor navigation. The proposed multi-drone framework not only elevates individual navigation capabilities but also optimises coordinated task allocation in complex, obstacle-laden environments. Experimental evaluations conducted in a setup tailored to meet the requirements of the NATO Sapience Autonomous Cooperative Drone Competition demonstrate the efficacy of the proposed system, yielding outstanding results and culminating in a first-place finish in the 2024 Sapience competition.

Multi-rotor aerial autonomous vehicles (MAVs, more widely known as "drones") have been generating increased interest in recent years due to their growing applicability in a vast and diverse range of fields (e.g., agriculture, commercial delivery, search and rescue). The sensitivity of visual-based methods to lighting conditions and occlusions had prompted growing study of navigation reliant on other modalities, such as acoustic sensing. A major concern in using drones in scale for tasks in non-controlled environments is the potential threat of adversarial attacks over their navigational systems, exposing users to mission-critical failures, security breaches, and compromised safety outcomes that can endanger operators and bystanders. While previous work shows impressive progress in acoustic-based drone localization, prior research in adversarial attacks over drone navigation only addresses visual sensing-based systems. In this work, we aim to compensate for this gap by supplying a comprehensive analysis of the effect of PGD adversarial attacks over acoustic drone localization. We furthermore develop an algorithm for adversarial perturbation recovery, capable of markedly diminishing the affect of such attacks in our setting. The code for reproducing all experiments will be released upon publication.

--The adoption of unmanned aerial vehicles to monitor critical infrastructure is gaining momentum in various industrial domains. Organizational imperatives drive this progression to minimize expenses, accelerate processes, and mitigate hazards faced by inspection personnel. However, traditional infrastructure monitoring systems face critical bottlenecks--5G networks lack the latency and reliability for large-scale drone coordination, while manual inspections remain costly and slow. We propose a 6G-enabled drone swarm system that integrates ultra-reliable, low-latency communications, edge AI, and semantic communication to automate inspections. By adopting LLMs for structured output and report generation, our framework is hypothesized to reduce inspection costs and improve fault detection speed compared to existing methods. I NTRODUCTION Drones, key components of the unmanned aerial system, have been widely used over the past decade for various applications, including inspections, surveillance, delivery, search and rescue, etc. [1].

-- Autonomous robotic inspection missions require balancing multiple conflicting objectives while navigating near costly obstacles. Current multi-objective path planning (MOPP) methods struggle to adapt to evolving risks like localization errors, weather, battery state, and communication issues. This letter presents an Adaptive Risk-aware and Energy-efficient NA vigation (ARENA) MOPP approach for UA Vs in complex 3D environments. Our method enables online trajectory adaptation by optimizing safety, time, and energy using 4D NURBS representation and a genetic-based algorithm to generate the Pareto front. A novel risk-aware voting algorithm ensures adaptivity. Simulations and real-world tests demonstrate the planner's ability to produce diverse, optimized trajectories covering 95% or more of the range defined by single-objective benchmarks and its ability to estimate power consumption with a mean error representing 14% of the full power range. The ARENA framework enhances UA V autonomy and reliability in critical, evolving 3D missions. Uncrewed aerial vehicles (UA Vs) are becoming crucial tools in various scenarios where human involvement can become too risky or incur high costs, such as search and rescue [1], surveillance [2], and inspection [3], [4]. Achieving autonomy in these scenarios heavily relies on the path planning module to generate safe and feasible trajectories. Numerous approaches have been proposed to find the shortest or safest path in a cluttered environment.