Aerial manipulation combines the versatility and speed of flying platforms with the functional capabilities of mobile manipulation, which presents significant challenges due to the need for precise localization and control. Traditionally, researchers have relied on offboard perception systems, which are limited to expensive and impractical specially equipped indoor environments. In this work, we introduce a novel platform for autonomous aerial manipulation that exclusively utilizes onboard perception systems. Our platform can perform aerial manipulation in various indoor and outdoor environments without depending on external perception systems. Our experimental results demonstrate the platform's ability to autonomously grasp various objects in diverse settings. This advancement significantly improves the scalability and practicality of aerial manipulation applications by eliminating the need for costly tracking solutions. To accelerate future research, we open source our ROS 2 software stack and custom hardware design, making our contributions accessible to the broader research community.

Robust depth perception in visually-degraded environments is crucial for autonomous aerial systems. Thermal imaging cameras, which capture infrared radiation, are robust to visual degradation. However, due to lack of a large-scale dataset, the use of thermal cameras for unmanned aerial system (UAS) depth perception has remained largely unexplored. This paper presents a stereo thermal depth perception dataset for autonomous aerial perception applications. The dataset consists of stereo thermal images, LiDAR, IMU and ground truth depth maps captured in urban and forest settings under diverse conditions like day, night, rain, and smoke. We benchmark representative stereo depth estimation algorithms, offering insights into their performance in degraded conditions. Models trained on our dataset generalize well to unseen smoky conditions, highlighting the robustness of stereo thermal imaging for depth perception. We aim for this work to enhance robotic perception in disaster scenarios, allowing for exploration and operations in previously unreachable areas. The dataset and source code are available at https://firestereo.github.io.

The localization of Unmanned aerial vehicles (UAVs) in deep tunnels is extremely challenging due to their inaccessibility and hazardous environment. Conventional outdoor localization techniques (such as using GPS) and indoor localization techniques (such as those based on WiFi, Infrared (IR), Ultra-Wideband, etc.) do not work in deep tunnels. We are developing a UAV-based system for the inspection of defects in the Deep Tunnel Sewerage System (DTSS) in Singapore. To enable the UAV localization in the DTSS, we have developed a distance measurement module based on the optical flow technique. However, the standard optical flow technique does not work well in tunnels with poor lighting and a lack of features. Thus, we have developed an enhanced optical flow algorithm with prediction, to improve the distance measurement for UAVs in deep hazardous tunnels.

Airships, best recognized for their unique quality of payload/energy ratio, present a fascinating challenge for the field of engineering. Their construction and operation require a delicate balance of materials and rules, making them a compelling object of study. They embody a distinct intersection of physics, design, and innovation, offering a wide array of possibilities for future transportation and exploration. Thanks to their long-flight endurance, they are suited for long-term missions. To operate in complex environments such as indoor cluttered spaces, their membrane and mechatronics need to be protected from impacts. This paper presents a new indoor airship design inspired by origami and the Kresling pattern. The airship structure combines a carbon fiber exoskeleton and UV resin micro-lattices for shock absorption. Our design strengthens the robot while granting the ability to access narrow spaces by folding the structure - up to a volume expansion ratio of 19.8. To optimize the numerous parameters of the airship, we present a pipeline for design, manufacture, and assembly. It takes into account manufacturing constraints, dimensions of the target deployment area, and aerostatics, allowing for easy and quick testing of new configurations. We also present unique features made possible by combining origami with airship design, which reduces the chances of mission-compromising failures. We demonstrate the potential of the design with a complete simulation including an effective control strategy leveraging lightweight mechatronics to optimize flight autonomy in exploration missions of unstructured environments.

Ukraine on Tuesday hit the Moscow region in series of drone strikes that killed one woman, destroyed dozens of homes and forced some 50 flights to be rerouted from the Russian capital, reporting by Reuters confirmed. The attack on Moscow was reportedly the largest drone strike levied by Kyiv at Russia since the war began more than two and half years ago. Russia, which has heavily relied on drones and missiles in its assault against Ukraine and routinely pummels Kyiv with a barrage of aerial assaults, said it destroyed at least 20 Ukrainian drones over the Moscow region along with another 124 across eight other regions. A residential building outside of Moscow after it was hit in a series of drone strikes by Ukraine on Sept. 10, 2024. Kremlin spokesperson Dmitry Peskov suggested the attacks levied at the Russian capital, which has a population of some 21 million, were not legitimate military targets.

This is the moment a drone attack hit a residential building near Moscow. Russian authorities say at least two high-rise apartments were damaged in a wave of overnight attacks by Ukraine on the area.

The Ukrainian Air Force said on Telegram that its air defences downed 38 out of 46 Shahed-type attack drones launched by Russia. They were shot down over a number of regions and cities including Kyiv, Odesa, Kherson, Sumy, Kharkiv and Poltova. The air force added that Russia also launched an Iskander-M ballistic missile and a Kh-31 air-to-surface missile. Ukraine and Russia regularly launch overnight drone raids on each other's territory. The latest wave of drone strikes comes as Moscow claims gains in eastern Ukraine.

One person has been killed in Moscow after the remnants of a downed Ukrainian drone hit the apartment block where he was living and started a fire, according to Russian officials. Moscow regional Governor Andrei Vorobyov said debris from the drone damaged at least two high-rise apartment buildings in the Ramenskoye district in the early hours of Tuesday, setting several flats on fire. City mayor Sergei Sobyanin said emergency teams had been sent to a number of locations across the region as well as to the area near the Zhukovo airport and around the Domodedovo district – the site of one of Moscow's largest airports. More than 30 flights were suspended. Russia said its air defences shot down more than 70 Ukrainian drones during the night with at least 15 intercepted in and around Moscow.

Application of Unmanned Aerial Vehicles (UAVs) in search and rescue, emergency management, and law enforcement has gained traction with the advent of low-cost platforms and sensor payloads. The emergence of hybrid neural and symbolic AI approaches for complex reasoning is expected to further push the boundaries of these applications with decreasing levels of human intervention. However, current UAV simulation environments lack semantic context suited to this hybrid approach. To address this gap, HAMERITT (Hybrid Ai Mission Environment for RapId Training and Testing) provides a simulation-based autonomy software framework that supports the training, testing and assurance of neuro-symbolic algorithms for autonomous maneuver and perception reasoning. HAMERITT includes scenario generation capabilities that offer mission-relevant contextual symbolic information in addition to raw sensor data. Scenarios include symbolic descriptions for entities of interest and their relations to scene elements, as well as spatial-temporal constraints in the form of time-bounded areas of interest with prior probabilities and restricted zones within those areas. HAMERITT also features support for training distinct algorithm threads for maneuver vs. perception within an end-to-end mission run. Future work includes improving scenario realism and scaling symbolic context generation through automated workflow.

Over the past decade, there has been a remarkable surge in utilizing quadrotors for various purposes due to their simple structure and aggressive maneuverability, such as search and rescue, delivery and autonomous drone racing, etc. One of the key challenges preventing quadrotors from being widely used in these scenarios is online waypoint-constrained time-optimal trajectory generation and control technique. This letter proposes an imitation learning-based online solution to efficiently navigate the quadrotor through multiple waypoints with time-optimal performance. The neural networks (WN&CNets) are trained to learn the control law from the dataset generated by the time-consuming CPC algorithm and then deployed to generate the optimal control commands online to guide the quadrotors. To address the challenge of limited training data and the hover maneuver at the final waypoint, we propose a transition phase strategy that utilizes MINCO trajectories to help the quadrotor 'jump over' the stop-and-go maneuver when switching waypoints. Our method is demonstrated in both simulation and real-world experiments, achieving a maximum speed of 5.6m/s while navigating through 7 waypoints in a confined space of 5.5m*5.5m*2.0m. The results show that with a slight loss in optimality, the WN&CNets significantly reduce the processing time and enable online optimal control for multiple-waypoint constrained flight tasks.