Drone footage captured in St. Lucie County, Palm Beach Gardens, St. Petersburg, and Siesta Key shows damage to homes and structures after Hurricane Milton and multiple tornadoes tore across the state.

Mobile robots rely on maps to navigate through an environment. In the absence of any map, the robots must build the map online from partial observations as they move in the environment. Traditional methods build a map using only direct observations. In contrast, humans identify patterns in the observed environment and make informed guesses about what to expect ahead. Modeling these patterns explicitly is difficult due to the complexity of the environments. However, these complex models can be approximated well using learning-based methods in conjunction with large training data. By extracting patterns, robots can use direct observations and predictions of what lies ahead to better navigate an unknown environment. In this dissertation, we present several learning-based methods to equip mobile robots with prediction capabilities for efficient and safer operation. In the first part of the dissertation, we learn to predict using geometrical and structural patterns in the environment. Partially observed maps provide invaluable cues for accurately predicting the unobserved areas. We first demonstrate the capability of general learning-based approaches to model these patterns for a variety of overhead map modalities. Then we employ task-specific learning for faster navigation in indoor environments by predicting 2D occupancy in the nearby regions. This idea is further extended to 3D point cloud representation for object reconstruction. Predicting the shape of the full object from only partial views, our approach paves the way for efficient next-best-view planning. In the second part of the dissertation, we learn to predict using spatiotemporal patterns in the environment. We focus on dynamic tasks such as target tracking and coverage where we seek decentralized coordination between robots. We first show how graph neural networks can be used for more scalable and faster inference.

The same types of consumer-grade hobby drones heard buzzing overhead at the beach may play a key role in uncovering the Earth's remaining hidden species. For the first time, researchers recently used such a drone to discover and describe a new species of carnation hanging off of towering vertical cliffs in Hawaii. The revelation was made possible thanks to rapid advancements in drone sensor technology and a new, custom-designed claw-like plant extraction device. Details of the discovery were published this week in open access journal PhytoKeys. The newly described carnation, called Schiedea waiahuluensis, was spotted hanging off cliffs in the Waiahulu region of Hawaii's Kauaʻi island.

Locals and emergency responders are only just beginning to assess the total damage incurred from Hurricane Milton, but one early example of the historic storm's intensity is already on clear display. In the early hours of October 10th, extreme weather documentarian Brandon Clement uploaded nine minutes of ground and drone footage that showcased the destructive effects of 100 mph sustained winds on St. Petersburg's Tropicana Field, home to MLB's Tampa Bay Rays. As local news outlet WFLA explained on Thursday, the baseball team's home field was topped by fabric paneling that served as its roofing, an estimated two-thirds of which is now either shredded or gone completely. It's currently unclear if Tropicana Field suffered serious interior damage, but Clement's aerial images reveal substantial amounts of debris scattering the field, stands, and surrounding areas that includes what appears to be hundreds of cots. City officials previously intended to use the Rays' stadium as a hub for emergency worker coordinating efforts, but it's unclear how the facility's current state will affect response logistics in the coming days.

The Israeli military "repeatedly" fired at UNIFIL headquarters and positions in southern Lebanon, injuring two members of the peacekeeping force, the United Nations says, as Israel presses on with its assault on Hezbollah. UNIFIL – the UN Interim Force in Lebanon – said on Thursday that two of its peacekeepers were injured after an Israeli tank "fired its weapon" at a guard tower at the group's headquarters, located in the border area town of Naqoura. The attack on the tower had caused the two peacekeepers to fall. "The injuries are fortunately, this time, not serious, but they remain in hospital," said UNIFIL in a statement. The Israeli soldiers also fired on a UN position – named "1-31"- in the village of Labbouneh, "hitting the entrance to the bunker where peacekeepers were sheltering, and damaging vehicles and a communications system", it said. The peacekeeping force reported that it had observed an Israeli military drone flying inside the UN position up to the bunker entrance.

Russia's overnight attacks on Ukraine also left several people wounded in the southern city of Zaporizhzhia. Meanwhile, Ukrainian drones targeted a military airfield in the Maikop region of southern Russia. Local officials evacuated 40 people from a nearby village. Russia's missile strike on the Odesa region hit a Panamanian-registered ship on Wednesday night, Oleh Kiper said - two days after a Palau-flagged ship was attacked, leaving one dead on board. Another ship, which was said to be carrying 6,000 tonnes of corn, was attacked on Sunday.

Air ducts are integral to modern buildings but are challenging to access for inspection. Small quadrotor drones offer a potential solution, as they can navigate both horizontal and vertical sections and smoothly fly over debris. However, hovering inside air ducts is problematic due to the airflow generated by the rotors, which recirculates inside the duct and destabilizes the drone, whereas hovering is a key feature for many inspection missions. In this article, we map the aerodynamic forces that affect a hovering drone in a duct using a robotic setup and a force/torque sensor. Based on the collected aerodynamic data, we identify a recommended position for stable flight, which corresponds to the bottom third for a circular duct. We then develop a neural network-based positioning system that leverages low-cost time-of-flight sensors. By combining these aerodynamic insights and the data-driven positioning system, we show that a small quadrotor drone (here, 180 mm) can hover and fly inside small air ducts, starting with a diameter of 350 mm. These results open a new and promising application domain for drones.

Aerial Vision-and-Language Navigation (VLN) is a novel task enabling Unmanned Aerial Vehicles (UAVs) to navigate in outdoor environments through natural language instructions and visual cues. It remains challenging due to the complex spatial relationships in outdoor aerial scenes. In this paper, we propose an end-to-end zero-shot framework for aerial VLN tasks, where the large language model (LLM) is introduced as our agent for action prediction. Specifically, we develop a novel Semantic-Topo-Metric Representation (STMR) to enhance the spatial reasoning ability of LLMs. This is achieved by extracting and projecting instruction-related semantic masks of landmarks into a top-down map that contains the location information of surrounding landmarks. Further, this map is transformed into a matrix representation with distance metrics as the text prompt to the LLM, for action prediction according to the instruction. Experiments conducted in real and simulation environments have successfully proved the effectiveness and robustness of our method, achieving 15.9% and 12.5% improvements (absolute) in Oracle Success Rate (OSR) on AerialVLN-S dataset.

--Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator . However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. Additionally, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position sub-dynamics, the vehicle attitude sub-dynamics, and the manipulator sub-dynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches. I. INTRODUCTION An Unmanned Aerial Manipulator (UAM) is a coupled system where a quadrotor (or multirotor) vehicle carries a manipulator: the presence of the manipulator greatly improves the dexterity and flexibility of the quadrotor, making it capable to accomplish a wide range of tasks, from simple payload transportation to more complex tasks such as pick and place, contact-based inspection, grasping and assembling etc. [1]-[8]. This work was supported in part by "Aerial Manipulation" under IHFC grand project (GP/2021/DA/032), in part by "Capacity building for human resource development in Unmanned Aircraft System (Drone and related Technology)", MeiTY, India, in part by the Natural Science Foundation of China grants 62233004 and 62073074, and in part by Jiangsu Provincial Scientific Research Center of Applied Mathematics grant BK20233002.

In the area of multi-drone systems, navigating through dynamic environments from start to goal while providing collision-free trajectory and efficient path planning is a significant challenge. To solve this problem, we propose a novel SwarmPath technology that involves the integration of Artificial Potential Field (APF) with Impedance Controller. The proposed approach provides a solution based on collision free leader-follower behaviour where drones are able to adapt themselves to the environment. Moreover, the leader is virtual while drones are physical followers leveraging APF path planning approach to find the smallest possible path to the target. Simultaneously, the drones dynamically adjust impedance links, allowing themselves to create virtual links with obstacles to avoid them. As compared to conventional APF, the proposed SwarmPath system not only provides smooth collision-avoidance but also enable agents to efficiently pass through narrow passages by reducing the total travel time by 30% while ensuring safety in terms of drones connectivity. Lastly, the results also illustrate that the discrepancies between simulated and real environment, exhibit an average absolute percentage error (APE) of 6% of drone trajectories. This underscores the reliability of our solution in real-world scenarios.