This paper presents a formation control approach for contactless gesture-based Human-Swarm Interaction (HSI) between a team of multi-rotor Unmanned Aerial Vehicles (UAVs) and a human worker. The approach is intended for monitoring the safety of human workers, especially those working at heights. In the proposed dynamic formation scheme, one UAV acts as the leader of the formation and is equipped with sensors for human worker detection and gesture recognition. The follower UAVs maintain a predetermined formation relative to the worker's position, thereby providing additional perspectives of the monitored scene. Hand gestures allow the human worker to specify movements and action commands for the UAV team and initiate other mission-related commands without the need for an additional communication channel or specific markers. Together with a novel unified human detection and tracking algorithm, human pose estimation approach and gesture detection pipeline, the proposed approach forms a first instance of an HSI system incorporating all these modules onboard real-world UAVs. Simulations and field experiments with three UAVs and a human worker in a mock-up scenario showcase the effectiveness and responsiveness of the proposed approach.

DoorDash just announced the launch of a drone delivery pilot program, in partnership with Alphabet's Wing. The testing began in Christiansburg, VA (approximate population 22,000), and is limited to only "eligible items" from fast food chain Wendy's. Whether that includes the iconic Frosty dessert/fry condiment is as yet unclear. There's only one affiliated Wendy's location, but local consumers should see a "deliver by drone" tab on the DoorDash checkout page. The company says orders should arrive in 30 minutes or less, making high-flying drones about as fast as a standard pizza delivery in the 1980s.

Research and development in Unmanned Aerial Vehicles (UAVs) or Unmanned Aircraft Systems (UAS) has witnessed unprecedented scientific and commercial interest and growth, particularly during the last two decades. Although military applications dominated the global market for years, interest in using UAVs in civil and public domains increases exponentially, worldwide, albeit challenges related to integrating unmanned aviation into the national airspace. Sample applications include, but are not limited to, surveillance [1], search and rescue [2], aerial photography [3], fire monitoring [4], agriculture [5], and aerial delivery [6]. The listed applications refer to solely passive tasks, that is, tasks in which no UAV interaction with the environment is needed. However, contact with the environment is required in industrial and maintenance applications like bridge inspection, water damn inspection, high-voltage transmission line inspection [7], assembly tasks [8] or construction [9].

In this paper, we explore the application of Unmanned Aerial Vehicles (UAVs) in maritime search and rescue (mSAR) missions, focusing on medium-sized fixed-wing drones and quadcopters. We address the challenges and limitations inherent in operating some of the different classes of UAVs, particularly in search operations. Our research includes the development of a comprehensive software framework designed to enhance the efficiency and efficacy of SAR operations. This framework combines preliminary detection onboard UAVs with advanced object detection at ground stations, aiming to reduce visual strain and improve decision-making for operators. It will be made publicly available upon publication. We conduct experiments to evaluate various Region of Interest (RoI) proposal methods, especially by imposing simulated limited bandwidth on them, an important consideration when flying remote or offshore operations. This forces the algorithm to prioritize some predictions over others.

An autonomous drone with wheels can roll along the ground, only flying when needed to clear obstacles, which helps its battery last seven times longer, according to its developers. Rolling robots are efficient and can travel long distances, but cannot traverse big obstacles, while flying drones can get past large obstructions, but have limited range.

Swarm robots have sparked remarkable developments across a range of fields. While it is necessary for various applications in swarm robots, a fast and robust coordinate initialization in vision-based drone swarms remains elusive. To this end, our paper proposes a complete system to recover a swarm's initial relative pose on platforms with size, weight, and power (SWaP) constraints. To overcome limited coverage of field-of-view (FoV), the drones rotate in place to obtain observations. To tackle the anonymous measurements, we formulate a non-convex rotation estimation problem and transform it into a semi-definite programming (SDP) problem, which can steadily obtain global optimal values. Then we utilize the Hungarian algorithm to recover relative translation and correspondences between observations and drone identities. To safely acquire complete observations, we actively search for positions and generate feasible trajectories to avoid collisions. To validate the practicability of our system, we conduct experiments on a vision-based drone swarm with only stereo cameras and inertial measurement units (IMUs) as sensors. The results demonstrate that the system can robustly get accurate relative poses in real time with limited onboard computation resources. The source code is released.

The paper focuses on modeling and experimental evaluation of a quadcopter team configurable coordination guided by a single quadruped robot. We consider the quadcopter team as particles of a two-dimensional deformable body and propose a two-dimensional affine transformation model for safe and collision-free configurable coordination of this heterogeneous robotic system. The proposed affine transformation is decomposed into translation, that is specified by the quadruped global position, and configurable motion of the quadcopters, which is determined by a nonsingular Jacobian matrix so that the quadcopter team can safely navigate a constrained environment while avoiding collision. We propose two methods to experimentally evaluate the proposed heterogeneous robot coordination model. The first method measures real positions of quadcopters, quadruped, and environmental objects all with respect to the global coordinate system. On the other hand, the second method measures position with respect to the local coordinate system fixed on the dog robot which in turn enables safe planning the Jacobian matrix of the quadcopter team while the world is virtually approached the robotic system.

Autonomous collision avoidance requires accurate environmental perception; however, flight systems often possess limited sensing capabilities with field-of-view (FOV) restrictions. To navigate this challenge, we present a safety-aware approach for online determination of the optimal sensor-pointing direction $\psi_\text{d}$ which utilizes control barrier functions (CBFs). First, we generate a spatial density function $\Phi$ which leverages CBF constraints to map the collision risk of all local coordinates. Then, we convolve $\Phi$ with an attitude-dependent sensor FOV quality function to produce the objective function $\Gamma$ which quantifies the total observed risk for a given pointing direction. Finally, by finding the global optimizer for $\Gamma$, we identify the value of $\psi_\text{d}$ which maximizes the perception of risk within the FOV. We incorporate $\psi_\text{d}$ into a safety-critical flight architecture and conduct a numerical analysis using multiple simulated mission profiles. Our algorithm achieves a success rate of $88-96\%$, constituting a $16-29\%$ improvement compared to the best heuristic methods. We demonstrate the functionality of our approach via a flight demonstration using the Crazyflie 2.1 micro-quadrotor. Without a priori obstacle knowledge, the quadrotor follows a dynamic flight path while simultaneously calculating and tracking $\psi_\text{d}$ to perceive and avoid two static obstacles with an average computation time of 371 $\mu$s.

Eerie new drone footage has for the first time revealed the extent of the damage to the Fukushima nuclear power plant 13 years on from its meltdown. The plant's operators, Tokyo Electric Power Company Holdings, or TEPCO, released 12 photos from inside the site, which are the first ever images from inside the main structural support called the pedestal in the hardest-hit reactor's primary containment vessel, an area directly under the reactor's core. Officials had long hoped to reach the area to examine the core and melted nuclear fuel which dripped there when the plant's cooling systems were damaged by a massive earthquake and tsunami in 2011. The high-definition color images captured by the drones show brown objects with various shapes and sizes dangling from various locations in the pedestal. Parts of the control-rod drive mechanism, which controls the nuclear chain reaction, and other equipment attached to the core were dislodged by the drones. The Fukushima disaster was one of the world's most devastating nuclear mishaps The plant's operators, Tokyo Electric Power Company Holdings, or TEPCO, released 12 photos from inside the site TEPCO officials said they were unable to tell from the images whether the dangling lumps were melted fuel or melted equipment without obtaining other data such as radiation levels.

We present CNER-UAV, a fine-grained \textbf{C}hinese \textbf{N}ame \textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task of address resolution in \textbf{U}nmanned \textbf{A}erial \textbf{V}ehicle delivery systems. The dataset encompasses a diverse range of five categories, enabling comprehensive training and evaluation of NER models. To construct this dataset, we sourced the data from a real-world UAV delivery system and conducted a rigorous data cleaning and desensitization process to ensure privacy and data integrity. The resulting dataset, consisting of around 12,000 annotated samples, underwent human experts and \textbf{L}arge \textbf{L}anguage \textbf{M}odel annotation. We evaluated classical NER models on our dataset and provided in-depth analysis. The dataset and models are publicly available at \url{https://github.com/zhhvvv/CNER-UAV}.