From Bench to Flight: Translating Drone Impact Tests into Operational Safety Limits
Lab INIT Robots, Department of Mechanical Engineering, École de technologie supérieure, Montreal, QC, Canada（Lab INIT Robots，机械工程系，École de technologie supérieure，蒙特利尔，QC，加拿大）
Aziz Mohamed Mili, Louis Catar, Paul Gérard, Ilyass Tabiai, David St-Onge
Indoor micro-aerial vehicles (MAVs) are increasingly used for tasks that require close proximity to people, yet practitioners lack practical methods to tune motion limits based on measured impact risk. We present an end-to-end, open toolchain that converts benchtop impact tests into deployable safety governors for drones. First, we describe a compact and replicable impact rig and protocol for capturing force-time profiles across drone classes and contact surfaces. Second, we provide data-driven models that map pre-impact speed to impulse and contact duration, enabling direct computation of speed bounds for a target force limit. Third, we release scripts and a ROS2 node that enforce these bounds online and log compliance, with support for facility-specific policies. We validate the workflow on multiple commercial off-the-shelf quadrotors and representative indoor assets, demonstrating that the derived governors preserve task throughput while meeting force constraints specified by safety stakeholders. Our contribution is a practical bridge from measured impacts to runtime limits, with shareable datasets, code, and a repeatable process that teams can adopt to certify indoor MAV operations near humans.
FZI Research Center for Information Technology（FZI信息技术研究中心）；TU Dortmund University（多特蒙德工业大学）
We introduce a mission planning concept for routing unmanned aerial vehicles (UAVs) through a set of sampling locations in the immediate aftermath of an incident such as a fire or chemical accident. Using interpolation methods that account for the spatial interdependencies inherent in the surveyed phenomenon, these samples allow predicting the distribution of hazardous substances across the affected area. We define the generalized correlated team orienteering problem (GCorTOP) for selecting {informative} samples considering spatial correlations between observed and unobserved locations as well…
Enhancing Drone Light Shows Performances: Optimal Allocation and Trajectories for Swarm Drone Formations
Aerospace Engineering Department, Faculty of Aeronautics and Astronautics, Kocaeli University（航空航天工程系，航空航天学院，科伊亚利大学）；Interdisciplinary Research Centre for Aviation and Space Exploration (IRC-ASE), KFUPM（航空与航天探索交叉研究中心（IRC-ASE），国王费萨尔大学）
FAIR^2 Drones: An AI-Ready Standard for Cross-Domain Wildlife Drone Datasets
The Ohio State University, Department of Computer Science and Engineering（俄亥俄州立大学计算机科学与工程系）；School of Civil, Aerospace and Design Engineering, University of Bristol（布里斯托尔大学土木、航空航天与设计工程学院）；D Optical Metrology (3DOM), Fondazione Bruno Kessler (FBK)（3DOM光学计量（3DOM），布鲁诺·克塞勒基金会（FBK））；Computer Vision and Machine Learning Systems Group, Institute for Geoinformatics, University of Muenster（计算机视觉与机器学习系统组，地理信息研究所，穆恩斯特大学）；Unmanned Aerial Systems Center, University of Southern Denmark（无人飞行系统中心，南部丹麦大学）；Department of Collective Behavior, Max Planck Institute of Animal Behavior（集体行为部门，动物行为马克斯·普朗克研究所）；Department of Biology, University of Konstanz（生物学系，康斯坦茨大学）；Department of Biology, University of Southern Denmark（生物学系，南部丹麦大学）
Open-World Drone Active Tracking with Goal-Centered Rewards
South China University of Technology（华南理工大学）；Institute for Super Robotics (Huangpu)（超机器人研究所）；Pazhou Laboratory（琶洲实验室）；Key Laboratory of Big Data and Intelligent Robot, Ministry of Education（大数据与智能机器人重点实验室）；Peng Cheng Laboratory（鹏城实验室）；Sun Yat-sen University（中山大学）；Harbin Engineering University（哈尔滨工程大学）
Microsoft（微软）；Stanford University（斯坦福大学）；University of Zurich and ETH（苏黎世大学和苏黎世联邦理工学院）
Autonomous drone racing is a challenging research problem at the intersection of computer vision, planning, state estimation, and control. We introduce AirSim Drone Racing Lab, a simulation framework for enabling fast prototyping of algorithms for autonomy and enabling machine learning research in this domain, with the goal of reducing the time, money, and risks associated with field robotics. Our framework enables generation of racing tracks in multiple photo-realistic environments, orchestration of drone races, comes with a suite of gate assets, allows for multiple sensor modalities (monocul…
Obstacle avoidance is an essential topic in the field of autonomous drone research. When choosing an avoidance algorithm, many different options are available, each with their advantages and disadvantages. As there is currently no consensus on testing methods, it is quite challenging to compare the performance between algorithms. In this paper, we propose AvoidBench, a benchmarking suite which can evaluate the performance of vision-based obstacle avoidance algorithms by subjecting them to a series of tasks. Thanks to the high fidelity of multi-rotors dynamics from RotorS and virtual scenes of …
Dublin City University（都柏林城市大学）；Technical University of Berlin（柏林工业大学）；Einstein Center Digital Future（爱因斯坦数字未来中心）
Creating an appropriate energy consumption prediction model is becoming an important topic for drone-related research in the literature. However, a general consensus on the energy consumption model is yet to be reached at present. As a result, there are many variations that attempt to create models that range in complexity with a focus on different aspects. In this paper, we benchmark the five most popular energy consumption models for drones derived from their physical behaviours and point to the difficulties in matching with a realistic energy dataset collected from a delivery drone in fligh…
As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios, radars must perform well in dense scenes with a large number of neighboring scatterers. Central to radar performance is the detection algorithm used to separate targets from the background noise and clutter. Traditionally, most radar systems use conventional CFAR detectors but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in non-linear target detection, we propose a n…
RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification
School of Information Science and Engineering, Zhejiang Sci-Tech University（浙江理工大学信息科学与工程学院）；School of Communication and Information Engineering, Hangzhou Dianzi University（杭州电子科技大学通信与信息工程学院）；College of Electronic Engineering, National University of Defense Technology（国防科技大学电子工程学院）
Utilizing autonomous drones or unmanned aerial vehicles (UAVs) has shown great advantages over preceding methods in support of urgent scenarios such as search and rescue (SAR) and wildfire detection. In these operations, search efficiency in terms of the amount of time spent to find the target is crucial since with the passing of time the survivability of the missing person decreases or wildfire management becomes more difficult with disastrous consequences. In this work, it is considered a scenario where a drone is intended to search and detect a missing person (e.g., a hiker or a mountaineer…
Learning on the Fly: Replay-Based Continual Object Perception for Indoor Drones
National University of Science and Technology Politehnica Bucharest, Romania（罗马尼亚布加勒斯特多项技术大学）；Institute of Mathematics ``Simion Stoilow'' of the Romanian Academy, Romania（罗马尼亚科学院模拟昂斯数学研究所）；NORCE Norwegian Research Center, Norway（挪威NORCE研究中心）
Fly360: Omnidirectional Obstacle Avoidance within Drone View
Institute of Automation Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所）；Insta360 Research, Shenzhen, China（Insta360研究院）；Wuhan University,Wuhan, China（武汉大学）
Automatic reconstruction of 3D models from images using multi-view Structure-from-Motion methods has been one of the most fruitful outcomes of computer vision. These advances combined with the growing popularity of Micro Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools ubiquitous for large number of Architecture, Engineering and Construction applications among audiences, mostly unskilled in computer vision. However, to obtain high-resolution and accurate reconstructions from a large-scale object using SfM, there are many critical constraints on the quality of image …
Neural Network Verification for Gliding Drone Control: A Case Study
Heriot-Watt University and Edinburgh Centre for Robotics（赫瑞-沃里克大学和爱丁堡机器人中心）；School of Engineering, University of Edinburgh（爱丁堡大学工程学院）；Maynooth University（迈诺特大学）
VRUD: A Drone Dataset for Complex Vehicle-VRU Interactions within Mixed Traffic
National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University（吉林大学汽车底盘集成与仿生全国重点实验室）；DRIVEResearch；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）；Department of Computer Science, Durham University（杜伦大学计算机科学系）
Istituto Italiano di Tecnologia（意大利理工学院）；Politecnico di Milano（米兰理工大学）
Jet-powered vertical takeoff and landing (VTOL) drones require precise thrust estimation to ensure adequate stability margins and robust maneuvering. Small-scale turbojets have become good candidates for powering heavy aerial drones. However, due to limited instrumentation available in these turbojets, estimating the precise thrust using classical techniques is not straightforward. In this paper, we present a methodology to accurately estimate the online thrust for the small-scale turbojets used on the iRonCub - an aerial humanoid robot. We use a grey-box method to capture the turbojet system …
University of California San Diego（加利福尼亚大学圣地亚哥分校）；Kyung Hee University（庆熙大学）
Innovations from surgical robotic research rarely translates to live surgery due to the significant difference between the lab and a live environment. Live environments require considerations that are often overlooked during early stages of research such as surgical staff, surgical procedure, and the challenges of working with live tissue. One such example is the da Vinci Research Kit (dVRK) which is used by over 40 robotics research groups and represents an open-sourced version of the da Vinci Surgical System. Despite dVRK being available for nearly a decade and the ideal candidate for transl…
