Recent huge technological development of Unmanned Aerial Vehicles (UAVs) can provide breakthrough means of fighting wildland fires. We propose an innovative forest firefighting system based on the use of a swarm of hundreds of UAVs able to generate a continuous flow of extinguishing liquid on the fire front, simulating rain effect. Automatic battery replacement and refilling of the extinguishing liquid ensure the continuity of the action, and fire-resistant materials protect drones exposed to possible high temperatures. We demonstrate the validity of the approach in Mediterranean scrub first computing the critical water flow rate according to the main factors involved in the evolution of a fire, then estimating the number of linear meters of active fire front that can be extinguished depending on the number of drones available and the amount of extinguishing fluid carried. A fire propagation cellular automata model is also employed to study the evolution of the fire. Simulation results suggest that the proposed system can successfully integrate, or in case of low-intensity and limited extent fires completely replace, current forest firefighting techniques.
Drone swarms are required for the simultaneous delivery of multiple packages. We demonstrate a multi-stop drone swarm-based delivery in a smart city. We leverage formation flying to conserve energy and increase the flight range of a drone swarm. An adaptive formation is presented in which a swarm adjusts to extrinsic constraints and changes the formation pattern in-flight. We utilize the existing building rooftops in a city and build a line-of-sight skyway network to safely operate the swarms. We use a heuristic-based A* algorithm to route a drone swarm in a skyway network.
The paper focuses on a heterogeneous swarm of drones to achieve a dynamic landing of formation on a moving robot. This challenging task was not yet achieved by scientists. The key technology is that instead of facilitating each agent of the swarm of drones with computer vision that considerably increases the payload and shortens the flight time, we propose to install only one camera on the leader drone. The follower drones receive the commands from the leader UAV and maintain a collision-free trajectory with the artificial potential field. The experimental results revealed a high accuracy of t…
Swarm of drones are increasingly being asked to carry out missions that can't be completed by one drone. Particularly, in delivery, issues arise due to the swarm's limited flight endurance. Hence, we propose a novel formation-guided framework for selecting Swarm-based Drone-as-a-Service (SDaaS) for delivery. A detailed study is carried out to highlight the effect of swarm formations on energy consumption. Two SDaaS selection approaches, i.e. Fixed and Adaptive, are designed considering the different formation decisions a swarm can take. The proposed framework considers extrinsic constraints in…
Drone swarms are teams of autonomous un-manned aerial vehicles that act as a collective entity. We are interested in humanizing drone swarms, equipping them with the ability to emotionally affect human users through their non-verbal motions. Inspired by recent findings in how observers are emotionally touched by watching dance moves, we investigate the questions of whether and how coordinated drone swarms' motions can achieve emotive impacts on general audience. Our preliminary study on Amazon Mechanical Turk led to a number of interesting findings, including both promising results and challen…
We demonstrate formation flying for drone swarm services. A set of drones fly in four different swarm formations. A dataset is collected to study the effect of formation flying on energy consumption. We conduct a set of experiments to study the effect of wind on formation flying. We examine the forces the drones exert on each other when flying in a formation. We finally identify and classify the formations that conserve most energy under varying wind conditions. The collected dataset aims at providing researchers data to conduct further research in swarm-based drone service delivery. Demo: htt…
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Laboratory of Intelligent Systems (LIS)（智能系统实验室）
Among the available solutions for drone swarm simulations, we identified a gap in simulation frameworks that allow easy algorithms prototyping, tuning, debugging and performance analysis, and do not require the user to interface with multiple programming languages. We present SwarmLab, a software entirely written in Matlab, that aims at the creation of standardized processes and metrics to quantify the performance and robustness of swarm algorithms, and in particular, it focuses on drones. We showcase the functionalities of SwarmLab by comparing two state-of-the-art algorithms for the navigati…
Zhejiang University（浙江大学）；Zhejiang University of Technology（浙江工业大学）；Northwestern Polytechnical University（西北工业大学）
Autonomous drone racing is becoming an excellent platform to challenge quadrotors' autonomy techniques including planning, navigation and control technologies. However, most research on this topic mainly focuses on single drone scenarios. In this paper, we describe a novel time-optimal trajectory generation method for generating time-optimal trajectories for a swarm of quadrotors to fly through pre-defined waypoints with their maximum maneuverability without collision. We verify the method in the Gazebo simulations where a swarm of 5 quadrotors can fly through a complex 6-waypoint racing track…
We propose a novel framework for swarm-based drone delivery services with in-flight energy recharging. The framework aims to enhance the delivery time of multiple packages by reducing the number of stops and recharging times at intermediate stations. The proposed framework considers various intrinsic and extrinsic delivery constraints. We propose to use support drones whose sole purpose is to recharge other drones in the swarm during their flight. In this respect, we compute the optimal set of optimal support drones to minimize the probability of delivery services and recharging time at the ne…
We propose a novel framework for the recommendation of swarm-based drone delivery services based on the consumers preferences. We propose a density-based pruning approach that uses the concept of partnerships with charging station providers to reduce the search space of swarm-based drone service delivery providers. A weighted service composition algorithm is proposed that considers the providers capabilities and consumers' preferences in selecting the best next service. We propose a voting-based recommendation algorithm to select the best providers. We conduct a set of experiments to evaluate …
Anthropomorphic robot avatars present a conceptually novel approach to remote affective communication, allowing people across the world a wider specter of emotional and social exchanges over traditional 2D and 3D image data. However, there are several limitations of current telepresence robots, such as the high weight, complexity of the system that prevents its fast deployment, and the limited workspace of the avatars mounted on either static or wheeled mobile platforms. In this paper, we present a novel concept of telecommunication through a robot avatar based on an anthropomorphic swarm of d…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
We propose a novel human-swarm interaction system, allowing the user to directly control a swarm of drones in a complex environment through trajectory drawing with a hand gesture interface based on the DNN-based gesture recognition. The developed CV-based system allows the user to control the swarm behavior without additional devices through human gestures and motions in real-time, providing convenient tools to change the swarm's shape and formation. The two types of interaction were proposed and implemented to adjust the swarm hierarchy: trajectory drawing and free-form trajectory generation …
We demonstrate how efficient autonomous drone swarms can be in detecting and tracking occluded targets in densely forested areas, such as lost people during search and rescue missions. Exploration and optimization of local viewing conditions, such as occlusion density and target view obliqueness, provide much faster and much more reliable results than previous, blind sampling strategies that are based on pre-defined waypoints. An adapted real-time particle swarm optimization and a new objective function are presented that are able to deal with dynamic and highly random through-foliage conditio…
As a fire erupts, the first few minutes can be critical, and first respondents must race to the scene to analyze the situation and act fast before it gets out of hand. Factors such as road traffic condition and distance may not allow quick rescue operation using traditional means and methods, leading to unmanageable spreading of fire, injuries or even deaths that can be avoided. FireFly drone-based rescue consists of a squad of highly equipped drones that will be the first responders to the fire site. Their intervention will make the task of the fire rescue team much more effective and will co…
University of Turku（图尔库大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；King Faisal University（费萨尔国王大学）
This work focuses on the formation reshaping in an optimized manner in autonomous swarm of drones. Here, the two main problems are: 1) how to break and reshape the initial formation in an optimal manner, and 2) how to do such reformation while minimizing the overall deviation of the drones and the overall time, i.e., without slowing down. To address the first problem, we introduce a set of routines for the drones/agents to follow while reshaping to a secondary formation shape. And the second problem is resolved by utilizing the temperature function reduction technique, originally used in the p…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
Heterogeneous teams of mobile robots and UAVs are offering a substantial benefit in an autonomous exploration of the environment. Nevertheless, although joint exploration scenarios for such systems are widely discussed, they are still suffering from low adaptability to changes in external conditions and faults of swarm agents during the UAV docking. We propose a novel vision-based drone swarm docking system for robust landing on a moving platform when one of the agents lost its position signal. The proposed SwarmHawk system relies on vision-based detection for the mobile platform tracking and …
Drones are not fully trusted yet. Their reliance on radios and cameras for navigation raises safety and privacy concerns. These systems can fail, causing accidents, or be misused for unauthorized recordings. Considering recent regulations allowing commercial drones to operate only at night, we propose a radically new approach where drones obtain navigation information from artificial lighting. In our system, standard light bulbs modulate their intensity to send beacons and drones decode this information with a simple photodiode. This optical information is combined with the inertial and altitu…
Using Drone Swarm to Stop Wildfire: A Predict-then-optimize Approach
The University of Hong Kong（香港大学）；Shenzhen University（深圳大学）；Amazon（亚马逊公司）；The University of California, Berkeley（加州大学伯克利分校）；Johns Hopkins University（约翰斯·霍普金斯大学）
University of Leeds（利兹大学）；University of Sheffield（谢菲尔德大学）
This paper introduces a testbed to study distributed sensing problems of Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Several Smart City applications, such as transport and disaster response, require efficient collection of sensor data by a swarm of intelligent and cooperative UAVs. This often proves to be too complex and costly to study systematically and rigorously without compromising scale, realism and external validity. With the proposed testbed, this paper sets a stepping stone to emulate, within small laboratory spaces, large sensing areas of interest originated from e…
Skoltech（斯科尔科沃科技学院）；Imperial College London（伦敦帝国学院）
To achieve high fidelity haptic rendering of soft objects in a high mobility virtual environment, we propose a novel haptic display DandelionTouch. The tactile actuators are delivered to the fingertips of the user by a swarm of drones. Users of DandelionTouch are capable of experiencing tactile feedback in a large space that is not limited by the device's working area. Importantly, they will not experience muscle fatigue during long interactions with virtual objects. Hand tracking and swarm control algorithm allow guiding the swarm with hand motions and avoid collisions inside the formation. S…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
We propose a concept of a novel interaction strategy for providing rich haptic feedback in Virtual Reality (VR), when each user's finger is connected to micro-quadrotor with a wire. Described technology represents the first flying wearable haptic interface. The solution potentially is able to deliver high resolution force feedback to each finger during fine motor interaction in VR. The tips of tethers are connected to the centers of quadcopters under their bottom. Therefore, flight stability is increasing and the interaction forces are becoming stronger which allows to use smaller drones.
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）
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This work proposes an entirely visual approach to coordinate markerless drone swarms based on imitation learning. Each agent is controlled by a small and efficient convolutional neural network that takes raw omnidirectional images as inputs and predicts 3D velocity commands that match those computed by a flocking algorithm. We start training in simulation and propose a simple yet effective unsupervised domain adaptation approach to transfer the …
Flocking phase transition and threat responses in bio-inspired autonomous drone swarms
École Nationale de l’Aviation Civile, Université de Toulouse, France（法国国家民用航空学院，图卢兹大学）；Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS) & Université de Toulouse – Paul Sabatier, France（动物认知研究中心，整合生物学研究所（CBI），法国国家科学研究中心（CNRS）及图卢兹大学–保罗·萨巴蒂埃大学，法国）；Laboratoire de Physique Théorique, CNRS & Université de Toulouse – Paul Sabatier, France（理论物理实验室，法国国家科学研究中心（CNRS）及图卢兹大学–保罗·萨巴蒂埃大学，法国）
The vision-based relative localization can provide effective feedback for the cooperation of aerial swarm and has been widely investigated in previous works. However, the limited field of view (FOV) inherently restricts its performance. To cope with this issue, this letter proposes a novel distributed active vision-based relative localization framework and apply it to formation control in aerial swarms. Inspired by bird flocks in nature, we devise graph-based attention planning (GAP) to improve the observation quality of the active vision in the swarm. Then active detection results are fused w…
This paper proposes a new machine learning based system for forest fire earlier detection in a low-cost and accurate manner. Accordingly, it is aimed to bring a new and definite perspective to visual detection in forest fires. A drone is constructed for this purpose. The microcontroller in the system has been programmed by training with deep learning methods, and the unmanned aerial vehicle has been given the ability to recognize the smoke, the earliest sign of fire detection. The common problem in the prevalent algorithms used in fire detection is the high false alarm and overlook rates. Conf…
Preserving Relative Localization of FoV-Limited Drone Swarm via Active Mutual Observation
Huzhou Institute, Zhejiang University（浙江大学湖州研究院）；Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院 cyber系统与控制研究所）；The Hong Kong University of Science and Technology (GZ)（香港科技大学（广州））
Exploring Unstructured Environments using Minimal Sensing on Cooperative Nano-Drones
Computer Vision and Aerial Robotics Group at Universidad Politécnica de Madrid（马德里理工大学计算机视觉与空中机器人小组）；Centre for Automation and Robotics C.A.R. (UPM-CSIC)（自动化与机器人中心（UPM-CSIC））；Department of Mechanical Engineering, Texas A&M University（机械工程系，德克萨斯A&M大学）
Optimal Wildfire Escape Route Planning for Drones under Dynamic Fire and Smoke
Budapest University of Technology and Economics（布达佩斯技术与经济大学）；HUN-REN Institute for Computer Science and Control (SZTAKI)（HUN-REN计算机科学与控制研究所）；Machine Perception Research Laboratory（机器感知研究实验室）
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
Reinforcement learning (RL) methods have been actively applied in the field of robotics, allowing the system itself to find a solution for a task otherwise requiring a complex decision-making algorithm. In this paper, we present a novel RL-based Tic-tac-toe scenario, i.e. SwarmPlay, where each playing component is presented by an individual drone that has its own mobility and swarm intelligence to win against a human player. Thus, the combination of challenging swarm strategy and human-drone collaboration aims to make the games with machines tangible and interactive. Although some research on …
Optimizing Forest Fire Prevention: Intelligent Scheduling Algorithms for Drone-Based Surveillance System
Majmaah University（马杰马阿大学）；University of Sousse（苏塞大学）；Monastir University（莫纳斯提尔大学）；University of Hafr Al Batin（哈夫尔阿尔巴廷大学）；Prince Sultan University（苏丹王子大学）；University of Manouba（马努巴大学）；University of Tunis（突尼斯大学）；Qassim University（卡西姆大学）
AirSwarm: Enabling Cost-Effective Multi-UAV Research with COTS drones
Centre for Advanced Robotics Technology Innovation (CARTIN)（高级机器人技术创新中心（CARTIN））；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
Land & Localize: An Infrastructure-free and Scalable Nano-Drones Swarm with UWB-based Localization
University of Bologna（博洛尼亚大学）；Technology Innovation Institute（技术创新研究所）；KU Leuven（荷语鲁汶大学）；ETH Zürich（苏黎世联邦理工学院）；Dalle Molle Institute for Artificial Intelligence (IDSIA)（达勒莫勒人工智能研究所（IDSIA））；USI-SUPSI（瑞士意大利语区大学-瑞士南部应用科学与艺术大学）
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
This paper presents a data-driven approach to learning vision-based collective behavior from a simple flocking algorithm. We simulate a swarm of quadrotor drones and formulate the controller as a regression problem in which we generate 3D velocity commands directly from raw camera images. The dataset is created by simultaneously acquiring omnidirectional images and computing the corresponding control command from the flocking algorithm. We show that a convolutional neural network trained on the visual inputs of the drone can learn not only robust collision avoidance but also coherence of the f…
