Over the past two decades, Unmanned Aerial Vehicles (UAVs), more commonly known as drones, have gained a lot of attention, and are rapidly becoming ubiquitous because of their diverse applications such as surveillance, disaster management, pollution monitoring, film-making, and military reconnaissance. However, incidents such as fatal system failures, malicious attacks, and disastrous misuses have raised concerns in the recent past. Security and viability concerns in drone-based applications are growing at an alarming rate. Besides, UAV networks (UAVNets) are distinctive from other ad-hoc networks. Therefore, it is necessary to address these issues to ensure proper functioning of these UAVs while keeping their uniqueness in mind. Furthermore, adequate security and functionality require the consideration of many parameters that may include an accurate cognizance of the working mechanism of vehicles, geographical and weather conditions, and UAVNet communication. This is achievable by creating a simulator that includes these aspects. A performance evaluation through relevant drone simulator becomes indispensable procedure to test features, configurations, and designs to demonstrate superiority to comparative schemes and suitability. Thus, it becomes of paramount importance to establish the credibility of simulation results by investigating the merits and limitations of each simulator prior to selection. Based on this motivation, we present a comprehensive survey of current drone simulators. In addition, open research issues and research challenges are discussed and presented.
Droneulator: A Portable UAV Simulator for Agricultural Workflows with RotorPy and Godot 4
Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln, Lincoln, UK（林肯自主系统中心（L-CAS）、林肯大学、林肯，英国）；Faculty of Aerospace Engineering, MAVLab, Delft University of Technology (TU Delft), Delft, Netherlands（航空航天工程学院、MAVLab、代尔夫特理工大学（TU Delft）、代尔夫特，荷兰）
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…
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Most human-robot interfaces, such as joysticks and keyboards, require training and constant cognitive effort and provide a limited degree of awareness of the robots state and its environment. Embodied interactions, instead of interfaces, could bridge the gap between humans and robots, allowing humans to naturally perceive and act through a distal robotic body. Establishing an embodied interaction and mapping human movements and a non-anthropomorphic robot is particularly challenging. In this paper, we describe a natural and immersive embodied interaction that allows users to control and experi…
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心）；FSTM/DCS, University of Luxembourg（卢森堡大学科学、技术与医学学院计算机科学系）
Mechanical and Industrial Engineering Department, Northeastern University（东北大学机械与工业工程学院）
Targets search and detection encompasses a variety of decision problems such as coverage, surveillance, search, observing and pursuit-evasion along with others. In this paper we develop a multi-agent deep reinforcement learning (MADRL) method to coordinate a group of aerial vehicles (drones) for the purpose of locating a set of static targets in an unknown area. To that end, we have designed a realistic drone simulator that replicates the dynamics and perturbations of a real experiment, including statistical inferences taken from experimental data for its modeling. Our reinforcement learning m…
Humans race drones faster than algorithms, despite being limited to a fixed camera angle, body rate control, and response latencies in the order of hundreds of milliseconds. A better understanding of the ability of human pilots of selecting appropriate motor commands from highly dynamic visual information may provide key insights for solving current challenges in vision-based autonomous navigation. This paper investigates the relationship between human eye movements, control behavior, and flight performance in a drone racing task. We collected a multimodal dataset from 21 experienced drone pil…
Service delivery is set to experience a major paradigm shift with fast advances in drone technologies coupled with higher expectations from customers and increased competition. We propose a novel service-oriented approach to enable the ubiquitous delivery of packages in a drone-operated skyway network. We discuss the benefits, framework and architecture, contemporary approaches, open challenges and future visioned directions of service-based drone deliveries.
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）
Zhejiang University（浙江大学）；Huzhou Institute of Zhejiang University（浙江大学湖州研究院）；DJI（大疆）
With the autonomy of aerial robots advances in recent years, autonomous drone racing has drawn increasing attention. In a professional pilot competition, a skilled operator always controls the drone to agilely avoid obstacles in aggressive attitudes, for reaching the destination as fast as possible. Autonomous flight like elite pilots requires planning in SE(3), whose non-triviality and complexity hindering a convincing solution in our community by now. To bridge this gap, this paper proposes an open-source baseline, which includes a high-performance SE(3) planner and a challenging simulation …
In this work, we present a system that enables delivery drones to autonomously navigate and deliver packages at various locations around a house according to the desire of the recipient and without the need for any external markers as currently used. This development is motivated by recent advancements in deep learning that can potentially supplant the specialized markers presently used by delivery drones for identifying sites at which to deliver packages. The proposed system is more natural in that it takes instruction on where to deliver the package as input, similar to the instructions prov…
Missouri Univ. of Science and Technology（密苏里科技大学）；Sapienza Univ. of Rome（罗马大学）；Univ. of Perugia（佩鲁贾大学）
In this paper, we study the feasibility of sending drones to deliver goods from a depot to a customer by solving what we call the Mission-Feasibility Problem (MFP). Due to payload constraints, the drone can serve only one customer at a time. To this end, we propose a novel framework based on time-dependent cost graphs to properly model the MFP and tackle the delivery dynamics. When the drone moves in the delivery area, the global wind may change thereby affecting the drone's energy consumption, which in turn can increase or decrease. This issue is addressed by designing three algorithms, namel…
Rigid grippers used in existing aerial manipulators require precise positioning to achieve successful grasps and transmit large contact forces that may destabilize the drone. This limits the speed during grasping and prevents "dynamic grasping", where the drone attempts to grasp an object while moving. On the other hand, biological systems (e.g., birds) rely on compliant and soft parts to dampen contact forces and compensate for grasping inaccuracy, enabling impressive feats. This paper presents the first prototype of a soft drone -- a quadrotor where traditional (i.e., rigid) landing gears ar…
Over 600,000 bridges in the U.S. must be inspected every two years to identify flaws, defects, or potential problems that may need follow-up maintenance. Bridge inspection has adopted unmanned aerial vehicles (or drones) for improving safety, efficiency, and cost-effectiveness. Although drones can operate in an autonomous mode, keeping inspectors in the loop is critical for complex tasks in bridge inspection. Therefore, inspectors need to develop the skill and confidence to operate drones in their jobs. This paper presents the design and development of a virtual reality-based training and asse…
Georgia Institute of Technology（佐治亚理工学院）；University of Maryland, College Park（马里兰大学帕克分校）；Carnegie Mellon University（卡内基梅隆大学）；Qatar University（卡塔尔大学）
Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. To achieve the full potential of such drones, it is necessary to develop new enhanced formulations of both common and emerging sensing scenarios. Namely, several fundamental challenges in visual sensing remain unsolved including: 1) Fitting sizable targets in camera frames; 2) Effective viewpoints matching target poses; 3) Occlusion by elements in the environment, including other targets. In this paper, we introduce Argus: an autonomous system that utilizes drones to incrementally …
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）
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）
TraQuad is an autonomous tracking quadcopter capable of tracking any moving (or static) object like cars, humans, other drones or any other object on-the-go. This article describes the applications and advantages of TraQuad and the reduction in cost (to about 250$) that has been achieved so far using the hardware and software capabilities and our custom algorithms wherever needed. This description is backed by strong data and the research analyses which have been drawn out of extant information or conducted on own when necessary. This also describes the development of completely autonomous (ev…
Grasping and releasing objects would cause oscillations to delivery drones in the warehouse. To reduce such undesired oscillations, this paper treats the to-be-delivered object as an unknown external disturbance and presents an image-based disturbance observer (DOB) to estimate and reject such disturbance. Different from the existing DOB technique that can only compensate for the disturbance after the oscillations happen, the proposed image-based one incorporates image-based disturbance prediction into the control loop to further improve the performance of the DOB. The proposed image-based DOB…
Pose Graph Optimization for a MAV Indoor Localization Fusing 5GNR TOA with an IMU
Interdisciplinary Center for Security Reliability and Trust (SnT)（安全可靠性与信任跨学科中心（SnT））；University of Luxembourg（卢森堡大学）；Faculty of Science, Technology, and Medicine (FSTM)（科学、技术与医学学院（FSTM））
本研究针对室内无人机定位问题，提出融合5G NR TOA与IMU数据的位姿图优化方法，通过仿真生成12组测试序列验证，在充足基站与高带宽配置下轨迹平均误差小于15厘米，且算法可实现机载实时跟踪。
LLM-Land: Large Language Models for Context-Aware Drone Landing
Department of Electrical and Computer Engineering（电气与计算机工程系）；Drexel University（德雷塞尔大学）；Department of Electrical and Systems Engineering（电气与系统工程系）；University of Pennsylvania（宾夕法尼亚大学）
Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be related to the ability of human pilots to select task-relevant visual information effectively. This work investigates whether neural networks capable of imitating human eye gaze behavior and attention can improve neural network performance for the challenging task of vision-based autonomous drone racing. We hypothesize that gaze-based attention prediction can be an efficient mechanism for visual information selection and decision making in a simulator-based drone racing task. We test this hypot…
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 …
Unmanned Aerial Vehicles (UAV), commonly known as drones, have many potential uses in real world applications. Drones require advanced planning and navigation algorithms to enable them to safely move through and interact with the world around them. This paper presents an extended potential field controller (ePFC) which enables an aerial robot, or drone, to safely track a dynamic target location while simultaneously avoiding any obstacles in its path. The ePFC outperforms a traditional potential field controller (PFC) with smoother tracking paths and shorter settling times. The proposed ePFC's …
With the development of industry, drones are appearing in various field. In recent years, deep reinforcement learning has made impressive gains in games, and we are committed to applying deep reinforcement learning algorithms to the field of robotics, moving reinforcement learning algorithms from game scenarios to real-world application scenarios. We are inspired by the LunarLander of OpenAI Gym, we decided to make a bold attempt in the field of reinforcement learning to control drones. At present, there is still a lack of work applying reinforcement learning algorithms to robot control, the p…
Learning Resilient Formation Control of Drones with Graph Attention Network
School of Mechanical and Aerospace Engineering, Nanyang Technological University（机械与航空航天工程学院，南洋理工大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电气与电子工程学院，南洋理工大学）
Technion Israel Institute of Technology（以色列理工学院）；Rafael Advanced Defense Systems Ltd.（拉斐尔先进防御系统有限公司）
This paper considers the problem of finding a landing spot for a drone in a dense urban environment. The conflicting requirement of fast exploration and high resolution is solved using a multi-resolution approach, by which visual information is collected by the drone at decreasing altitudes so that spatial resolution of the acquired images increases monotonically. A probability distribution is used to capture the uncertainty of the decision process for each terrain patch. The distributions are updated as information from different altitudes is collected. When the confidence level for one of th…
With the rapid advance of sophisticated control algorithms, the capabilities of drones to stabilise, fly and manoeuvre autonomously have dramatically improved, enabling us to pay greater attention to entire missions and the interaction of a drone with humans and with its environment during the course of such a mission. In this paper, we present an indoor office drone assistant that is tasked to run errands and carry out simple tasks at our laboratory, while given instructions from and interacting with humans in the space. To accomplish its mission, the system has to be able to understand verba…
