Model checking has been used to verify the correctness of digital circuits, security protocols, communication protocols, as they can be modelled by means of finite state transition model. However, modelling the behaviour of hybrid systems like UAVs in a Kripke model is challenging. This work is aimed at capturing the behaviour of an UAV performing cooperative search mission into a Kripke model, so as to verify it against the temporal properties expressed in Computation Tree Logic (CTL). SMV model checker is used for the purpose of model checking.
UAV swarms have triggered wide concern due to their potential application values in recent years. While there are studies proposed in terms of the architecture design for UAV swarms, two main challenges still exist: (1) Scalability, supporting a large scale of vehicles; (2) Versatility, integrating diversified missions. To this end, a multi-layered and distributed architecture for mission oriented miniature fixed-wing UAV swarms is presented in this paper. The proposed architecture is built on the concept of modularity. It divides the overall system to five layers: low-level control, high-leve…
Instituto Balseiro - Universidad Nacional de Cuyo（巴尔塞罗研究所 - 库约国立大学）；Universidad de Buenos Aires（布宜诺斯艾利斯大学）；CONICET（国家科学技术研究委员会）；Imperial College London（伦敦帝国理工学院）
The design of systems that can change their behaviour to account for scenarios that were not foreseen at design time remains an open challenge. In this paper we propose an approach for adaptation of mobile robot missions that is not constrained to a predefined set of mission evolutions. We propose applying the MORPH adaptive software architecture to UAVs and show how controller synthesis can be used both to guarantee correct transitioning from the old to the new mission goals while architectural reconfiguration to include new software actuators and sensors if necessary. The architecture brings…
This study proposes an efficient data collection strategy exploiting a team of Unmanned Aerial Vehicles (UAVs) to monitor and collect the data of a large distributed sensor network usually used for environmental monitoring, meteorology, agriculture, and renewable energy applications. The study develops a collaborative mission planning system that enables a team of UAVs to conduct and complete the mission of sensors' data collection collaboratively while considering existing constraints of the UAV payload and battery capacity. The proposed mission planner system employs the Differential Evoluti…
We present a hybrid compositional approach for real-time mission planning for multi-rotor unmanned aerial vehicles (UAVs) in a time critical search and rescue scenario. Starting with a known environment, we specify the mission using Metric Temporal Logic (MTL) and use a hybrid dynamical model to capture the various modes of UAV operation. We then divide the mission into several sub-tasks by exploiting the invariant nature of safety and timing constraints along the way, and the different modes (i.e., dynamics) of the UAV. For each sub-task, we translate the MTL specifications into linear constr…
Democritus University of Thrace（色雷斯德谟克利特大学）；Information Technologies Institute, The Centre for Research & Technology, Hellas（希腊研究与技术中心信息技术研究所）；National Technical University（国立技术大学）
This paper proposes a novel mission planning platform, capable of efficiently deploying a team of UAVs to cover complex-shaped areas, in various remote sensing applications. Under the hood lies a novel optimization scheme for grid-based methods, utilizing Simulated Annealing algorithm, that significantly increases the achieved percentage of coverage and improves the qualitative features of the generated paths. Extensive simulated evaluation in comparison with a state-of-the-art alternative methodology, for coverage path planning (CPP) operations, establishes the performance gains in terms of a…
Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. This paper provides a framework for using reinforcement learning to allow the UAV to navigate successfully in such environments. We conducted our simulation and real implementation to show how the UAVs can successfully learn to navigate through an unknown environment. Technical aspects regarding to applying reinforcement learning algorithm to a UAV system and UAV flight control were also addressed. This will enable continuing research…
Indian Institute of Science（印度科学学院）；Missouri Univ. of Science & Tech.（密苏里科技大学）
Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urban applications like traffic monitoring and construction surveys. Autonomous navigation allows drones to visit waypoints and accomplish activities as part of their mission. A common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. To this end, for a fleet of drones, we propose a novel Mission Scheduling Problem (MSP) that co…
GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning
University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；Concordia University（康考迪亚大学）；McGill University（麦吉尔大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；California Institute of Technology（加州理工学院）
Nanyang Technological University（南洋理工大学）；Imperial College London（帝国理工学院）；International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
The expectation of unmanned air vehicles (UAVs) pushes the operation environment to narrow spaces, where the systems may fly very close to an object and perform an interaction. This phase brings the variation in UAV dynamics: thrust and drag coefficient of the propellers might change under different proximity. At the same time, UAVs may need to operate under external disturbances to follow time-based trajectories. Under these challenging conditions, a standard controller approach may not handle all missions with a fixed structure, where there may be a need to adjust its parameters for each dif…
The University of Manchester（曼彻斯特大学）；Cardiff University（卡迪夫大学）
In recent years, Unmanned Surface Vehicles (USV) have been extensively deployed for maritime applications. However, USV has a limited detection range with sensor installed at the same elevation with the targets. In this research, we propose a cooperative Unmanned Aerial Vehicle - Unmanned Surface Vehicle (UAV-USV) platform to improve the detection range of USV. A floatable and waterproof UAV is designed and 3D printed, which allows it to land on the sea. A catamaran USV and landing platform are also developed. To land UAV on the USV precisely in various lighting conditions, IR beacon detector …
Recently unmanned aerial vehicles (UAV) have been widely deployed in various real-world scenarios such as disaster rescue and package delivery. Many of these working environments are unstructured with uncertain and dynamic obstacles. UAV collision frequently happens. An UAV with high agility is highly desired to adjust its motions to adapt to these environmental dynamics. However, UAV agility is restricted by its battery power output; particularly, an UAV's power system cannot be aware of its actual power need in motion planning while the need is dynamically changing as the environment and UAV…
Seattle Children’s Research Institute, University of Washington（华盛顿大学西雅图儿童研究所）；Monash University（莫纳什大学）；University of Salford（索尔福德大学）
Advances in hardware technology have enabled more integration of sophisticated software, triggering progress in the development and employment of Unmanned Vehicles (UVs), and mitigating restraints for onboard intelligence. As a result, UVs can now take part in more complex mission where continuous transformation in environmental condition calls for a higher level of situational responsiveness. This paper serves as an introduction to UVs mission planning and management systems aiming to highlight some of the recent developments in the field of autonomous underwater and aerial vehicles in additi…
A Novel MDP Decomposition Framework for Scalable UAV Mission Planning in Complex and Uncertain Environments
Control and Instrumentation Department（控制与仪器部门）；Interdisciplinary Research Center for Intelligent Manufacturing and Robotics（智能制造与机器人交叉研究中心）；Interdisciplinary Research Center for Smart Mobility and Logistics（智能移动与物流交叉研究中心）
Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg.（自动化与机器人研究组，安全、可靠性与信任 interdisciplinary centre（SnT），卢森堡大学）
Hybrid Many-Objective Optimization in Probabilistic Mission Design for Compliant and Effective UAV Routing
Control Methods and Intelligent Systems Group, TU Darmstadt（控制方法与智能系统组，德累斯顿理工大学）；Honda Research Institute EU（本田欧洲研究院）；Uncertainty in Artificial Intelligence Group, TU Eindhoven（不确定性人工智能组，埃因霍温理工大学）
Queensland University of Technology（昆士兰科技大学）；Data61 / CSIRO（Data61 / 联邦科学与工业研究组织）；Griffith University（格里菲斯大学）
The use of multi-rotor Unmanned Aerial Vehicles (UAVs) for search and rescue as well as remote sensing is rapidly increasing. Multi-rotor UAVs, however, have limited endurance. The range of UAV applications can be widened if teams of multiple UAVs are used. We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Examples of such environments include indoor scenarios, urban or natural canyons…
University of Petroleum and Energy Studies（石油与能源研究大学）；International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
The world we live in is full of technology and with each passing day the advancement and usage of UAVs increases efficiently. As a result of the many application scenarios, there are some missions where the UAVs are vulnerable to external disruptions, such as a ground station's loss of connectivity, security missions, safety concerns, and delivery-related missions. Therefore, depending on the scenario, this could affect the operations and result in the safe landing of UAVs. Hence, this paper presents a heuristic approach towards safe landing of multi-rotor UAVs in the dynamic environments. The…
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Texas A&M University（德州农工大学）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；Infoscitex corporation（Infoscitex公司）；Air Force Research Laboratory（美国空军研究实验室）
Persistent monitoring missions require an up-to-date knowledge of the changing state of the underlying environment. UAVs can be gainfully employed to continually visit a set of targets representing tasks (and locations) in the environment and collect data therein for long time periods. The enduring nature of these missions requires the UAV to be regularly recharged at a service station. In this paper, we consider the case in which the service station is not co-located with any of the targets. An efficient monitoring requires the revisit time, defined as the maximum of the time elapsed between …
Jinan University（暨南大学）；University of Tunis El Manar（突尼斯马纳尔大学）；National Institute of Applied Sciences and Technology, University of Carthage（迦太基大学应用科学与技术国家研究所）；Prince Sultan University（苏丹王子大学）；CISTER/INESC TEC；ISEP-IPP（波尔图理工学院工程学院）；National School of Electronics and Telecommunication（国立电子与电信学院）
In the last few years, Unmanned Aerial Vehicles (UAVs) are making a revolution as an emerging technology with many different applications in the military, civilian, and commercial fields. The advent of autonomous drones has initiated serious challenges, including how to maintain their safe operation during their missions. The safe operation of UAVs remains an open and sensitive issue since any unexpected behavior of the drone or any hazard would lead to potential risks that might be very severe. The motivation behind this work is to propose a methodology for the safety assurance of drones over…
University of Maryland（马里兰大学）；DEVCOM Army Research Laboratory（美国陆军作战能力发展司令部陆军研究实验室）
We study a resource allocation problem for the cooperative aerial-ground vehicle routing application, in which multiple Unmanned Aerial Vehicles (UAVs) with limited battery capacity and multiple Unmanned Ground Vehicles (UGVs) that can also act as a mobile recharging stations need to jointly accomplish a mission such as persistently monitoring a set of points. Due to the limited battery capacity of the UAVs, they sometimes have to deviate from their task to rendezvous with the UGVs and get recharged. Each UGV can serve a limited number of UAVs at a time. In contrast to prior work on determinis…
