基于目标信息估计的分布式局部协调任务分配方法

分布式决策是提高群体自主性的关键技术之一.以侦查类无人机(unmanned search aerial vehicles,USAV)和打击类无人机(unmanned combat aerial vehicles,UCAV)执行协同搜索、攻击灰色目标区域问题为背景,建立了一种考虑局部链式通信、无人机飞行性能和任务执行能力等多约束的分布式任务分配模型,基于贝叶斯定理将任务空间的连续/离散不确定量用任务收益值量化描述.然后,提出了一种基于一致性协调算法的在线协同策略,并利用一致协调理论建立了一种冲突调解规则,在此基础上,设计了一种分布式任务分配求解算法,能够实现多USAV,UCAV的协同多任务快速分配.最后,通过数值仿真,验证了本文算法求解不确定空间任务分配问题的可行性和快速性.     

Vision‐based navigation through urban canyons

We address the problem of navigating unmanned vehicles safely through urban canyons in two dimensions using only vision‐based techniques. Two commonly used vision‐based obstacle avoidance techniques (namely stereo vision and optic flow) are implemented on an aerial and a ground‐based robotic platform and evaluated for urban canyon navigation. Optic flow is evaluated for its ability to produce a centering response between obstacles, and stereo vision is evaluated for detecting obstacles to the front. We also evaluate a combination of these two techniques, which allows a vehicle to detect obstacles to the front while remaining centered between obstacles to the side. Through experiments on an unmanned ground vehicle and in simulation, this combination is shown to be beneficial for navigating urban canyons, including T‐junctions and 90‐deg bends. Experiments on a rotorcraft unmanned aerial vehicle, which was constrained to two‐dimensional flight, demonstrate that stereo vision allowed it to detect an obstacle to the front, and optic flow allowed it to turn away from obstacles to the side. We discuss the theory behind these techniques, our experience in implementing them on the robotic platforms, and their suitability to the urban canyon navigation problem. © 2009 Wiley Periodicals, Inc.     

Post‐disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches

In this paper, we provide a review of the principal aspects related to search & rescue (SAR) with unmanned aerial vehicles (UAVs), with particular interest in the phase of post‐disaster assessment (PDA). Some areas of interest related to this topic have been chosen for the analysis: the aerial platforms used in the field, multirobot software architectures, onboard sensors and simultaneous localization and mapping approaches, terrain coverage algorithms, autonomous navigation techniques, and human‐swarm interfaces. All these aspects have been analyzed with respect to the state‐of‐the‐art, and also in relation to the project PRISMA, which focuses on the development and deployment of robots and autonomous systems that can operate in emergency scenarios, with a specific reference to monitoring and real‐time intervention.     

Robotic technologies for solar‐powered UAVs: Fully autonomous updraft‐aware aerial sensing for multiday search‐and‐rescue missions

Large‐scale aerial sensing missions can greatly benefit from the perpetual endurance capability provided by high‐performance low‐altitude solar‐powered unmanned aerial vehicles (UAVs). However, today these UAVs suffer from small payload capacity, low energetic margins, and high operational complexity. To tackle these problems, this paper presents four individual technical contributions and integrates them into an existing solar‐powered UAV system: First, a lightweight and power‐efficient day/night‐capable sensing system is discussed. Second, means to optimize the UAV platform to the specific payload and to thereby achieve sufficient energetic margins for day/night flight with payload are presented. Third, existing autonomous launch and landing functionality is extended for solar‐powered UAVs. Fourth, as a main contribution an extended Kalman filter (EKF)‐based autonomous thermal updraft tracking framework is developed. Its novelty is that it allows the end‐to‐end integration of the thermal‐induced roll moment into the estimation process. It is assessed against unscented Kalman filter and particle filter methods in simulation and implemented on the aircraft's low‐power autopilot. The complete system is verified during a 26 h search‐and‐rescue aerial sensing mock‐up mission that represents the first‐ever fully autonomous perpetual endurance flight of a small solar‐powered UAV with a day/night‐capable sensing payload. It also represents the first time that solar‐electric propulsion and autonomous thermal updraft tracking are combined in flight. In contrast to previous work that has focused on the energetic feasibility of perpetual flight, the individual technical contributions of this paper are considered core functionality to guarantee ease‐of‐use, effectivity, and reliability in future multiday aerial sensing operations with small solar‐powered UAVs.     

A Java™ universal vehicle router for routing unmanned aerial vehicles

We consider vehicle routing problems in the context of the Air Force operational problem of routing unmanned aerial vehicles from base locations to various reconnaissance sites. The unmanned aerial vehicle routing problem requires consideration of heterogeneous vehicles, vehicle endurance limits, time windows, and time walls for some of the sites requiring coverage, site priorities, and asymmetric travel distances. We propose a general architecture for operational research problems, specified for vehicle routing problems, that encourages object‐oriented programming and code reuse. We create an instance of this architecture for the unmanned aerial vehicle routing problem and describe the components of this architecture to include the general user interface created for the operational users of the system. We employ route building heuristics and tabu search in a symbiotic fashion to provide a user‐defined level‐of‐effort solver interface. Empirical tests of solution algorithms parameterized for solution speed reveal reasonable solution quality is attained.     

A real‐time field experiment on search and rescue operations assisted by unmanned aerial vehicles

This paper reports on the performance of a novel system for supporting search and rescue activities, known as SARUAV (search and rescue unmanned aerial vehicle), in a field experiment during which a real‐world search scenario was simulated. The experiment took place on March 2–3, 2017, at two sites located in southwestern Poland. Three groups acted in the experiment: (1) SARUAV and unmanned aerial vehicle (UAV) operators, (2) ground searchers, and (3) participants who simulated being lost. In the uncomplicated topography without snow cover, the system identified the lost persons, and ground searchers found them 31 min after the SARUAV report had been disseminated. In the mountainous area covered with snow, one person was found within 9 min after searchers received the SARUAV report; however, the other two persons were not identified by SARUAV. The field experiment served as a proof of concept of the SARUAV system, confirmed its potential in person identification studies, and helped to identify numerous scientific and technical problems that need to be solved to develop a mature version of the system.     

Data Comets: Designing a Visualization Tool for Analyzing Autonomous Aerial Vehicle Logs with Grounded Evaluation

Autonomous unmanned aerial vehicles are complex systems of hardware, software, and human input. Understanding this complexity is key to their development and operation. Information visualizations already exist for exploring flight logs but comprehensive analyses currently require several disparate and custom tools. This design study helps address the pain points faced by autonomous unmanned aerial vehicle developers and operators. We contribute: a spiral development process model for grounded evaluation visualization development focused on progressively broadening target user involvement and refining user goals; a demonstration of the model as part of developing a deployed and adopted visualization system; a data and task abstraction for developers and operators performing post-flight analysis of autonomous unmanned aerial vehicle logs; the design and implementation of Data Comets , an open-source and web-based interactive visualization tool for post-flight log analysis incorporating temporal, geospatial, and multivariate data; and the results of a summative evaluation of the visualization system and our abstractions based on in-the-wild usage. A free copy of this paper and source code are available at osf.io/h4p7g     

TrackerBots: Autonomous unmanned aerial vehicle for real‐time localization and tracking of multiple radio‐tagged animals

Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods. We present a novel autonomous aerial vehicle system—TrackerBots—to track and localize multiple radio‐tagged animals. The simplicity of measuring the received signal strength indicator (RSSI) values of very high frequency (VHF) radio‐collars commonly used in the field is exploited to realize a low‐cost and lightweight tracking platform suitable for integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the nonlinearity of the system based on RSSI measurements, our tracking and planning approaches integrate a particle filter for tracking and localizing and a partially observable Markov decision process for dynamic path planning. This approach allows autonomous navigation of a UAV in a direction of maximum information gain to locate multiple mobile animals and reduce exploration time and, consequently, conserve on‐board battery power. We also employ the concept of search termination criteria to maximize the number of located animals within power constraints of the aerial system. We validated our real‐time and online approach through both extensive simulations and field experiments with five VHF radio‐tags on a grassland plain.     

Dealing with midair collisions in dense collective aerial systems

The idea of creating collective aerial systems is appealing because several rather simple flying vehicles could join forces to cover a large area in little time in applications such as monitoring, mapping, search and rescue, or airborne communication relays. In most of these scenarios, a fleet of cooperating vehicles is dispatched to a confined airspace area and requested to fly close to a nominal altitude. Moreover, depending on the task each vehicle is assigned to, individual flight trajectories in this essentially two‐dimensional space may interfere, resulting in disastrous collisions. This paper begins by introducing a probabilistic model to predict the rate of midair collisions that would occur if nothing is done to prevent them. In a second step, a control strategy for midair collision avoidance is proposed, which is interesting because it requires only local communication and information about flight altitudes. The proposed strategy is systematically analyzed in theory and simulation as well as in experiments with five physical aerial vehicles. A significant reduction in collision rates can be achieved. Statistically, values close to zero are possible when the swarm's density is below an application‐dependent threshold. Such low collision rates warrant an acceptable level of confidence in collision‐free operation of a physical swarm. © 2011 Wiley Periodicals, Inc.     

Low‐altitude Terrestrial Spectroscopy from a Pushbroom Sensor

Hyperspectral cameras sample many different spectral bands at each pixel, enabling advanced detection and classification algorithms. However, their limited spatial resolution and the need to measure the camera motion to create hyperspectral images makes them unsuitable for nonsmooth moving platforms such as unmanned aerial vehicles (UAVs). We present a procedure to build hyperspectral images from line sensor data without camera motion information or extraneous sensors. Our approach relies on an accompanying conventional camera to exploit the homographies between images for mosaic construction. We provide experimental results from a low‐altitude UAV, achieving high‐resolution spectroscopy with our system.     

Decentralized planning and control for UAV–UGV cooperative teams

In this paper we study a symbiotic aerial vehicle-ground vehicle robotic team where unmanned aerial vehicles (UAVs) are used for aerial manipulation tasks, while unmanned ground vehicles (UGVs) aid and assist them. UGV can provide a UAV with a safe landing area and transport it across large distances, while UAV can provide an additional degree of freedom for the UGV, enabling it to negotiate obstacles. We propose an overall system control framework that includes high-accuracy motion planning for each individual robot and ad-hoc decentralized mission planning for complex missions. Experimental results obtained in a mockup arena for parcel transportation scenario show that the system is able to plan and execute missions in various environments and that the obtained plans result in lower energy consumption.     

微型无人机集群低时延组网规划方法

微型无人机已广泛应用于航拍、植保、电力巡线等民用领域.但目前的微型无人机之间缺少信息交互和任务协作.针对搜索与营救场景,研究微型无人机集群的运动模式规划问题,以实现微型无人机的任务协同,完成对整个搜救区域的搜索,并将图像通过多跳传回地面.图像数据通常采用短距高吞吐量无线WiFi通信技术传输,但是Wi Fi有限的通信距离和微型无人机移动会引起网络中断,导致数据丢失,造成较大的传输时延.针对此问题,细致地考虑通信约束,提出基于卷地毯式搜索的组网规划算法,保证无人机网络的连通性,并据此设计非均匀节点部署方法和对应的同步/异步运动策略,可极大地降低时延,获得较好的节点负载均衡.     

Using Genetic Algorithms for Tasking Teams of Raven UAVs

Control of multiple unmanned aerial vehicles is of importance given that so many have been deployed in the field. This work discusses how genetic algorithms (GA) have been applied to the cooperative tasking of the AeroVironment’s Raven unmanned aerial vehicle (UAV) engaged in an intelligence, reconnaissance, and surveillance (ISR) mission. Mission assumptions, development of the GA, the method used to test for convergence, and the outcome of preliminary testing are all discussed.     

Design and Hardware-in-the-Loop Integration of a UAV Microavionics System in a Manned–Unmanned Joint Airspace Flight Network Simulator

One of the challenges for manned-unmanned air vehicles flying in joint airspace is the need to develop customized but scalable algorithms and hardware that will allow safe and efficient operations. In this work, we present the design of a bus-backboned UAV microavionics system and the hardware-in-the-loop integration of this unit within a joint flight network simulator. The microavionics system is structured around the Controller Area Network and Ethernet bus data backbone. The system is designed to be cross-compatible across our experimental mini-helicopters, aircrafts and ground vehicles, and it is tailored to allow autonomous navigation and control for a variety of different research test cases. The expandable architecture allows not only scalability, but also flexibility to test manned-unmanned fleet cooperative algorithm designs at both hardware and software layer deployed on bus integrated flight management computers. The flight simulator is used for joint simulation of virtual manned and unmanned vehicles within a common airspace. This allows extensive hardware-in-the-loop testing capability of customized devices and algorithms in realistic test cases that require manned and unmanned vehicle coordinated flight trajectory planning.     

Airborne vision‐based collision‐detection system

Machine vision represents a particularly attractive solution for sensing and detecting potential collision‐course targets due to the relatively low cost, size, weight, and power requirements of vision sensors (as opposed to radar and Traffic Alert and Collision Avoidance System). This paper describes the development and evaluation of a real‐time, vision‐based collision‐detection system suitable for fixed‐wing aerial robotics. Using two fixed‐wing unmanned aerial vehicles (UAVs) to recreate various collision‐course scenarios, we were able to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. This type of image data is extremely scarce and was invaluable in evaluating the detection performance of two candidate target detection approaches. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400 to about 900 m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advance warning of between 8 and 10 s ahead of impact, which approaches the 12.5‐s response time recommended for human pilots. We overcame the challenge of achieving real‐time computational speeds by exploiting the parallel processing architectures of graphics processing units (GPUs) found on commercial‐off‐the‐shelf graphics devices. Our chosen GPU device suitable for integration onto UAV platforms can be expected to handle real‐time processing of 1,024 × 768 pixel image frames at a rate of approximately 30 Hz. Flight trials using manned Cessna aircraft in which all processing is performed onboard will be conducted in the near future, followed by further experiments with fully autonomous UAV platforms. © 2010 Wiley Periodicals, Inc.     

Robust Gyro‐free Attitude Estimation for a Small Fixed‐wing Unmanned Aerial Vehicle

This paper proposes a backup attitude estimation scheme for small fixed‐wing unmanned aerial vehicles (UAVs) in the event of gyroscopic failure. The attitude is propagated in terms of 3 degrees‐of‐freedom (DoF) aircraft dynamics. The errors in attitude propagation are updated using indirect attitude information obtained from accelerations as sensed by onboard accelerometers and a global positioning system (GPS) receiver. In the event of gyroscopic failure, large uncertainties are introduced into the attitude propagation model. Such uncertainties in states and parameters are modeled as norm‐bound uncertainties and a discrete‐time robust extended Kalman filter (REKF) is implemented to estimate the attitude of the UAV.     

The nested k‐means method: A new approach for detecting lost persons in aerial images acquired by unmanned aerial vehicles

A new method, named as the nested k‐means, for detecting a person captured in aerial images acquired by an unmanned aerial vehicle (UAV), is presented. The nested k‐means method is used in a newly built system that supports search and rescue (SAR) activities through processing of aerial photographs taken in visible light spectra (red‐green‐blue channels, RGB). First, the k‐means classification is utilized to identify clusters of colors in a three‐dimensional space (RGB). Second, the k‐means method is used to verify if the automatically selected class of colors is concurrently spatially clustered in a two‐dimensional space (easting‐northing, EN), and has human‐size area. The UAV images were acquired during the field campaign carried out in the Izerskie Mountains (SW Poland). The experiment aimed to observe several persons using an RGB camera, in spring and winter, during various periods of day, in uncovered terrain and sparse forest. It was found that the nested k‐means method has a considerable potential for detecting a person lost in the wilderness and allows to reduce area to be searched to 4.4 and 7.3% in spring and winter, respectively. In winter, land cover influences the performance of the nested k‐means method, with better skills in sparse forest than in the uncovered terrain. In spring, such a relationship does not hold. The nested k‐means method may provide the SAR teams with a tool for near real‐time detection of a person and, as a consequence, to reduce search area to approximately 0.5–7.3% of total terrain to be visited, depending on season and land cover.     

Aerial online mapping on-board system by real-time object detection for UGV path generation in unstructured outdoor environments

An optimal path provides efficient operation of unmanned ground vehicles (UGVs) for many kinds of tasks such as transportation, exploration, surveillance, and search and rescue in unstructured areas that include various unexpected obstacles. Various onboard sensors such as LiDAR, radar, sonar, and cameras are used to detect obstacles around the UGVs. However, their range of view is often limited by movable obstacles or barriers, resulting in inefficient path generation. Here, we present the aerial online mapping system to generate an efficient path for a UGV on a two-dimensional map. The map is updated by projecting obstacles detected in the aerial images taken by an unmanned aerial vehicle through an object detector based on a conventional convolutional neural network. The proposed system is implemented in real-time by a skid steering ground vehicle and a quadcopter with relatively small, low-cost embedded systems. The frameworks and each module of the systems are given in detail to evaluate the performance. The system is also demonstrated in unstructured outdoor environments such as in a football field and a park with unreliable communication links. The results show that the aerial online mapping is effective in path generation for autonomous UGVs in real environments.     

A column‐generation‐based approach to fleet design problems mixing owned and hired vehicles

We look at the problem of choosing a fleet of vehicles to carry out delivery tasks across a long time horizon. The delivery quantities may vary significantly from day to day, and from season to season, and the underlying routing problem may have rich constraints, for example, time windows, multiple compartments, multiple commodities, and compatibility constraints. We consider the option of hiring extra vehicles from external carriers in order to efficiently carry out the day‐to‐day operations while containing the costs of owning the fleet. The goal is to design a fleet so as to minimize the sum of costs of routing the fleet every day of the horizon, the acquisition costs, the maintenance costs, and the costs of hiring external vehicles. In the literature, there is no previous work on fleet design for a long time horizon, which also considers the hiring options. We propose to tackle the problem using column generation and develop three different heuristics. The methods proposed are tested and compared on a set of real‐world problems. It is also shown how introducing the possibility of hiring helps reducing the overall cost and the number of idle vehicles over the planning horizon.     

Characterization of Sky‐region Morphological‐temporal Airborne Collision Detection

Automated airborne collision‐detection systems are a key enabling technology for facilitating the integration of unmanned aerial vehicles (UAVs) into the national airspace. These safety‐critical systems must be sensitive enough to provide timely warnings of genuine airborne collision threats, but not so sensitive as to cause excessive false alarms. Hence, an accurate characterization of detection and false‐alarm sensitivity is essential for understanding performance tradeoffs, and system designers can exploit this characterization to help achieve a desired balance in system performance. In this paper, we experimentally evaluate a sky‐region, image‐based, aircraft collision‐detection system that is based on morphological and temporal processing techniques. (Note that the examined detection approaches are not suitable for the detection of potential collision threats against a ground clutter background.) A novel collection methodology for collecting realistic airborne collision‐course target footage in both head‐on and tail‐chase engagement geometries is described. Under (hazy) blue sky conditions, our proposed system achieved detection ranges greater than 1540 m in three flight test cases with no false‐alarm events in 14.14 h of nontarget data (under cloudy conditions, the system achieved detection ranges greater than 1170 m in four flight test cases with no false‐alarm events in 6.63 h of nontarget data). Importantly, this paper is the first documented presentation of detection range versus false‐alarm curves generated from airborne target and nontarget image data. © 2012 Wiley Periodicals, Inc.