基于聚类分析和运动描述语言的扑翼飞行机器人行为规划

为了解决扑翼飞行机器人实时控制过程中操作者工作量大、操作较为复杂的难题,实现扑翼飞行机器人的分布式智能控制,提出了基于聚类分析和运动描述语言的扑翼飞行机器人行为规划方法.利用扑翼飞行机器人飞行数据聚类分析的结果,将机器人运动行为进行合理分类.在保证了运动描述语言的基元关系的同时,合理提取了扑翼飞行机器人的行为特征,并针对扑翼飞行机器人绕杆任务定义了4类运动基元.以扑翼飞行机器人和机载陀螺仪搭建了扑翼飞行机器人实验系统.通过直接控制方法和基于运动描述语言的机器人行为规划方法进行了实物实验和仿真实验,实验结果验证了所提方法的可行性和有效性.     

独立驱动的仿鸟扑翼飞行机器人的系统设计与实验EI北大核心CSCD

扑翼飞行机器人模仿自然界中的飞行生物,通过扑动翅膀拍打空气驱动飞行.它们机动性好、飞行效率高、噪音小,在某些应用场景比传统的固定翼飞机和旋翼飞机更有优势.目前扑翼飞行机器人的研究大多集中在机理研究和理论的建模与控制,鲜有实现室外的自主飞行,难以应对复杂的实际应用需求.在本文中,设计了一种独立驱动的仿鸟扑翼飞行机器人USTBird,通过两个舵机独立驱动左右翅膀可实现无可控尾翼的机动飞行.通过搭载自主设计的微型飞控板、GPS以及惯性导航模块,采用PD控制实现了扑翼飞行机器人的室外自主巡航飞行.设计了针对扑翼机器人的轻型两自由云台,很大程度上消除了机翼扑动飞行引起的图像抖动问题.针对机身振动和GPS测量误差带来的位置误差,采用无迹卡尔曼滤波算法对GPS采集的位置信息进行估计,提升了位置估计精度.设计了面向扑翼飞行机器人的地面站系统.考虑到扑翼飞行机器人存在的转向滞后问题,对偏航角设计双闭环分段PD控制器,最终实现了在外圆半径40 m和内圆半径10 m的圆环内的自主巡航任务.     

基于线驱转向的仿蝴蝶扑翼飞行机器人系统设计与控制

作为一种新型飞行机器人, 仿蝴蝶扑翼飞行机器人模仿自然界蝴蝶的生物结构和飞行方式, 能够有效地融 入并适应复杂环境, 在军民融合领域具有广阔的应用前景. 目前针对仿蝴蝶扑翼飞行机器人的研究大多停留在对生 物蝴蝶飞行机理的研究, 鲜有能够实现自由可控飞行的机器人系统. 本文设计了一款基于线驱转向的仿蝴蝶扑翼飞 行机器人, 名为USTButterfly-S, 其翼展50 cm, 重50 g, 可实现长达5分钟的自由可控飞行. 首先结合生物蝴蝶翅膀的 扑动特征, 设计了双曲柄双摇杆对称扑翼驱动机构. 然后模仿凤蝶的翅翼形状, 设计了仿蝴蝶翼型. 对翅膀的几何学 分析表明, USTButterfly-S的翅膀与凤蝶具有较好的形态相似性. 接着针对仿蝴蝶扑翼飞行机器人的转向控制问题, 首次采用线驱动机构拉动翅膀调节翅翼面积, 进而实现了USTButterfly-S的无尾航向控制. 最后集成自主设计的飞 控系统, USTButterfly-S能够实现室内盘旋飞行并进行实时航拍. 在实际飞行实验中, USTButterfly-S展现出类似生 物蝴蝶的飞行特征.     

微扑翼飞行机器人姿态反演自适应模糊控制

研究微扑翼飞行机器人姿态控制优化问题,因扑翼飞行的复杂性、系统的非线性、时变参数以及各种干扰而极具挑战性.为了提高系统姿态稳定性,提出了一种反演自适应模糊控制策略,针对传统反演控制律设计的不足之处,对微扑翼飞行机器人控制律设计中需要知道被控对象精确模型信息的部分,采用模糊控制法去逼近,从而实现了无需微扑翼飞行器精确模型的全新控制律,避免了因建模误差对控制带来的不良影响,并在此基础上证明了系统的稳定性.仿真结果证实了控制方法的有效性.     

基于Markov预测模型的动态足球机器人阵型策略方法

本文设计并实现了一种新的实体机器人足球比赛中队伍阵型策略方法,此方法建立在对球的Markov预测模型基础上,将目标和阵型相融合,实现了阵型的动态选择以及阵型间的动态切换。     

仿生扑翼飞行机器人研究中若干问题的思考

在成功研制仿生扑翼飞行机器人样机的基础上，提出仿生扑翼飞行机器人研究中值得思考的若干问题。有关低雷诺数问题,提出以动量定理为基础分析昆虫翅膀产生高飞行升力方法具有合理性的观点；有关非定常微分方程问题，提出非定常微分方程并非解决一切问题之关键的观点；有关翅变形问题，提出采用柔性翅的模型翅膀进行研究的观点。     

面向扑翼飞行控制的建模与奇异摄动分析

针对扑翼飞行中的周期性和时标不一现象,以及扑翼飞行实际控制中的问题,本文基于奇异摄动理论,提出了一种针对扑翼周期系统的稳定性分析方法.具体而言,首先建立了扑翼飞行器的多刚体模型,为后文对翅翼动力学的奇异摄动分析铺平道路;其次,对多刚体模型进行简化,抽象出扑翼飞行动力学的核心问题,并针对实际控制中的问题,提出了利用奇异摄...     

扑翼飞行器的建模与控制研究进展

扑翼飞行器（Flapping-wing air vehicle,FAV）即通过模拟昆虫以及鸟类飞行方式而制造的仿生机器人.与常见的固定翼和旋翼飞行器相比,具有效率高、质量轻、机动性强、耗能低等显著优点,是飞行器发展的重要方向.关于扑翼机的研究始于上世纪后期,现如今从理论探索到机体开发都有了可喜的成果.本文首先介绍了世界领先的几款扑翼飞行器的特点,接着简述了扑翼飞行器在动力学、能源、控制等方面的发展现状,并对未来的研究方向做出了展望.     

扑翼飞行器的多机协同飞行设计与实践

扑翼飞行器(flapping-wing aerial vehicle,FAV)是一种模仿动物飞行方式的新型飞行器,其具有仿生性且飞行声音小等特点,具有广泛的军事和民用前景.由于扑翼飞行器动力学模型复杂且容易受到风等环境因素的影响,目前尚无成熟稳定的控制算法可以用来控制扑翼飞行器.目前对扑翼飞行器的控制大多仍以手动控制为...     

仿生变形飞行器的飞行特性研究

随着无人飞行器的小型化甚至微型化发展,扑翼飞行的优势逐渐显现出来。受鸟类及昆虫飞行运动的启发,分析鸟类及昆虫的扑翼运动特性,设计了一种鸟类扑翼飞行方式,使用涡格法进行了扑翼的气动计算,并采用质点弹道学模型分析了仿生飞行的轨迹特性。仿真结果表明,设计的扑翼运动效果良好。     

Efficient Flight Control via Mechanical Impedance Manipulation: Energy Analyses for Hummingbird-Inspired MAVs

Within the growing family of unmanned aerial vehicles (UAV), flapping-wing micro aerial vehicles (MAV) are a relatively new field of research. Inspired by small size and agile flight of insects and birds, these systems offer a great potential for applications such as reconnaissance, surveillance, search and rescue, mapping, etc. Nevertheless, practicality of these vehicles depends on how we address various challenges ranging from control methodology to morphological construction and power supply design. A reasonable approach to resolving such problems is to acquire further inspiration from solutions in nature. Through modeling synchronous muscles in insects, we have shown that manipulation of mechanical impedance properties at wing joints can be a very efficient method for controlling lift and thrust production in flapping-wing MAVs. In the present work, we describe how this approach can be used to decouple lift/thrust regulation, thus reducing the complexity of flight controller. Although of simple design, this controller is still capable of demonstrating a high degree of precision and maneuverability throughout various simulated flight experiments with different types of trajectories. Furthermore, we use these flight simulations to investigate the power requirements of our control approach. The results indicate that these characteristics are considerably lower compared to when conventional control strategies—methods that often rely on manipulating stroke properties such as frequency or magnitude of the flapping motion—are employed. With less power demands, we believe our proposed control strategy is able to significantly improve flight time in future flapping-wing MAVs.     

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.     

Modeling Synchronous Muscle Function in Insect Flight: a Bio-Inspired Approach to Force Control in Flapping-Wing MAVs

Micro-aerial vehicles (MAV) and their promising applications—such as undetected surveillance or exploration of environments with little space for land-based maneuvers—are a well-known topic in the field of aerial robotics. Inspired by high maneuverability and agile flight of insects, over the past two decades a significant amount of effort has been dedicated to research on flapping-wing MAVs, most of which aim to address unique challenges in morphological construction, force production, and control strategy. Although remarkable solutions have been found for sufficient lift generation, effective methods for motion control still remain an open problem. The focus of this paper is to investigate general flight control mechanisms that are potentially used by real insects, thereby providing inspirations for flapping-wing MAV control. Through modeling the insect flight muscles, we show that stiffness and set point of the wing’s joint can be respectively tuned to regulate the wing’s lift and thrust forces. Therefore, employing a suitable controller with variable impedance actuators at each wing joint is a prospective approach to agile flight control of insect-inspired MAVs. The results of simulated flight experiments with one such controller are provided and support our claim.     

空气流动阻力下非线性车辆队列最优能耗控制方法

为了优化车辆队列在长距离行驶过程中的能源消耗,对空气流动阻力下车辆队列能耗优化间距策略以及相应的队列控制方法进行了研究;首先根据车辆队列在行驶过程中受到的空气流动阻力,建立基于异构风阻系数的车辆动力学模型;其次,设计基于滑模控制的非线性车辆队列控制方法,使其能够在不同风阻系数下稳定地收敛到期望的车辆队列;在此基础上,构建稳态下车辆队列能量消耗评价模型,并通过优化分析,计算能量消耗最优下的车辆队列期望车间距;最后通过数值仿真的手段验证所提控制方法的有效性与可行性;该结果表明:所设计的控制器能够使整个车辆队列达到期望的控制效果;得到的最优车间距能够使得特定条件下车辆队列稳态能量消耗降低。     

无信号交叉口网联车调度与分布式控制策略

针对多车道无信号交叉口,在高交通流量时易发生拥堵的问题,提出了一种适用于交叉口智能网联车（connected automated vehicle,CAV）通行的解决方案,将问题解耦成顺序决策和分布式控制两个问题。而车辆调度是方案中的关键点,对通行效率有很大的影响,且问题的复杂度是指数级。为解决该问题,提出一种基于队列评价模型的决策方法（queue evaluation spanning tree,QEST）,将网联车存到的多个队列模型中,并以通行效率和车辆延迟建立代价函数,以此对队列进行评价,通过不断循环选择最佳的队列,优化车辆在交叉口的通行顺序,有效提升了通行效率。对第二个问题,提出一种分布式控制框架,使得车辆按预定顺序通过交叉口。结果表明,该方案在中高交通流量时能有效提升交叉口的通行效率并降低车辆延迟和能量消耗。     

Formation control of VTOL UAV vehicles under switching-directed interaction topologies with disturbance rejection

This paper addresses the adaptive formation control of a group of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAV) with switching-directed interaction topologies. In addition, to tackle the adverse effect of disturbances, a couple of smooth bounded estimators are involved in the procedure design. Exploiting an extraction algorithm, we take advantage of the fully actuated rotational dynamics, to control the translational dynamics of each vehicle. We propose a distributed control scheme such that all vehicles track a desired reference velocity signal while keeping a desired prespecified formation. In this framework, the underlying topology of the agents may switch among several directed graphs, each having a spanning tree. The stability of the overall closed-loop system is proved through Lyapunov function. Finally, simulation results are given to better highlight the effectiveness of the proposed control scheme.     

An impact study of integrating connected automated vehicles with conventional traffic

Connected and automated vehicles (CAVs) promise to change the transportation landscape with safer, faster and more energy-efficient mobility. However 100% CAV penetration may never be achieved, raising the question of how to ensure a productive co-existence of CAVs with conventional vehicles. In this paper, we address the problem of optimally controlling CAVs under mixed traffic conditions where both CAVs and human-driven vehicles (non-CAVs) travel on the roads. We specifically consider problems with such mixed traffic crossing an intersection without using explicit traffic signaling, the objective being to minimize energy consumption subject to a throughput maximization requirement while guaranteeing safety constraints. The impact of CAVs on overall energy consumption is investigated under different traffic scenarios as a function of the CAV penetration rate (i.e., the fraction of CAVs relative to all vehicles). Performance under traffic light control is used as a baseline for this study. Results are validated through simulation using MATLAB and VISSIM. The results indicate that the energy efficiency improvement becomes more significant as the CAV penetration rate increases, while the significance diminishes as traffic becomes heavier.     

无人机群场景下边端协同计算卸载技术

当今全球频繁出现自然灾害,针对一种无人机协同下的应急救灾计算卸载场景,提出一种带有协调器的边-端架构。综合考虑场景中的时延、能耗与无人机之间的负载均衡作为系统总代价,采用改进的深度强化学习算法APPO（advanced proximal policy optimization）以最小化系统总代价为目标进行卸载优化。任务的部分卸载相比二进制卸载可以更大程度上降低系统的总代价,APPO算法针对不同的任务情况可以找到合适的卸载比例与无人机进行卸载。仿真与实验结果表明,该算法与全本地处理相比,系统总代价降低了约50%,与较先进的A2C相比,系统总代价降低了约14%。展现了所提策略在该场景下的优越性。     

A generalized model for investigating scheduling schemes in computational clusters

In this paper, we present a generalized model for the performance evaluation of scheduling compute-intensive jobs with unknown service times in computational clusters. We propose the application of parameters defined in the SPECpower_ssj2008 benchmark of the Standard Performance Evaluation Corporation to construct a performance evaluation model. In addition, we also define a method to rank physical servers based on either the high performance priority or the energy efficiency priority, and measures to characterize the performance of computational clusters.We investigate three schemes (separate queue, class queue and common queue) for buffering jobs in a computational cluster that is built from Commercial Off-The-Shelf (COTS) servers. Numerical results show that the buffering schemes do not have impact on performance measures related to the energy consumption of the investigated cluster. However, the buffering schemes play an important role in ensuring the quality of service parameters such as the waiting time and the response time experienced by arriving jobs. Furthermore, Dynamic Voltage and Frequency Scaling should be carefully applied to reduce the energy consumption of computational clusters.