微空中车辆(MAVS)在户外操作的限制靠近障碍物,通过他们承受风阵风的能力。目前广泛的位置控制方法,例如比例整体衍生物控制在阵风的影响下不会均匀。增量非线性动态反转(INDI)是一种基于传感器的控制技术,可以控制受扰动的非线性系统。它是为载人飞机或MAVS的态度控制而开发的。在本文中,我们将这种方法概括为严重燃烧负载下MAV的外环控制。在一个实验中对传统的比例积分衍生物(PID)控制器的显着改进进行了说明,其中四轮电机在10米/秒的吹风机排气进出中。控制方法不依赖于频繁的位置更新,如使用标准GPS模块的外部实验中所示。最后,我们研究了使用线性化来计算推力向量增量的效果,与非线性计算相比。该方法需要很少的建模并且是计算效率。
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本文提出了一项新颖的控制法,以使用尾随机翼无人驾驶飞机(UAV)进行准确跟踪敏捷轨迹,该轨道在垂直起飞和降落(VTOL)和向前飞行之间过渡。全球控制配方可以在整个飞行信封中进行操作,包括与Sideslip的不协调的飞行。显示了具有简化空气动力学模型的非线性尾尾动力学的差异平坦度。使用扁平度变换,提出的控制器结合了位置参考的跟踪及其导数速度,加速度和混蛋以及偏航参考和偏航速率。通过角速度进纸术语包含混蛋和偏航率参考,可以改善随着快速变化的加速度跟踪轨迹。控制器不取决于广泛的空气动力学建模,而是使用增量非线性动态反演(INDI)仅基于局部输入输出关系来计算控制更新,从而导致对简化空气动力学方程中差异的稳健性。非线性输入输出关系的精确反转是通过派生的平坦变换实现的。在飞行测试中对所得的控制算法进行了广泛的评估,在该测试中,它展示了准确的轨迹跟踪和挑战性敏捷操作,例如侧向飞行和转弯时的侵略性过渡。
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We address the theoretical and practical problems related to the trajectory generation and tracking control of tail-sitter UAVs. Theoretically, we focus on the differential flatness property with full exploitation of actual UAV aerodynamic models, which lays a foundation for generating dynamically feasible trajectory and achieving high-performance tracking control. We have found that a tail-sitter is differentially flat with accurate aerodynamic models within the entire flight envelope, by specifying coordinate flight condition and choosing the vehicle position as the flat output. This fundamental property allows us to fully exploit the high-fidelity aerodynamic models in the trajectory planning and tracking control to achieve accurate tail-sitter flights. Particularly, an optimization-based trajectory planner for tail-sitters is proposed to design high-quality, smooth trajectories with consideration of kinodynamic constraints, singularity-free constraints and actuator saturation. The planned trajectory of flat output is transformed to state trajectory in real-time with consideration of wind in environments. To track the state trajectory, a global, singularity-free, and minimally-parameterized on-manifold MPC is developed, which fully leverages the accurate aerodynamic model to achieve high-accuracy trajectory tracking within the whole flight envelope. The effectiveness of the proposed framework is demonstrated through extensive real-world experiments in both indoor and outdoor field tests, including agile SE(3) flight through consecutive narrow windows requiring specific attitude and with speed up to 10m/s, typical tail-sitter maneuvers (transition, level flight and loiter) with speed up to 20m/s, and extremely aggressive aerobatic maneuvers (Wingover, Loop, Vertical Eight and Cuban Eight) with acceleration up to 2.5g.
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二次运动的准确轨迹跟踪控制对于在混乱环境中的安全导航至关重要。但是,由于非线性动态,复杂的空气动力学效应和驱动约束,这在敏捷飞行中具有挑战性。在本文中,我们通过经验比较两个最先进的控制框架:非线性模型预测控制器(NMPC)和基于差异的控制器(DFBC),通过以速度跟踪各种敏捷轨迹,最多20 m/s(即72 km/h)。比较在模拟和现实世界环境中进行,以系统地评估这两种方法从跟踪准确性,鲁棒性和计算效率的方面。我们以更高的计算时间和数值收敛问题的风险来表明NMPC在跟踪动态不可行的轨迹方面的优势。对于这两种方法,我们还定量研究了使用增量非线性动态反演(INDI)方法添加内环控制器的效果,以及添加空气动力学阻力模型的效果。我们在世界上最大的运动捕获系统之一中进行的真实实验表明,NMPC和DFBC的跟踪误差降低了78%以上,这表明有必要使用内环控制器和用于敏捷轨迹轨迹跟踪的空气动力学阻力模型。
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我们提出了通过现实的模拟和现实世界实验来支持可复制研究的多运动无人机控制(UAV)和估计系统。我们提出了一个独特的多帧本地化范式,用于同时使用多个传感器同时估算各种参考框架中的无人机状态。该系统可以在GNSS和GNSS贬低的环境中进行复杂的任务,包括室外室内过渡和执行冗余估计器,以备份不可靠的本地化源。提出了两种反馈控制设计:一个用于精确和激进的操作,另一个用于稳定和平稳的飞行,并进行嘈杂的状态估计。拟议的控制和估计管道是在3D中使用Euler/Tait-Bryan角度表示的,而无需使用Euler/Tait-Bryan角度表示。取而代之的是,我们依靠旋转矩阵和一个新颖的基于标题的惯例来代表标准多电流直升机3D中的一个自由旋转自由度。我们提供了积极维护且有据可查的开源实现,包括对无人机,传感器和本地化系统的现实模拟。拟议的系统是多年应用系统,空中群,空中操纵,运动计划和遥感的多年研究产物。我们所有的结果都得到了现实世界中的部署的支持,该系统部署将系统塑造成此处介绍的表单。此外,该系统是在我们团队从布拉格的CTU参与期间使用的,该系统在享有声望的MBZIRC 2017和2020 Robotics竞赛中,还参加了DARPA SubT挑战赛。每次,我们的团队都能在世界各地最好的竞争对手中获得最高位置。在每种情况下,挑战都促使团队改善系统,并在紧迫的期限内获得大量高质量的体验。
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Hybrid unmanned aerial vehicles (UAVs) integrate the efficient forward flight of fixed-wing and vertical takeoff and landing (VTOL) capabilities of multicopter UAVs. This paper presents the modeling, control and simulation of a new type of hybrid micro-small UAVs, coined as lifting-wing quadcopters. The airframe orientation of the lifting wing needs to tilt a specific angle often within $ 45$ degrees, neither nearly $ 90$ nor approximately $ 0$ degrees. Compared with some convertiplane and tail-sitter UAVs, the lifting-wing quadcopter has a highly reliable structure, robust wind resistance, low cruise speed and reliable transition flight, making it potential to work fully-autonomous outdoor or some confined airspace indoor. In the modeling part, forces and moments generated by both lifting wing and rotors are considered. Based on the established model, a unified controller for the full flight phase is designed. The controller has the capability of uniformly treating the hovering and forward flight, and enables a continuous transition between two modes, depending on the velocity command. What is more, by taking rotor thrust and aerodynamic force under consideration simultaneously, a control allocation based on optimization is utilized to realize cooperative control for energy saving. Finally, comprehensive Hardware-In-the-Loop (HIL) simulations are performed to verify the advantages of the designed aircraft and the proposed controller.
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In this paper, we propose an effective unified control law for accurately tracking agile trajectories for lifting-wing quadcopters with different installation angles, which have the capability of vertical takeoff and landing (VTOL) as well as high-speed cruise flight. First, we derive a differential flatness transform for the lifting-wing dynamics with a nonlinear model under coordinated turn condition. To increase the tracking performance on agile trajectories, the proposed controller incorporates the state and input variables calculated from differential flatness as feedforward. In particular, the jerk, the 3-order derivative of the trajectory, is converted into angular velocity as a feedforward item, which significantly improves the system bandwidth. At the same time, feedback and feedforward outputs are combined to deal with external disturbances and model mismatch. The control algorithm has been thoroughly evaluated in the outdoor flight tests, which show that it can achieve accurate trajectory tracking.
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系列弹性执行器(SEA)具有固有的合规性,可为机器人提供安全的扭矩来源,这些源是与各种环境相互作用的机器人,包括人类。这些应用对海体扭矩控制器有很高的要求,扭矩响应以及与其环境的相互作用行为。为了区分现有技术的扭矩控制器,这项工作正在引入统一的理论和实验框架,其基于它们的扭矩传递行为,表观阻抗行为,特别是表观阻抗的钝化性,即它们的相互作用稳定性,也是如此作为对传感器噪声的敏感性。我们比较经典的海上控制方法,如级联PID控制器和全状态反馈控制器,使用干扰观察者,加速反馈和适应规则,具有先进的控制器。仿真和实验证明了稳定的相互作用,高带宽和低噪声水平之间的折衷。基于这些权衡,可以基于与各个环境的所需交互来设计和调整特定于应用程序特定控制器。
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该论文提出了两种控制方法,用于用微型四轮驱动器进行反弹式操纵。首先,对专门为反转设计设计的现有前馈控制策略进行了修订和改进。使用替代高斯工艺模型的贝叶斯优化通过在模拟环境中反复执行翻转操作来找到最佳运动原语序列。第二种方法基于闭环控制,它由两个主要步骤组成:首先,即使在模型不确定性的情况下,自适应控制器也旨在提供可靠的参考跟踪。控制器是通过通过测量数据调整的高斯过程来增强无人机的标称模型来构建的。其次,提出了一种有效的轨迹计划算法,该算法仅使用二次编程来设计可行的轨迹为反弹操作设计。在模拟和使用BitCraze Crazyflie 2.1四肢旋转器中对两种方法进行了分析。
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在本文中,我们分析了具有基于视觉导航的无人机(UAV)的时间延迟动力学对控制器设计的影响。时间延迟是网络物理系统中不可避免的现象,并且对无人机的控制器设计和轨迹产生具有重要意义。时间延迟对无人机动态的影响随着基于视力较慢的导航堆栈的使用而增加。我们表明,文献中的现有模型不包括时间延迟,不适合控制器调整,因为一个微不足道的解决方案始终存在错误的解决方案。我们确定的微不足道的解决方案表明,使用无限控制器的利益来实现最佳性能,这与实际发现相矛盾。我们通过引入无人机的新型非线性时间延迟模型来避免这种缺点,然后获得与每个UAV控制回路相对应的一组线性解耦模型。分析了角度和高度动力学的线性时间延迟模型的成本函数,与无延迟模型相反,我们显示了有限的最佳控制器参数的存在。由于使用了时间延迟模型,我们在实验上表明,所提出的模型准确地表示系统稳定性限制。由于时间延迟的考虑,我们使用基于视觉探视的无人机(VO)导航,在跟踪峰值速度为2.09 m/s的lemsistate轨迹时,我们实现了RMSE 5.01 cm的跟踪结果,这与最新-艺术。
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安装在微空中车辆(MAV)上的地面穿透雷达是有助于协助人道主义陆地间隙的工具。然而,合成孔径雷达图像的质量取决于雷达天线的准确和精确运动估计以及与MAV产生信息性的观点。本文介绍了一个完整的自动空气缩进的合成孔径雷达(GPSAR)系统。该系统由空间校准和时间上同步的工业级传感器套件组成,使得在地面上方,雷达成像和光学成像。自定义任务规划框架允许在地上控制地上的Stripmap和圆形(GPSAR)轨迹的生成和自动执行,以及空中成像调查飞行。基于因子图基于Dual接收机实时运动(RTK)全局导航卫星系统(GNSS)和惯性测量单元(IMU)的测量值,以获得精确,高速平台位置和方向。地面真理实验表明,传感器时机为0.8美元,正如0.1美元的那样,定位率为1 kHz。与具有不确定标题初始化的单个位置因子相比,双位置因子配方可提高高达40%,批量定位精度高达59%。我们的现场试验验证了本地化准确性和精度,使得能够相干雷达测量和检测在沙子中埋入的雷达目标。这验证了作为鸟瞰着地图检测系统的潜力。
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A reduced order model of a generic submarine is presented. Computational fluid dynamics (CFD) results are used to create and validate a model that includes depth dependence and the effect of waves on the craft. The model and the procedure to obtain its coefficients are discussed, and examples of the data used to obtain the model coefficients are presented. An example of operation following a complex path is presented and results from the reduced order model are compared to those from an equivalent CFD calculation. The controller implemented to complete these maneuvers is also presented.
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空中操纵的生长场通常依赖于完全致动的或全向微型航空车(OMAV),它们可以在与环境接触时施加任意力和扭矩。控制方法通常基于无模型方法,将高级扳手控制器与执行器分配分开。如有必要,在线骚扰观察员拒绝干扰。但是,虽然是一般,但这种方法通常会产生次优控制命令,并且不能纳入平台设计给出的约束。我们提出了两种基于模型的方法来控制OMAV,以实现轨迹跟踪的任务,同时拒绝干扰。第一个通过从实验数据中学到的模型来优化扳手命令并补偿模型错误。第二个功能优化了低级执行器命令,允许利用分配无空格并考虑执行器硬件给出的约束。在现实世界实验中显示和评估两种方法的疗效和实时可行性。
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Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals. Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g. MavLink). The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols. In addition, the modular design enables various components to be easily usable independently in other projects. We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights.
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This book provides a solution to the control and motion planning design for an octocopter system. It includes a particular choice of control and motion planning algorithms which is based on the authors' previous research work, so it can be used as a reference design guidance for students, researchers as well as autonomous vehicles hobbyists. The control is constructed based on a fault tolerant approach aiming to increase the chances of the system to detect and isolate a potential failure in order to produce feasible control signals to the remaining active motors. The used motion planning algorithm is risk-aware by means that it takes into account the constraints related to the fault-dependant and mission-related maneuverability analysis of the octocopter system during the planning stage. Such a planner generates only those reference trajectories along which the octocopter system would be safe and capable of good tracking in case of a single motor fault and of majority of double motor fault scenarios. The control and motion planning algorithms presented in the book aim to increase the overall reliability of the system for completing the mission.
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开发了一个领导者追随者系统,用于合作运输。据我们所知,这是一个不需要互联通信的第一工作,并且可以实时修改有效载荷的参考轨迹,以便它可以应用于动态变化的环境。为了在无通信条件下实时跟踪修改的参考轨迹,引导跟随系统被认为是非文展系统,其中开发了控制器以实现有效载荷的渐近跟踪。为了消除安装力传感器的需要,开发了UKFS(Unscented Kalman滤波器)以估计领导者和追随者所施加的力量。进行稳定性分析以证明闭环系统的跟踪误差。仿真结果表明跟踪控制器的良好性能。实验表明,领导者的控制器和追随者可以在现实世界中工作,但是跟踪误差受到限制空间中气流的干扰的影响。
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Autonomous Micro Aerial Vehicles are deployed for a variety tasks including surveillance and monitoring. Perching and staring allow the vehicle to monitor targets without flying, saving battery power and increasing the overall mission time without the need to frequently replace batteries. This paper addresses the Active Visual Perching (AVP) control problem to autonomously perch on inclined surfaces up to $90^\circ$. Our approach generates dynamically feasible trajectories to navigate and perch on a desired target location, while taking into account actuator and Field of View (FoV) constraints. By replanning in mid-flight, we take advantage of more accurate target localization increasing the perching maneuver's robustness to target localization or control errors. We leverage the Karush-Kuhn-Tucker (KKT) conditions to identify the compatibility between planning objectives and the visual sensing constraint during the planned maneuver. Furthermore, we experimentally identify the corresponding boundary conditions that maximizes the spatio-temporal target visibility during the perching maneuver. The proposed approach works on-board in real-time with significant computational constraints relying exclusively on cameras and an Inertial Measurement Unit (IMU). Experimental results validate the proposed approach and shows the higher success rate as well as increased target interception precision and accuracy with respect to a one-shot planning approach, while still retaining aggressive capabilities with flight envelopes that include large excursions from the hover position on inclined surfaces up to 90$^\circ$, angular speeds up to 750~deg/s, and accelerations up to 10~m/s$^2$.
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有限的飞行距离和时间是多次交流者的常见问题。我们提出了一种方法,可以找到多杆的最佳速度和侧滑角,飞行给定路径以达到最长的飞行距离或时间。由于飞行速度和侧滑通常是多功能路径计划中的自由变量,因此可以在不更改任务的情况下更改它们。所提出的方法基于具有自适应步长的新型多变量极值寻求控制器,该控制器的灵感来自机器学习社区在随机优化方面的最新工作。我们的方法(a)不需要车辆的功耗模型,(b)在计算上是有效的,并且实时运行在低成本嵌入式计算机上,并且(c)比恒定步骤的标准极端寻求控制器收敛的速度更快尺寸。我们证明了这种方法的稳定性,并通过室外实验对其进行了验证。该方法显示出与不同的有效载荷和风的存在。与以均匀选择的随机侧滑角度以最大可​​实现的速度飞行相比,以最佳范围速度和侧滑飞行的飞行量将飞行范围提高14.3%,而无需有效载荷,而有盒子有效载荷则增加了19.4%。此外,与悬停的相比,以最佳耐力速度飞行,而侧滑的飞行时间则增加了7.5%,而无需有效载荷,而在盒子有效载荷的情况下,飞行时间增加了14.4%。可以在https://youtu.be/alds8lvfogk上找到视频。
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由于这些要求的竞争性质,尤其是在一系列的运行速度和条件下,在转向控制中的准确性和误差融合与优美运动的平衡路径与优美的运动具有挑战性。本文表明,考虑滑移对运动学控制,动态控制和转向执行器速率命令的影响的集成多层转向控制器可实现准确且优美的路径。这项工作建立在多层侧滑和基于YAW的模型上,该模型允许派生控制器考虑由于侧滑而引起的误差以及转向命令和优美的侧向运动之间的映射。基于观察者的侧滑估计与运动控制器中的标题误差相结合,以提供前馈滑移补偿。使用基于速度的路径歧管,通过连续变量结构控制器(VSC)来补偿路径以下误差,以平衡优雅的运动和误差收敛。后台动态控制器使用结果偏航率命令来生成转向率命令。高增益观察者(HGO)估计输出反馈控制的侧滑和偏航率。提供了输出反馈控制器的稳定性分析,并解决了峰值。该工作仅针对侧向控制,因此转向控制器可以与其他速度控制器结合使用。现场结果提供了与相关方法的比较,这些方法在不同的复杂情况下证明了具有不同天气条件和扰动的不同复杂情况。
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我们提出了一种基于直接质心控制的人形机器人的运动和平衡的综合方法。我们的方法使用人形生物的五质量描述。它从机器人的所需脚部轨迹和质心参数产生全身运动。一组简化的模型用于制定一般和直观的控制定律,然后实时应用它们,以估算和调节质量位置的中心和多体惯性主轴的方向。所提出的算法的组合产生了一条伸展的步态,并具有自然的上身运动。由于仅需要6轴IMU和关节编码器才能实现,因此机器人之间的可移植性很高。我们的方法已通过类人类开放式平台对实验进行了实验验证,证明了全身运动和推动排斥能力。
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