This paper presents the ARCAD simulator for the rapid development of Unmanned Aerial Systems (UAS), including underactuated and fully-actuated multirotors, fixed-wing aircraft, and Vertical Take-Off and Landing (VTOL) hybrid vehicles. The simulator is designed to accelerate these aircraft's modeling and control design. It provides various analyses of the design and operation, such as wrench-set computation, controller response, and flight optimization. In addition to simulating free flight, it can simulate the physical interaction of the aircraft with its environment. The simulator is written in MATLAB to allow rapid prototyping and is capable of generating graphical visualization of the aircraft and the environment in addition to generating the desired plots. It has been used to develop several real-world multirotor and VTOL applications. The source code is available at https://github.com/keipour/aircraft-simulator-matlab.
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Enabling vertical take-off and landing while providing the ability to fly long ranges opens the door to a wide range of new real-world aircraft applications while improving many existing tasks. Tiltrotor vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are a better choice than fixed-wing and multirotor aircraft for such applications. Prior works on these aircraft have addressed aerodynamic performance, design, modeling, and control. However, a less explored area is the study of their potential fault tolerance due to their inherent redundancy, which allows them to tolerate some degree of actuation failure. This paper introduces tolerance to several types of actuator failures in a tiltrotor VTOL aircraft. We discuss the design and modeling of a custom tiltrotor VTOL UAV, which is a combination of a fixed-wing aircraft and a quadrotor with tilting rotors, where the four propellers can be rotated individually. Then, we analyze the feasible wrench space the vehicle can generate and design the dynamic control allocation so that the system can adapt to actuator failures, benefiting from the configuration redundancy. The proposed approach is lightweight and is implemented as an extension to an already-existing flight control stack. Extensive experiments validate that the system can maintain the controlled flight under different actuator failures. To the best of our knowledge, this work is the first study of the tiltrotor VTOL's fault-tolerance that exploits the configuration redundancy. The source code and simulation can be accessed at https://theairlab.org/vtol.
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提出了一种能够改变形状中空飞行的新型Quadcopter,允许在四种配置中进行操作,其中包含持续的悬停在三个配置中。这是实现的,而不需要超出Quadcopter典型的四个电动机的执行器。通过自由旋转铰链来实现变形,使车臂通过减少或逆转推力向下折叠。放置在车辆的控制输入上的约束防止臂意外折叠或展开。这允许使用现有的四转器控制器和轨迹生成算法,只有最小的增加的复杂性。对于我们在悬停的实验载体中,我们发现这些约束导致车辆可以产生的最大偏航扭矩的36%减少,但不会导致最大推力或卷和螺距扭矩的减少。实验结果表明,对于典型的操纵,增加的限制对轨迹跟踪性能的影响忽略不计。最后,示出了改变配置的能力,使车辆能够在悬挂导线上移动小通道,并且执行有限的抓取任务。
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We designed and constructed an A-sized base autonomous underwater vehicle (AUV), augmented with a stack of modular and extendable hardware and software, including autonomy, navigation, control and high fidelity simulation capabilities (A-size stands for the standard sonobuoy form factor, with a maximum diameter of 124 mm). Subsequently, we extended this base vehicle with a novel tuna-inspired morphing fin payload module (referred to as the Morpheus AUV), to achieve good directional stability and exceptional maneuverability; properties that are highly desirable for rigid hull AUVs, but are presently difficult to achieve because they impose contradictory requirements. The morphing fin payload allows the base AUV to dynamically change its stability-maneuverability qualities by using morphing fins, which can be deployed, deflected and retracted, as needed. The base vehicle and Morpheus AUV were both extensively field tested in-water in the Charles river, Massachusetts, USA; by conducting hundreds of hours of operations over a period of two years. The maneuvering capability of the Morpheus AUV was evaluated with and without the use of morphing fins to quantify the performance improvement. The Morpheus AUV was able to showcase an exceptional turning rate of around 25-35 deg/s. A maximum turn rate improvement of around 35% - 50% was gained through the use of morphing fins.
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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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为了在真实硬件平台上安全可靠的任何机器人控制器的安全部署,通常是在现实的仿真环境中使用特定机器人全面评估控制器的性能的必要练习。尽管有几种可以为此目的提供核心物理引擎的软件解决方案,但通常是繁琐且容易出错的努力,将模拟环境与机器人控制器进行评估。控制器可能具有一个复杂的结构,该结构由有限状态机(FSM)内的多个状态和过渡组成,甚至可能需要通过GUI输入。在这项工作中,我们提出了MC-Mujoco,这是一个开源软件框架,该框架在Mujoco Physics Simulator和MC-RTC机器人控制框架之间形成接口。我们提供实施详细信息,并描述为基本上任何新机器人提供支持的过程。我们还展示并发布了一个样品FSM控制器,用于通过Mujoco中的HRP-5P人形机器人对刚性对象进行两足球运动和稳定的抓握。 MC-Mujoco,已开发的机器人模块和FSM控制器的代码和使用说明可在线获得。
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This paper introduces a structure-deformable land-air robot which possesses both excellent ground driving and flying ability, with smooth switching mechanism between two modes. The elaborate coupled dynamics model of the proposed robot is established, including rotors, chassis, especially the deformable structures. Furthermore, taking fusion locomotion and complex near-ground situations into consideration, a model based controller is designed for landing and mode switching under various harsh conditions, in which we realise the cooperation between fused two motion modes. The entire system is implemented in ADAMS/Simulink simulation and in practical. We conduct experiments under various complex scenarios. The results show our robot can accomplish land-air switching swiftly and smoothly, and the designed controller can effectively improve the landing flexibility and reliability.
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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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即使是最强大的自主行为也可能失败。这项研究的目的是在自主任务执行期间恢复和从失败中收集数据,以便将来可以防止它们。我们建议对实时故障恢复和数据收集进行触觉干预。Elly是一个系统,可以在自主机器人行为和人类干预之间进行无缝过渡,同时从人类恢复策略中收集感觉信息。系统和我们的设计选择在单臂任务上进行了实验验证 - 在插座中安装灯泡 - 以及双层任务 - 拧上瓶盖的帽子 - 使用两个配备的4手指握把。在这些示例中,Elly在总共40次运行中实现了超过80%的任务完成。
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子格式微型航空车(MAV)中的准确而敏捷的轨迹跟踪是具有挑战性的,因为机器人的小规模会引起大型模型不确定性,要求强大的反馈控制器,而快速的动力学和计算约束则阻止了计算上昂贵的策略的部署。在这项工作中,我们提出了一种在MIT SoftFly(一个子)MAV(0.7克)上进行敏捷和计算有效轨迹跟踪的方法。我们的策略采用了级联的控制方案,在该方案中,自适应态度控制器与受过训练的神经网络政策相结合,以模仿轨迹跟踪可靠的管模型模型预测控制器(RTMPC)。神经网络政策是使用我们最近的工作获得的,这使该政策能够保留RTMPC的稳健性,但以其计算成本的一小部分。我们通过实验评估我们的方法,即使在更具挑战性的操作中,达到均方根误差也低于1.8 cm,与我们先前的工作相比,最大位置误差减少了60%,并证明了对大型外部干扰的稳健性
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用于移动操作的机器人平台需要满足许多对许多现实世界应用的两个矛盾要求:需要紧凑的基础才能通过混乱的室内环境导航,而支撑需要足够大以防止翻滚或小费,尤其是在快速操纵期间有效载荷或与环境有力互动的操作。本文提出了一种新颖的机器人设计,该设计通过多功能足迹来满足这两种要求。当操纵重物时,它可以将其足迹重新配置为狭窄的配置。此外,其三角形配置可通过防止支撑开关来在不平坦的地面上进行高精度任务。提出了一种模型预测控制策略,该策略统一计划和控制,以同时导航,重新配置和操纵。它将任务空间目标转换为新机器人的全身运动计划。提出的设计已通过硬件原型进行了广泛的测试。足迹重新配置几乎可以完全消除操纵引起的振动。控制策略在实验室实验和现实世界的施工任务中被证明有效。
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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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医疗机器人技术可以帮助改善和扩大医疗服务的影响力。医疗机器人的一个主要挑战是机器人与患者之间的复杂物理相互作用是必须安全的。这项工作介绍了基于医疗应用中分形阻抗控制(FIC)的最近引入的控制体系结构的初步评估。部署的FIC体系结构在主机和复制机器人之间延迟很强。它可以在接纳和阻抗行为之间在线切换,并且与非结构化环境的互动是强大的。我们的实验分析了三种情况:远程手术,康复和远程超声扫描。实验不需要对机器人调整进行任何调整,这在操作员没有调整控制器所需的工程背景的医疗应用中至关重要。我们的结果表明,可以使用手术刀进行切割机器人,进行超声扫描并进行远程职业治疗。但是,我们的实验还强调了需要更好的机器人实施例,以精确控制3D动态任务中的系统。
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我们描述了更改 - 联系机器人操作任务的框架,要求机器人与对象和表面打破触点。这种任务的不连续交互动态使得难以构建和使用单个动力学模型或控制策略,并且接触变化期间动态的高度非线性性质可能对机器人和物体造成损害。我们提出了一种自适应控制框架,使机器人能够逐步学习以预测更改联系人任务中的接触变化,从而了解了碎片连续系统的交互动态,并使用任务空间可变阻抗控制器提供平滑且精确的轨迹跟踪。我们通过实验比较我们框架的表现,以确定所需的代表性控制方法,以确定我们框架的自适应控制和增量学习组件需要在变化 - 联系机器人操纵任务中存在不连续动态的平稳控制。
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Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning research for legged robots is mainly limited to simulation, and only few and comparably simple examples have been deployed on real systems. The primary reason is that training with real robots, particularly with dynamically balancing systems, is complicated and expensive. In the present work, we report a new method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby we leverage fast, automated, and cost-effective data generation schemes. The approach is applied to the ANYmal robot, a sophisticated medium-dog-sized quadrupedal system. Using policies trained in simulation, the quadrupedal machine achieves locomotion skills that go beyond what had been achieved with prior methods: ANYmal is capable of precisely and energy-efficiently following high-level body velocity commands, running faster than ever before, and recovering from falling even in complex configurations.
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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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In this paper, we present a novel control architecture for the online adaptation of bipedal locomotion on inclined obstacles. In particular, we introduce a novel, cost-effective, and versatile foot sensor to detect the proximity of the robot's feet to the ground (bump sensor). By employing this sensor, feedback controllers are implemented to reduce the impact forces during the transition of the swing to stance phase or steeping on inclined unseen obstacles. Compared to conventional sensors based on contact reaction force, this sensor detects the distance to the ground or obstacles before the foot touches the obstacle and therefore provides predictive information to anticipate the obstacles. The controller of the proposed bump sensor interacts with another admittance controller to adjust leg length. The walking experiments show successful locomotion on the unseen inclined obstacle without reducing the locomotion speed with a slope angle of 12. Foot position error causes a hard impact with the ground as a consequence of accumulative error caused by links and connections' deflection (which is manufactured by university tools). The proposed framework drastically reduces the feet' impact with the ground.
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设置机器人环境快速测试新开发的算法仍然是一个困难且耗时的过程。这给有兴趣执行现实世界机器人实验的研究人员带来了重大障碍。Robotio是一个旨在解决此问题的Python库。它着重于为机器人,抓地力和摄像机等提供常见,简单和结构化的Python接口。这些接口以及这些接口的实现为常见硬件提供了。此启用使用机器人的代码可以在不同的机器人设置上可移植。在建筑方面,Robotio旨在与OpenAI健身房环境以及ROS兼容。提供了这两种示例。该库与许多有用的工具一起融合在一起,例如相机校准脚本和情节记录功能,这些功能进一步支持算法开发。
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在本次调查中,我们介绍了执行需要不同于环境的操作任务的机器人的当前状态,使得机器人必须隐含地或明确地控制与环境的接触力来完成任务。机器人可以执行越来越多的人体操作任务,并且在1)主题上具有越来越多的出版物,其执行始终需要联系的任务,并且通过利用完美的任务来减轻环境来缓解不确定性信息,可以在没有联系的情况下进行。最近的趋势已经看到机器人在留下的人类留给人类,例如按摩,以及诸如PEG孔的经典任务中,对其他类似任务的概率更有效,更好的误差容忍以及更快的规划或学习任务。因此,在本调查中,我们涵盖了执行此类任务的机器人的当前阶段,从调查开始所有不同的联系方式机器人可以执行,观察这些任务是如何控制和表示的,并且最终呈现所需技能的学习和规划完成这些任务。
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飞行操纵器是带有附着的刚性机器人的空中无人机,属于机器人的最新和最积极开发的研究领域。这些臂的刚性性质往往缺乏遵守,灵活性和运动平滑。这项工作建议使用柔软的机器人臂连接到全向微空中飞行器(OMAV),以利用臂的柔顺和灵活的行为,同时留下可操纵和动态的,感谢全向无人机作为浮座。随机臂的统一在这种组合平台的建模和控制中造成挑战;这些挑战是通过这项工作解决的。我们基于三个建模原理提出了飞行机械手的统一模型:分段恒定曲率(PCC)和增强刚体模型(ABBM)假设用于建模软连续式机器人和传统刚体机器人借用的浮动基础方法文学。为了演示该参数化的有效性和有用性,实现了一种基于分层模型的反馈控制器。在各种动态任务的模拟中验证并评估控制器,其中检查并验证了该平台的无缺陷运动,干扰恢复和轨迹跟踪能力。软飞行机械手平台可以在空中建筑,货物交付,人力援助,维护和仓库自动化中打开新的应用领域。
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