本文研究了一个四轮摩托车在城市地区的快速安全航空有效载荷运输的问题。四轮有效载荷系统（QPS）被视为刚体，并以非线性动力学进行建模。城市区域被建模为充满障碍物的环境，并通过合并现实的激光雷达数据获得了障碍物几何形状。我们的有效载荷运输方法分解为高级运动计划和低级轨迹控制。对于低级轨迹跟踪，应用反馈线性化控制将稳定地跟踪四轮驱动器的所需轨迹。对于高级运动计划，我们集成了*搜索和多项式计划，以定义四肢避免碰撞，四极管转子速度的界限和跟踪错误的安全轨迹，并从任意初始位置快速到达目标目的地。

We present a dynamic path planning algorithm to navigate an amphibious rotor craft through a concave time-invariant obstacle field while attempting to minimize energy usage. We create a nonlinear quaternion state model that represents the rotor craft dynamics above and below the water. The 6 degree of freedom dynamics used within a layered architecture to generate motion paths for the vehicle to follow and the required control inputs. The rotor craft has a 3 dimensional map of its surroundings that is updated via limited range onboard sensor readings within the current medium (air or water). Path planning is done via PRM and D* Lite.

Quadcopter trajectory tracking control has been extensively investigated and implemented in the past. Available controls mostly use the Euler angle standards to describe the quadcopters rotational kinematics and dynamics. As a result, the same rotation can be translated into different roll, pitch, and yaw angles because there are multiple Euler angle standards for characterization of rotation in a 3-dimensional motion space. Additionally, it is computationally expensive to convert a quadcopters orientation to the associated roll, pitch, and yaw angles, which may make it difficult to track quick and aggressive trajectories. To address these issues, this paper will develop a flatness-based trajectory tracking control without using Euler angles. We assess and test the proposed controls performance in the Gazebo simulation environment and contrast its functionality with the existing Mellinger controller, which has been widely adopted by the robotics and unmanned aerial system (UAS) communities.

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。

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$^2$.

本文着重于影响弹性的移动机器人的碰撞运动计划和控制的新兴范式转移，并开发了一个统一的层次结构框架，用于在未知和部分观察的杂物空间中导航。在较低级别上，我们开发了一种变形恢复控制和轨迹重新启动策略，该策略处理可能在本地运行时发生的碰撞。低级系统会积极检测碰撞（通过内部内置的移动机器人上的嵌入式霍尔效应传感器），使机器人能够从其内部恢复，并在本地调整后影响后的轨迹。然后，在高层，我们提出了一种基于搜索的计划算法，以确定如何最好地利用潜在的碰撞来改善某些指标，例如控制能量和计算时间。我们的方法建立在A*带有跳跃点的基础上。我们生成了一种新颖的启发式功能，并进行了碰撞检查和调整技术，从而使A*算法通过利用和利用可能的碰撞来更快地收敛到达目标。通过将全局A*算法和局部变形恢复和重新融合策略以及该框架的各个组件相结合而生成的整体分层框架在模拟和实验中都经过了广泛的测试。一项消融研究借鉴了与基于搜索的最先进的避免碰撞计划者（用于整体框架）的链接，以及基于搜索的避免碰撞和基于采样的碰撞 - 碰撞 - 全球规划师（对于更高的较高的碰撞 - 等级）。结果证明了我们的方法在未知环境中具有碰撞的运动计划和控制的功效，在2D中运行的一类撞击弹性机器人具有孤立的障碍物。

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.

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。

This paper proposes a novel controller framework that provides trajectory tracking for an Aerial Manipulator (AM) while ensuring the safe operation of the system under unknown bounded disturbances. The AM considered here is a 2-DOF (degrees-of-freedom) manipulator rigidly attached to a UAV. Our proposed controller structure follows the conventional inner loop PID control for attitude dynamics and an outer loop controller for tracking a reference trajectory. The outer loop control is based on the Model Predictive Control (MPC) with constraints derived using the Barrier Lyapunov Function (BLF) for the safe operation of the AM. BLF-based constraints are proposed for two objectives, viz. 1) To avoid the AM from colliding with static obstacles like a rectangular wall, and 2) To maintain the end effector of the manipulator within the desired workspace. The proposed BLF ensures that the above-mentioned objectives are satisfied even in the presence of unknown bounded disturbances. The capabilities of the proposed controller are demonstrated through high-fidelity non-linear simulations with parameters derived from a real laboratory scale AM. We compare the performance of our controller with other state-of-the-art MPC controllers for AM.

本文提出了一种用于特技飞行轨迹生成的新型算法，用于垂直起飞和降落（VTOL）TAILSITTER飞行飞机。该算法与固定翼轨迹生成的现有方法不同，因为它考虑了现实的六度自由度（6DOF）飞行动力学模型，包括空气动力学方程。使用全球动力学模型，能够生成特技轨迹，从而利用整个飞行信封，从而使敏捷的操纵通过摊位策略，侧向飞行，倒置飞行等。是在这项工作中得出的。通过在差异平坦的输出空间中执行快速最小化，可以获得适合在线运动计划的计算高效算法。该算法在包括六架特技飞行器的大型飞行实验中证明了这一算法，一个时间优势的无人机赛车轨迹以及三架尾灯飞机的飞机样有机赛序列。

本文示出了一般类空中机械手的动态，包括具有任意K型铰接式操纵器的废隔多转子底座，差异平坦。在破裂对称下的拉格朗日减少方法产生了缩小的运动方程，其关键变量：质量线性线性动量，车辆偏航角，操纵子相对接头角度成为扁平输出。利用平坦度理论和推力输入的二阶动态延伸，我们通过有效的相对程度将空中机械手的机制转变为其等效的微观形式。使用这种平坦度变换，在控制Lyapunov函数（CLF-QP）框架内提出了一种二次编程的控制器，并且在仿真中验证了其性能。

稳定性和安全性是成功部署自动控制系统的关键特性。作为一个激励示例，请考虑在复杂的环境中自动移动机器人导航。概括到不同操作条件的控制设计需要系统动力学模型，鲁棒性建模错误以及对安全\ newzl {约束}的满意度，例如避免碰撞。本文开发了一个神经普通微分方程网络，以从轨迹数据中学习哈密顿系统的动态。学识渊博的哈密顿模型用于合成基于能量的被动性控制器，并分析其\ emph {鲁棒性}，以在学习模型及其\ emph {Safety}中对环境施加的约束。考虑到系统的所需参考路径，我们使用虚拟参考调查员扩展了设计，以实现跟踪控制。州长国家是一个调节点，沿参考路径移动，平衡系统能级，模型不确定性界限以及违反安全性的距离，以确保稳健性和安全性。我们的哈密顿动力学学习和跟踪控制技术在\修订后的{模拟的己谐和四型机器人}在混乱的3D环境中导航。

考虑了使用间歇性冲动力在三维空间中对棍子进行非骚扰操作的问题。目的是在一系列旋转对称的垂直轴对称的配置序列之间兼顾棍子。棍棒的动力学由五个广义坐标和三个控制输入描述。在应用冲动输入的两种连续配置之间，动力学在杂耍者的参考框架中以Poincar \'E映射为方便地表示。通过稳定庞加尔\'e地图上的固定点来实现与所需杂耍运动相关的轨道的稳定化。脉冲控制的Poincar \'e MAP方法用于稳定轨道，数值模拟用于证明与任意初始配置中所需的杂耍运动的收敛。在限制情况下，如果连续旋转对称配置被任意接近，则表明动力学将减少到箍上杆上稳定进动的动力学。

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.

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.

This paper presents a state-of-the-art optimal controller for quadruped locomotion. The robot dynamics is represented using a single rigid body (SRB) model. A linear time-varying model predictive controller (LTV MPC) is proposed by using linearization schemes. Simulation results show that the LTV MPC can execute various gaits, such as trot and crawl, and is capable of tracking desired reference trajectories even under unknown external disturbances. The LTV MPC is implemented as a quadratic program using qpOASES through the CasADi interface at 50 Hz. The proposed MPC can reach up to 1 m/s top speed with an acceleration of 0.5 m/s2 executing a trot gait. The implementation is available at https:// github.com/AndrewZheng-1011/Quad_ConvexMPC

对于腿部机器人，航空动作是唯一可以通过标准运动步态绕过的障碍物的唯一选择。在这些情况下，机器人必须进行飞跃，以跳到障碍物或飞越障碍物上。但是，这些运动代表了一个挑战，因为在飞行阶段\ gls {com}无法控制，并且机器人方向的可控性有限。本文重点介绍了后一个问题，并提出了一个由两个旋转和驱动的质量（飞轮或反应轮）组成的\ gls {ocs}，以获得机器人方向的控制权。由于角动量的保护，即使与地面没有接触，它们的旋转速度也可以调节以引导机器人方向。飞轮的旋转轴设计为入射，导致一个紧凑的方向控制系统，该系统能够控制滚动和俯仰角，考虑到这两个方向的不同惯性矩。我们通过机器人Solo12上的模拟测试了该概念。

在许多无人机应用中，为空中机器人计划的时间轨迹至关重要，例如救援任务和包装交付，这些应用程序近年来已经广泛研究。但是，它仍然涉及一些挑战，尤其是在将特殊任务要求纳入计划以及空中机器人的动态方面。在这项工作中，我们研究了一种案例，使空中操纵器应以时间优势的方式从移动的移动机器人中移交一个包裹。我们没有手动设置方法轨迹，这使得很难确定在动态范围内完成所需任务的最佳总行进时间，而是提出了一个优化框架，该框架将离散的力学和互补性约束（DMCC）结合在一起。在提出的框架中，系统动力学受到离散的拉格朗日力学的约束，该机械也根据我们的实验提供了可靠的估计结果。移交机会是根据所需的互补限制自动确定和安排的。最后，通过使用我们的自设计的空中操纵器进行数值模拟和硬件实验来验证所提出的框架的性能。

以前已经评估过使用轮毂，无人驾驶飞机，立方体，小萨特人等进行空中和地面操纵，感知和侦察的可行性。在所有这些解决方案中，基于气球的系统具有使其极具吸引力的优点，例如，简单的操作机构和持久的操作时间。但是，在基于气球的应用中，有许多障碍要克服，以实现强大的游荡性能。我们试图确定设计和控制挑战，并提出一个新型的机器人平台，该平台允许在火星陨石坑的侦察和感知中应用气球。这项工作简要涵盖了我们建议的驱动和模型预测控制设计框架，用于转向此类气球系统。我们提出了多个无人接地车辆（UGV）的协调伺服，以调节电缆驱动的气球中的张力，并将其连接到未成熟的悬挂有效载荷上。

用无人驾驶飞行器（无人机）的操纵和抓住目前需要准确定位，并且通常以减小的速度执行，以确保成功的掌握。这是由于典型的无人机只能容纳具有少量自由度的刚性机械手，这限制了它们可以补偿由车辆定位误差引起的扰动的能力。此外，无人机必须最小化外部接触力以保持稳定性。另一方面，生物系统利用柔软度来克服类似的限制，并利用遵守来实现积极的抓握。本文调查了软空气机械手的控制和轨迹优化，由四射线和肌腱驱动的软夹持器组成，其中可以充分利用柔软度的优点。据我们所知，这是软操作和UAV控制之间交叉路口的第一个工作。我们介绍了四轮电机和软夹具的解耦方法，组合（i）几何控制器和四峰值（刚性）基础的最小拍摄轨迹优化，（ii）准静态有限元模型和控制空间软夹具的插值。我们证明了尽管添加了软载荷，但几何控制器渐近稳定了四轮流速度和姿态。最后，我们在逼真的软动力学模拟器中评估所提出的系统，并表明：（i）几何控制器对软有效载荷相对不敏感，（ii）尽管定位和初始条件不准确和初始条件，平台可以可靠地掌握未知对象，以及（iii）解耦控制器可用于实时执行。