我们提出了一个用于大型代理集合的运动规划的分层框架。所提出的框架从低级运动原语过度网格化工作空间开始,并提供一组用于构造更高级别运动原语的规则。我们的分层方法具有高度可扩展性和强大的可靠性,是规划多代理系统的理想工具。结果通过实验证明了四旋翼飞行器的集合,这些四旋翼飞行器必须在保持阵型的同时导航整齐的环境。
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我们提出了一个模块化框架,用于解决机器人群之间的运动规划问题。所提出的框架利用一组有限的低水平运动基元来在网格化工作空间中生成运动。允许的运动基元序列的约束通过机动自动机形式化。在高级别,控制策略确定在网格化工作空间的每个框中执行哪个动画原型。我们陈述了运动原语的一般条件,以获得可证明的正确行为,从而可以设计出安全设计运动原语的图书馆。整体框架通过利用低水平和高水平的反馈策略产生高度稳健的设计。我们提供适用于多机器人运动规划的运动原语和控制策略的特定设计;我们的方法的模块化使人们能够独立定制每个组件的设计。我们的方法通过实验验证了一组四面体。
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基于高斯过程模型的贝叶斯优化(BO)是优化评估成本昂贵的黑盒函数的有力范例。虽然几个BO算法可证明地收敛到未知函数的全局最优,但他们认为内核的超参数是已知的。在实践中情况并非如此,并且错误指定经常导致这些算法收敛到较差的局部最优。在本文中,我们提出了第一个BO算法,它可以证明是无后悔的,并且在不参考超参数的情况下收敛到最优。我们慢慢地调整了固定核的超参数,从而扩展了相关的函数类超时,使BO算法考虑了更复杂的函数候选。基于理论上的见解,我们提出了几种实用的算法,通过在线超参数估计来实现BO的经验数据效率,但是保留理论收敛保证。我们评估了几个基准问题的方法。
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SAE AutoDrive Challenge是一项为期三年的竞赛,旨在到2020年开发4级自主车。第一组挑战于2018年4月在亚利桑那州尤马市举行。我们的团队(aUToronto / Zeus)排名第一。在本文中,我们描述了我们完整的系统架构和专业算法,使我们能够获胜。我们表明,依靠简单,强大的算法,可以在短短六个月内开发出具有基础设施特征的车辆。 Wedo没有使用先前的地图。相反,我们开发了一种多传感器视觉本地化解决方案。我们所有的算法都使用CPUsonly实时运行。我们还详细介绍了我们系统的闭环性能。
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We present a control method for improved repetitive path following for a ground vehicle that is geared towards long-term operation where the operating conditions can change over time and are initially unknown. We use weighted Bayesian Linear Regression (wBLR) to model the unknown dynamics, and show how this simple model is more accurate in both its estimate of the mean behaviour and model uncertainty than Gaussian Process Regression and generalizes to novel operating conditions with little or no tuning. In addition, wBLR allows us to use fast adaptation and long-term learning in one, unified framework, to adapt quickly to new operating conditions and learn repetitive model errors over time. This comes with the added benefit of lower computational cost, longer look-ahead, and easier optimization when the model is used in a stochastic Model Predictive Controller (MPC). In order to fully capitalize on the long prediction horizons that are possible with this new approach, we use Tube MPC to reduce the growth of predicted uncertainty. We demonstrate the effectiveness of our approach in experiment on a 900 kg ground robot showing results over 3.0 km of driving with both physical and artificial changes to the robot's dynamics. All of our experiments are conducted using a stereo camera for localization. I. INTRODUCTION This paper presents a new probabilistic method for modelling robot dynamics geared towards stochastic Model Pre-dictive Control (MPC) and repetitive path following tasks. The goal of our approach is to enable a robot to operate in challenging and changing environments with minimal expert input and prior knowledge of the operating conditions. Our study is motivated by our previous work with Gaussian Processes (GPs) on this topic [1] and an interest in deploying robots in a wide range of operating conditions. Our method requires the unknown part of the dynamics to be linear in a set of model parameters. Safe control methods have emerged as a way to guarantee that safety constraints (e.g. a bound on maximum path tracking error) are kept in the face of model errors. Having an accurate estimate of model error is of critical importance to the validity of these safety guarantees. In order to derive models for complex systems or systems operating in challenging operating conditions, researchers increasingly rely on tools from machine learning. In particular, probabilistic models are used since they provide a measure of model uncertainty which can naturally be used to derive an upper bound on model error. Two common methods for doing this are GP regression [1]-[3] and various forms of local linear regression [4]-[6].
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This paper aims to design quadrotor swarm performances , where the swarm acts as an integrated, coordinated unit embodying moving and deforming objects. We divide the task of creating a choreography into three basic steps: designing swarm motion primitives, transitioning between those movements, and synchronizing the motion of the drones. The result is a flexible framework for designing choreographies comprised of a wide variety of motions. The motion primitives can be intuitively designed using a few parameters, providing a rich library for choreography design. Moreover, we combine and adapt existing goal assignment and trajectory generation algorithms to maximize the smoothness of the transitions between motion primitives. Finally, we propose a correction algorithm to compensate for motion delays and synchronize the motion of the drones to a desired periodic motion pattern. The proposed methodology was validated experimentally by generating and executing choreographies on a swarm of 25 quadrotors.
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This paper introduces a novel algorithm for mul-tiagent offline trajectory generation based on distributed model predictive control (DMPC). Central to the algorithm's scalability and success is the development of an on-demand collision avoidance strategy. By predicting future states and sharing this information with their neighbours, the agents are able to detect and avoid collisions while moving towards their goals. The proposed algorithm can be implemented in a distributed fashion and reduces the computation time by more than 85% compared to previous optimization approaches based on sequential convex programming (SCP), while only having a small impact on the optimality of the plans. The approach was validated both through extensive simulations and experimentally with teams of up to 25 quadrotors flying in confined indoor spaces.
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拉格朗日系统代表了广泛的机器人系统,包括操纵器,轮式和腿式机器人以及四旋翼飞行器。逆动力学控制和前馈线性化技术通常用于将拉格朗日系统的复杂非线性动力学转换为一组解耦双积分器,然后可以使用标准的外环控制器来计算线性化系统的指令加速度。然而,这些方法通常依赖于具有非常精确的系统模型,这在实践中通常是不可用的。尽管使用不同的学习方法已经在文献中解决了这一挑战,但是大多数这些方法在基于学习的控制系统的稳定性方面没有提供安全保证。在本文中,我们提供了一种基于学习的新型控制方法,该方法基于高斯过程(GP),确保闭环系统的稳定性和高精度跟踪。我们使用GP来近似命令加速度和系统的实际加速度之间的误差,然后使用GP的预测均值和方差来计算线性化模型的不确定性的上限。然后将该不确定性边界用于稳健的外环控制器中以确保整个系统的稳定性。此外,我们表明跟踪误差收敛于具有可以任意小的半径的球。此外,我们通过2自由度(DOF)平面机械手的仿真验证了我们的方法的有效性,并在6 DOF工业机械手上进行了实验验证。
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Control theory can provide useful insights into the properties of controlled,dynamic systems. One important property of nonlinear systems is the region ofattraction (ROA), a safe subset of the state space in which a given controllerrenders an equilibrium point asymptotically stable. The ROA is typicallyestimated based on a model of the system. However, since models are only anapproximation of the real world, the resulting estimated safe region cancontain states outside the ROA of the real system. This is not acceptable insafety-critical applications. In this paper, we consider an approach thatlearns the ROA from experiments on a real system, without ever leaving the trueROA and, thus, without risking safety-critical failures. Based on regularityassumptions on the model errors in terms of a Gaussian process prior, we use anunderlying Lyapunov function in order to determine a region in which anequilibrium point is asymptotically stable with high probability. Moreover, weprovide an algorithm to actively and safely explore the state space in order toexpand the ROA estimate. We demonstrate the effectiveness of this method insimulation.
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定量磁敏度作图(QSM)是一种强大的MRI技术,在量化许多神经疾病中的组织易感性方面显示出巨大的潜力。然而,内在的不适定偶极子反演问题极大地影响了磁化率图的准确性。我们提出了QSMGAN:一种基于改进的U-Net的3D深度卷积神经网络方法,增加了相位感受野,并使用WWAN-GP训练策略进一步细化了网络。我们的方法可以有效地从单方向相位图生成准确和逼真的QSM,并且比传统的基于非学习的偶极子反演算法表现得更好。
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