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。

Riemannian geometry provides powerful tools to explore the latent space of generative models while preserving the inherent structure of the data manifold. Lengths, energies and volume measures can be derived from a pullback metric, defined through the immersion that maps the latent space to the data space. With this in mind, most generative models are stochastic, and so is the pullback metric. Manipulating stochastic objects is strenuous in practice. In order to perform operations such as interpolations, or measuring the distance between data points, we need a deterministic approximation of the pullback metric. In this work, we are defining a new metric as the expected length derived from the stochastic pullback metric. We show this metric is Finslerian, and we compare it with the expected pullback metric. In high dimensions, we show that the metrics converge to each other at a rate of $\mathcal{O}\left(\frac{1}{D}\right)$.

Insects as pollinators play a key role in ecosystem management and world food production. However, insect populations are declining, calling for a necessary global demand of insect monitoring. Existing methods analyze video or time-lapse images of insects in nature, but the analysis is challenging since insects are small objects in complex and dynamic scenes of natural vegetation. The current paper provides a dataset of primary honeybees visiting three different plant species during two months of summer-period. The dataset consists of more than 700,000 time-lapse images from multiple cameras, including more than 100,000 annotated images. The paper presents a new method pipeline for detecting insects in time-lapse RGB-images. The pipeline consists of a two-step process. Firstly, the time-lapse RGB-images are preprocessed to enhance insects in the images. We propose a new prepossessing enhancement method: Motion-Informed-enhancement. The technique uses motion and colors to enhance insects in images. The enhanced images are subsequently fed into a Convolutional Neural network (CNN) object detector. Motion-Informed-enhancement improves the deep learning object detectors You Only Look Once (YOLO) and Faster Region-based Convolutional Neural Networks (Faster R-CNN). Using Motion-Informed-enhancement the YOLO-detector improves average micro F1-score from 0.49 to 0.71, and the Faster R-CNN-detector improves average micro F1-score from 0.32 to 0.56 on the our dataset. Our datasets are published on: https://vision.eng.au.dk/mie/

In today's uncertain and competitive market, where enterprises are subjected to increasingly shortened product life-cycles and frequent volume changes, reconfigurable manufacturing systems (RMS) applications play a significant role in the manufacturing industry's success. Despite the advantages offered by RMS, achieving a high-efficiency degree constitutes a challenging task for stakeholders and decision-makers when they face the trade-off decisions inherent in these complex systems. This study addresses work tasks and resource allocations to workstations together with buffer capacity allocation in RMS. The aim is to simultaneously maximize throughput and minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach support decision-makers with discovered knowledge to further understand the RMS design. In particular, this study presents a problem-specific customized SMO combined with a novel flexible pattern mining method for optimizing RMS and conducting post-optimal analyzes. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision-support and production planning of RMS.

Adaptation-relevant predictions of climate change are often derived by combining climate models in a multi-model ensemble. Model evaluation methods used in performance-based ensemble weighting schemes have limitations in the context of high-impact extreme events. We introduce a locally time-invariant model evaluation method with focus on assessing the simulation of extremes. We explore the behaviour of the proposed method in predicting extreme heat days in Nairobi.

This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments -- outdoors, from urban to woodland, and indoors in warehouses and mines - without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach CFEAR, we present an in-depth investigation on a wider range of data sets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar SLAM and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160Hz.

Biological cortical networks are potentially fully recurrent networks without any distinct output layer, where recognition may instead rely on the distribution of activity across its neurons. Because such biological networks can have rich dynamics, they are well-designed to cope with dynamical interactions of the types that occur in nature, while traditional machine learning networks may struggle to make sense of such data. Here we connected a simple model neuronal network (based on the 'linear summation neuron model' featuring biologically realistic dynamics (LSM), consisting of 10 of excitatory and 10 inhibitory neurons, randomly connected) to a robot finger with multiple types of force sensors when interacting with materials of different levels of compliance. Scope: to explore the performance of the network on classification accuracy. Therefore, we compared the performance of the network output with principal component analysis of statistical features of the sensory data as well as its mechanical properties. Remarkably, even though the LSM was a very small and untrained network, and merely designed to provide rich internal network dynamics while the neuron model itself was highly simplified, we found that the LSM outperformed these other statistical approaches in terms of accuracy.

Recent 3D-based manipulation methods either directly predict the grasp pose using 3D neural networks, or solve the grasp pose using similar objects retrieved from shape databases. However, the former faces generalizability challenges when testing with new robot arms or unseen objects; and the latter assumes that similar objects exist in the databases. We hypothesize that recent 3D modeling methods provides a path towards building digital replica of the evaluation scene that affords physical simulation and supports robust manipulation algorithm learning. We propose to reconstruct high-quality meshes from real-world point clouds using state-of-the-art neural surface reconstruction method (the Real2Sim step). Because most simulators take meshes for fast simulation, the reconstructed meshes enable grasp pose labels generation without human efforts. The generated labels can train grasp network that performs robustly in the real evaluation scene (the Sim2Real step). In synthetic and real experiments, we show that the Real2Sim2Real pipeline performs better than baseline grasp networks trained with a large dataset and a grasp sampling method with retrieval-based reconstruction. The benefit of the Real2Sim2Real pipeline comes from 1) decoupling scene modeling and grasp sampling into sub-problems, and 2) both sub-problems can be solved with sufficiently high quality using recent 3D learning algorithms and mesh-based physical simulation techniques.

我们提出了一种拓扑优化的样品深度学习策略。我们的端到端方法受到监督，包括基于物理学的预处理和模棱两可的网络。我们分析了深度学习管道的不同组成部分如何通过大规模比较影响所需的培训样品的数量。结果表明，包括物理概念不仅会极大地提高样本效率，还可以提高预测的身体正确性。最后，我们发布了两个拓扑优化数据集，其中包含问题和相应的地面真相解决方案。我们相信这些数据集将提高该领域的可比性和未来进度。

投资专业人员依靠将公司收入推送到未来（即收入预测）来近似规模的估值（高增长阶段的私人公司）并为他们的投资决定提供了信息。这项任务是手动和经验性的，使预测质量在很大程度上取决于投资专业人员的经验和见解。此外，关于规模的财务数据通常是专有，昂贵和稀缺的，排除了广泛采用数据驱动的方法。为此，我们提出了一种模拟的收入外推（SIRE）算法，该算法在小型数据集和短时间序列上产生精细颗粒的长期收入预测。父亲将收入动力学建模为线性动力学系统（LDS），该系统使用EM算法解决。主要的创新在于如何在培训和推论过程中获得嘈杂的收入测量。 Sire为在各个部门运作并提供置信度估计的规模工作。关于两项实际任务的定量实验表明，父亲大大超过了基线方法。当父亲从短时间序列中推断出来并长期预测时，我们还会观察到高性能。绩效效率的平衡和结果的解释性也得到了经验验证。从投资专业人员的角度进行评估，父亲可以精确地找到在2至5年内具有巨大潜在回报的规模。此外，我们的定性检查说明了父亲收入预测的一些有利属性。