Online Whole-body Motion Planning for Quadrotor using Multi-resolution Search

In this paper, we address the problem of online quadrotor whole-body motion planning (SE(3) planning) in unknown and unstructured environments. We propose a novel multi-resolution search method, which discovers narrow areas requiring full pose planning and normal areas requiring only position planning. As a consequence, a quadrotor planning problem is decomposed into several SE(3) (if necessary) and R^3 sub-problems. To fly through the discovered narrow areas, a carefully designed corridor generation strategy for narrow areas is proposed, which significantly increases the planning success rate. The overall problem decomposition and hierarchical planning framework substantially accelerate the planning process, making it possible to work online with fully onboard sensing and computation in unknown environments. Extensive simulation benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the state-of-the-art methods in computation time while maintaining high planning success rate. The proposed method is finally integrated into a LiDAR-based autonomous quadrotor, and various real-world experiments in unknown and unstructured environments are conducted to demonstrate the outstanding performance of the proposed method

PerturbScore: Connecting Discrete and Continuous Perturbations in NLP

With the rapid development of neural network applications in NLP, model robustness problem is gaining more attention. Different from computer vision, the discrete nature of texts makes it more challenging to explore robustness in NLP. Therefore, in this paper, we aim to connect discrete perturbations with continuous perturbations, therefore we can use such connections as a bridge to help understand discrete perturbations in NLP models. Specifically, we first explore how to connect and measure the correlation between discrete perturbations and continuous perturbations. Then we design a regression task as a PerturbScore to learn the correlation automatically. Through experimental results, we find that we can build a connection between discrete and continuous perturbations and use the proposed PerturbScore to learn such correlation, surpassing previous methods used in discrete perturbation measuring. Further, the proposed PerturbScore can be well generalized to different datasets, perturbation methods, indicating that we can use it as a powerful tool to study model robustness in NLP.Comment: Accepted by Findings of EMNLP202

Image Aesthetics Assessment via Learnable Queries

Image aesthetics assessment (IAA) aims to estimate the aesthetics of images. Depending on the content of an image, diverse criteria need to be selected to assess its aesthetics. Existing works utilize pre-trained vision backbones based on content knowledge to learn image aesthetics. However, training those backbones is time-consuming and suffers from attention dispersion. Inspired by learnable queries in vision-language alignment, we propose the Image Aesthetics Assessment via Learnable Queries (IAA-LQ) approach. It adapts learnable queries to extract aesthetic features from pre-trained image features obtained from a frozen image encoder. Extensive experiments on real-world data demonstrate the advantages of IAA-LQ, beating the best state-of-the-art method by 2.2% and 2.1% in terms of SRCC and PLCC, respectively.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Trajectory Generation and Tracking Control for Aggressive Tail-Sitter Flights

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

ImMesh: An Immediate LiDAR Localization and Meshing Framework

In this paper, we propose a novel LiDAR(-inertial) odometry and mapping framework to achieve the goal of simultaneous localization and meshing in real-time. This proposed framework termed ImMesh comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module utilizes the prepossessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster. The key contribution of this work is the meshing module, which represents a scene by an efficient hierarchical voxels structure, performs fast finding of voxels observed by new scans, and reconstructs triangle facets in each voxel in an incremental manner. This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3D points to a 2D local plane contained in the voxel, and then executes the meshing operation with pull, commit and push steps for incremental reconstruction of triangle facets. To the best of our knowledge, this is the first work in literature that can reconstruct online the triangle mesh of large-scale scenes, just relying on a standard CPU without GPU acceleration. To share our findings and make contributions to the community, we make our code publicly available on our GitHub: https://github.com/hku-mars/ImMesh

Worldwide tests of generic attractants, a promising tool for early detection of non-native cerambycid species

A large proportion of the insects which have invaded new regions and countries are emerging species, being found for the first time outside their native range. Being able to detect such species upon arrival at ports of entry before they establish in non-native countries is an urgent challenge. The deployment of traps baited with broad-spectrum semiochemical lures at ports-of-entry and other high-risk sites could be one such early detection tool. Rapid progress in the identification of semiochemicals for cerambycid beetles during the last 15 years has revealed that aggregation-sex pheromones and sex pheromones are often conserved at global levels for genera, tribes or subfamilies of the Cerambycidae. This possibly allows the development of generic attractants which attract multiple species simultaneously, especially when such pheromones are combined into blends. Here, we present the results of a worldwide field trial programme conducted during 2018-2021, using traps baited with a standardised 8-pheromone blend, usually com-plemented with plant volatiles. A total of 1308 traps were deployed at 302 sites covering simultaneously or sequentially 13 European countries, 10 Chinese provinces and some regions of the USA, Canada, Australia, Russia (Siberia) and the Caribbean (Martinique). We intended to test the following hypotheses: 1) if a species is regularly trapped in significant numbers by the blend on a continent, it increases the prob-ability that it can be detected when it arrives in other countries/continents and 2) if the blend exerts an effective, generic attraction to multiple species, it is likely that previously unknown and unexpected spe-cies can be captured due to the high degree of conservation of pheromone structures within related taxa. A total of 78,321 longhorned beetles were trapped, representing 376 species from eight subfamilies, with 84 species captured in numbers greater than 50 individuals. Captures comprised 60 tribes, with 10 tribes including more than nine species trapped on different continents. Some invasive species were captured in both the native and invaded continents. This demonstrates the potential of multipheromone lures as ef-fective tools for the detection of 'unexpected' cerambycid invaders, accidentally translocated outside their native ranges. Adding new pheromones with analogous well-conserved motifs is discussed, as well as the limitations of using such blends, especially for some cerambycid taxa which may be more attracted by the trap colour or other characteristics rather than to the chemical blend

Effects of Graphene Nanoplates on the Mechanical Behavior and Strengthening Mechanism of 7075Al Alloy

7075Al alloy is the preferred material for lightweight automotive applications, but the existing problem is that it is difficult to combine high strength and high toughness. This paper presents our research aimed at obtaining high strength and high toughness materials by adding a nano-phase to realize microstructure refinement. Graphene nanoplates (GNP)/7075Al composites and 7075Al alloy were prepared by a stirring casting method in the present study. In comparison to 7075Al, the tensile strength of GNP/7075Al composites was increased from 572 MPa to 632 MPa while maintaining good uniform elongation of 8% to 10%. The increased strength behavior of GNP/7075Al composites while maintaining the plasticity is discussed in terms of grain refinement and dislocation evolution by analyzing the composite microstructure and quantitatively analyzing the contributions of grain boundary strengthening, solid solution strengthening, precipitation strengthening and dislocation strengthening. GNP’s strengthening of GNP/7075Al composites is mainly attributed to the refinement of grain size and the increase of dislocation density

Characteristics and Driving Factors of Nitrogen-Use Efficiency in Chinese Greenhouse Tomato Cultivation

Excessive nitrogen fertilizer application in greenhouses could cause a significant variation in the nitrogen-use efficiency at the regional scale. This study aims to quantify agronomic nitrogen-use efficiency (AEN) and identify its driving factors across Chinese greenhouse tomato cultivation. Three hundred and forty-eight AEN values were obtained from 64 papers, including mineral nitrogen (MN) and mineral combined with organic nitrogen (MON) treatments. The average AEN values for the MN and MON treatments were 56.6 ± 7.0 kg kg−1 and 34.6 ± 3.5 kg kg−1, respectively. The AEN of the MN treatment was higher than that of the MON treatment for cultivation using soil with an organic matter content of less than 10 g kg−1 and the drip fertigation method. The AENs of the MN and MON treatments were divided into two segments according to the nitrogen application rate. The inflection points of the nitrogen application rate were 290 and 1100 kg N ha−1 for the MN and MON treatments, respectively. When the ratio of organic nitrogen to total nitrogen was less than 0.4, it was beneficial for improving the AEN. The soil organic matter content and the nitrogen application rate were the most critical factors determining the AEN. These results suggest that rationally reducing the nitrogen input and partially substituting mineral nitrogen with organic nitrogen can help improve the nitrogen-use efficiency

Characteristics and Driving Factors of Nitrogen-Use Efficiency in Chinese Greenhouse Tomato Cultivation

Excessive nitrogen fertilizer application in greenhouses could cause a significant variation in the nitrogen-use efficiency at the regional scale. This study aims to quantify agronomic nitrogen-use efficiency (AEN) and identify its driving factors across Chinese greenhouse tomato cultivation. Three hundred and forty-eight AEN values were obtained from 64 papers, including mineral nitrogen (MN) and mineral combined with organic nitrogen (MON) treatments. The average AEN values for the MN and MON treatments were 56.6 ± 7.0 kg kg−1 and 34.6 ± 3.5 kg kg−1, respectively. The AEN of the MN treatment was higher than that of the MON treatment for cultivation using soil with an organic matter content of less than 10 g kg−1 and the drip fertigation method. The AENs of the MN and MON treatments were divided into two segments according to the nitrogen application rate. The inflection points of the nitrogen application rate were 290 and 1100 kg N ha−1 for the MN and MON treatments, respectively. When the ratio of organic nitrogen to total nitrogen was less than 0.4, it was beneficial for improving the AEN. The soil organic matter content and the nitrogen application rate were the most critical factors determining the AEN. These results suggest that rationally reducing the nitrogen input and partially substituting mineral nitrogen with organic nitrogen can help improve the nitrogen-use efficiency

Hierarchical Capillary Coating to Biofunctionlize Drug-Eluting Stent for Improving Endothelium Regeneration

The drug-eluting stent (DES) has become one of the most successful and important medical devices for coronary heart disease, but yet suffers from insufficient endothelial cell (EC) growth and intima repair, eventually leading to treatment failure. Although biomacromolecules such as vascular endothelial growth factor (VEGF) would be promising to promote the intima regeneration, combining hydrophilic and vulnerable biomacromolecules with hydrophobic drugs as well as preserving the bioactivity after harsh treatments pose a huge challenge. Here, we report on a design of hierarchical capillary coating, which composes a base solid region and a top microporous region for incorporating rapamycin and VEGF, respectively. The top spongy region can guarantee the efficient, safe, and controllable loading of VEGF up to 1 μg/cm2 in 1 minute, providing a distinctive real-time loading capacity for saving the bioactivity. Based on this, we demonstrate that our rapamycin-VEGF hierarchical coating impressively promoted the competitive growth of endothelial cells over smooth muscle cells (ratio of EC/SMC~25) while relieving the adverse impact of rapamycin to ECs. We further conducted the real-time loading of VEGF on stents and demonstrate that the hierarchical combination of rapamycin and VEGF showed remarkable endothelium regeneration while maintaining a very low level of in-stent restenosis. This work paves an avenue for the combination of both hydrophobic and hydrophilic functional molecules, which should benefit the next generation of DES and may extend applications to diversified combination medical devices