In this article, a reinforcement learning (RL)-based controller is proposed for a multirotor-based transportation system, guaranteeing that the trained RL controller is effective in both simulation and practical experiments. The main novelty lies in that, as far as we know, this is the first attempt of combining the advantages of nonlinear and intelligent control techniques to derive a practice-oriented RL controller for the multirotor-based transportation system, where the high-dimensional complicated dynamics are fully considered in the framework. Specifically, inspired by the physical insight of the system, a new nonlinear control approach is proposed, in which the underactuated properties and the nontrivial couplings are well handled. On this basis, an RL network is proposed to parameterize the nonlinear controller, where the obtained algorithm presents the features of strong reliability and fast convergence even in complicated working conditions (e.g., model uncertainties, parameters drift, external disturbances, and so on). Subsequently, the states are proven to asymptotically converge to the equilibrium point by Lyapunov analysis and RL techniques. A series of simulation and real world experiments are implemented to verify satisfactory positioning accuracy and robustness of the proposed algorithm.
As a by-product originating from Salen Co(III) catalysed hydrolytic kinetic resolution (HKR) of (±)-epichlorohydrin in the manufacturing procedure of L-Carnitine, ( R)-3-chloro-1,2-propanediol was utilised as a starting chiral material to prepare via key nitrile intermediates and by a final hydrolysis L-Carnitine. The new synthetic approach demonstrated an efficient utilisation of the by-product.
3D point cloud-based place recognition has gotten more attention since 3D LiDAR sensors are widely used for robotic applications and autonomous driving. Most of the existing deep point cloud-based methods take a few regular number points or image-like formats as inputs which are inability to make full use of point clouds' geometric information. This paper proposes a novel place recognition approach that is flexible and effective to handle diverse numbers of 3D LiDAR point clouds in large-scale environments. The approach is composed of feature extraction and a global descriptor encoding. The feature extraction consumes the 3D LiDAR point cloud with KPConv that can extract features efficiently and flexibly. Before the global descriptor encoding, a transformer module is employed to aggregate the contextual information that exploits the relationship of all features. The NetVLAD layer encodes the features into a global descriptor for recognizing a similar place rapidly. The proposed approach is evaluated on the KITTI odometry dataset, which demonstrates the validity of the proposed approach.
To investigate the therapeutic effect of primary reconstruction of skin avulsion injury with bilateral anterolateral thigh flaps combined with thorax umbilicus flap or latissimus dorsi flap.From June 2005 to Aug. 2011, 4 cases with skin avulsion injury on both feet were treated. The bilateral anterolateral thigh flaps, including with anterolateral thigh cutaneous nerves, were transferred to cover the feet plantar. The thorax umbilicus flap or latissimus dorsi flap were used to cover the feet dorsum.All the skin avulsion injury were reconstructed primarily. All the flaps survived completely with good cosmetic and functional results. The patients were followed up for 6 months to 2 years with good sensory recovery (two point discrimination: 14-18 mm).The skin avulsion injury on both feet can be primarily reconstructed by bilateral anterolateral thigh flaps combined with thorax umbilicus flap or latissimus dorsi flap.
Blockchain provides alluring infrastructure for distributed ledgers supporting anonymous online payments. However, existing solutions for blockchain scalability have limitations of either being increasingly cumbersome in security analysis or inherent deficiencies (e.g., surviving on duplicate transactions). Moreover, current state-of-the-art scalable blockchains suffer from low throughput when used for larger transaction blockchains. To improve scalability, we propose sibcha, a novel protocol that equipped with k (power of 2) parallel sibling chains that correspond to k transaction pools (indexed by the rightmost $$\log _{2}{k}$$ bits of transaction payers' addresses). In the protocol, i-th transaction (along with a Merkle tree path) would be announced to the i-th chain based on the rightmost $$\log _{2}{k}$$ bits of the hashing determined in solving proof-of-work (PoW) puzzle (i is the exact value in decimal format represented by the $$\log _{2}{k}$$ bits). To achieve parallel transactions, we design a inter-chain mechanism without other correlations (such as block ordering, inter-chain transactions, block updates, eventual atomicity decoupling, two-phase PoW puzzle solving, etc.), which makes sibcha considerably simpler than current state-of-the-art solutions (e.g., OHIE at IEEE S&P 2020 and Monoxide at USENIX Security 2019). SibCha has much less (e.g., 1.86 $$\times \sim $$ 3.16 $$\times $$ ) confirmation latency than OHIE. Prototype implementations also demonstrate that its throughput scales linearly with available bandwidth (1.5 $$\times $$ that of Conflux).
In this article, we propose a novel optimization-based tightly coupled Direct Visual-Inertial Odometry (DVIO), which fuses the visual and inertial measurements to provide real-time full state estimation. Different from existing frameworks, the key novelty of the proposed method is to integrate the data association, state estimation, and outlier detection into a nonlinear optimization framework in a tightly coupled way. Specifically, by jointly minimizing the preintegration error of the inertial measurement unit and the photometric error of the camera, the data association is tightly coupled with the process of state estimation. Then, an iterative selection strategy is design to reject outliers during the data association and establish more visual constraints in the optimization. In addition, a hybrid weighting method is proposed to tightly integrate the iterative selection into the optimization by dynamically weighting residual terms. As a consequence, the proposed method aligns the image patches, estimates the motion and removes the outliers synchronously. Comparative experiments on the public dataset and extensive real-world experiments show that DVIO outperforms state-of-the-art visual-inertial odometries in terms of both the accuracy and the robustness. Thus, DVIO is highly applicable to the navigation or the simultaneous localization and mapping of mobile devices or agile robots like micro air vehicles.
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