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Abstract The real-time path planning of unmanned aerial vehicles (UAVs) in dynamic environments with moving threats is a difficult problem. To solve this problem, this paper proposes a time-based rapidly exploring random tree (time-based RRT*) algorithm, called the hierarchical rapidly exploring random tree algorithm based on potential function lazy planning and low-cost optimization (HPO-RRT*). The HPO-RRT* algorithm can guarantee path homotopy optimality and high planning efficiency. This algorithm uses a hierarchical architecture comprising a UAV perception system, path planner, and path optimizer. After the UAV perception system predicts moving threats and updates world information, the path planner obtains the heuristic path. First, the path planner uses the bias sampling method based on the artificial potential field function proposed in this paper to guide sampling to improve the efficiency and quality of sampling. Then, the tree is efficiently extended by the improved time-based lazy collision checking RRT* algorithm to obtain the heuristic path. Finally, a low-cost path optimizer quickly optimizes the heuristic path directly to optimize the path while avoiding additional calculations. Simulation results show that the proposed algorithm outperforms the three existing advanced algorithms in terms of addressing the real-time path-planning problem of UAVs in a dynamic environment.

A critical examination of specimens, with literature and a field survey have shown that Rubus pekinensis is conspecific with R. crataegifolius. Their morphological variations range can be defined as: leaves at the base may be ovate, suborbicular, narrowly ovate, entire, at the middle, ovate, narrowly ovate, oblong-lanceolate, palmately 3-lobed or 5-lobed and at the top, ovate, lanceolate, entire or 3-lobed; flowers solitary in the axillae or several flowers clustered at the terminal of branchlets or formed into short racemes. Therefore, we treat the former species as a synonym for the latter one.

To improve the precision and robustness of Unmanned Aerial Vehicle (UAV) integrated navigation systems, this paper presents an Interacting Multiple Model (IMM) navigation algorithm based on a Robust Cubature Kalman Filter (RCKF) with modified Zero Velocity Update (ZUPT) method assistance. This algorithm has a two-level fusion structure. At the bottom level, the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation model and the Dynamic Zero Velocity Update/Inertial Navigation System (DZUPT/INS) integrated navigation model are established by modifying the Zero Velocity Update (ZUPT) method. Subsequently, the RCKF algorithm adopts a robust factor to weaken the influence of measurement outliers on the filter solution. At the top level, the estimation results of the GPS/INS integrated navigation model and the DZUPT/INS integrated navigation model are fused by the IMM algorithm. In addition to enhancing the robustness of filter estimation in the presence of measurement outliers, the proposed navigation algorithm also corrects navigation errors with ZUPT method assistance. Simulation and experimental analyses demonstrate the performance of the proposed navigation algorithm for UAVs.

In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to perform military missions autonomously. This paper proposes a novel algorithm called flight cost-based Rapidly-exploring Random Tree star (FC-RRT*) extending the standard Rapidly-exploring Random Tree star (RRT*) to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment. First, a flight cost function that includes threat strength and path length was designed to comprehensively evaluate the connection between two path nodes. Second, in order to solve the UAV path planning problem from the front-end, the flight cost function and flight constraints were used to inspire the expansion of new nodes. Third, the designed cost function was used to guide the update of the parent node to allow the algorithm to consider both the threat and the length of the path when generating the path. The simulation and comparison results show that FC-RRT* effectively overcomes the shortcomings of standard RRT*. FC-RRT* is able to plan an optimal path that significantly improves path safety as well as maintains has the shortest distance while satisfying flight constraints in the complex environment. This paper has application value in UAV 3D global path planning.

Carbon nanofiber (CNF) is a nanomaterial with unique mechanical properties, which can improve the properties of composite materials effectively. Research focusses on the impact of CNF on asphalt, asphalt binders, and mixtures. Traditional emulsified asphalt presents a limited performance at both high and low temperatures. Emulsified asphalt with a better performance, is therefore required in engineering. Referring to research on CNF-asphalt, CNF is considered to improve the performance of emulsified asphalt. In this study, a preparation method for CNF modified emulsified asphalt with styrene-butadiene rubber (SBR) was proposed. Ultrasonication and surfactant were utilized to disperse the CNFs in water. The optimum dispersion surfactant percentages and ultrasonic energy density to disperse CNFs were determined through ultraviolet-visible spectra (UV-vis spectra). The modified emulsified asphalt was produced using CNFs suspension with SBR as a modifier, and the properties of the residue, with different percentages of CNFs, were tested. Gel permeation chromatography (GPC) was performed to analyze the molecular size distribution. The results indicated that CNFs significantly improved high-temperature performance of the residue but decreased low-temperature properties. The addition of SBR not only perfected storage stability but also improved low-temperature performance by introducing more small molecules.

Aimed at improving upon the disadvantages of the single centralized Kalman filter for integrated navigation, including its fragile robustness and low solution accuracy, a nonlinear double model based on the improved decentralized federated extended Kalman filter (EKF) for integrated navigation is proposed. The multisensor error model is established and simplified in this paper according to the near-ground short distance navigation applications of small unmanned aerial vehicles (UAVs). In order to overcome the centralized Kalman filter that is used in the linear Gaussian system, the improved federated EKF is designed for multisensor-integrated navigation. Subsequently, because of the navigation requirements of UAVs, especially for the attitude solution accuracy, this paper presents a nonlinear double model that consists of the nonlinear attitude heading reference system (AHRS) model and nonlinear strapdown inertial navigation system (SINS)/GPS-integrated navigation model. Moreover, the common state parameters of the nonlinear double model are optimized by the federated filter to obtain a better attitude. The proposed algorithm is compared with multisensor complementary filtering (MSCF) and multisensor EKF (MSEKF) using collected flight sensors data. The simulation and experimental tests demonstrate that the proposed algorithm has a good robustness and state estimation solution accuracy.

Background: Argonaute, the core component of the RNA induced silencing complex (RISC), binds to mature miRNAs and regulates gene expression at transcriptional or post-transcriptional level. We recently reported that Argonaute 2 (Ago2) also assembles into complexes with miRNA precursors (pre-miRNAs). These Ago2:pre-miRNA complexes are catalytically active in vitro and constitute non-canonical RISCs. Results: The use of pre-miRNAs as guides by Ago2 bypasses Dicer activity and complicates in vitro RISC reconstitution. In this work, we characterized Ago2:pre-miRNA complexes and identified RNAs that are targeted by miRNAs but not their corresponding pre-miRNAs. Using these target RNAs we were able to recapitulate in vitro pre-miRNA processing and canonical RISC loading, and define the minimal factors required for these processes. Conclusions: Our results indicate that Ago2 and Dicer are sufficient for processing and loading of miRNAs into RISC. Furthermore, our studies suggest that Ago2 binds primarily to the 5'- and alternatively, to the 3'-end of select pre-miRNAs. [ABSTRACT FROM AUTHOR]

Small regulatory RNAs are essential and ubiquitous riboregulators that are the key mediators of RNA interference (RNAi). They include microRNAs (miRNAs) and short-interfering RNAs (siRNAs), classes of ∼22 nucleotide RNAs. miRNAs and siRNAs bind to Argonaute proteins and form effector complexes that regulate gene expression; in animals, this regulation occurs primarily at the post-transcriptional level. In this review, we will discuss our current understanding of how miRNA and siRNAs are generated and how they function to silence gene expression, focusing on animal and, in particular, mammalian miRNAs. [ABSTRACT FROM AUTHOR]