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New types of aerial robots (NTARs) have found extensive applications in the military, civilian contexts, scientific research, disaster management, and various other domains. Compared with traditional aerial robots, NTARs exhibit a broader range of morphological diversity, locomotion capabilities, and enhanced operational capacities. Therefore, this study defines aerial robots with the four characteristics of morphability, biomimicry, multi-modal locomotion, and manipulator attachment as NTARs. Subsequently, this paper discusses the latest research progress in the materials and manufacturing technology, actuation technology, and perception and control technology of NTARs. Thereafter, the research status of NTAR systems is summarized, focusing on the frontier development and application cases of flapping-wing micro-air vehicles, perching aerial robots, amphibious robots, and operational aerial robots. Finally, the main challenges presented by NTARs in terms of energy, materials, and perception are analyzed, and the future development trends of NTARs are summarized in terms of size and endurance, mechatronics, and complex scenarios, providing a reference direction for the follow-up exploration of NTARs.

Recently, testing and using micro-unmanned vehicles, such as unmanned aerial vehicles (UAVs), in scenarios such as supply transportation, agricultural management, and military operations have become more common. It is no longer sufficient to control a single UAV to accomplish all missions. With the increasing complexities associated with operating and task requirements, an unmanned swarm requires a series of algorithms with higher efficiency, greater generalization ability, and better adaptability than the earlier algorithms. A combination of unmanned swarms with artificial intelligence is becoming a common solution to manage the above requirements. Deep reinforcement learning (DRL) is a machine learning method that combines deep learning (DL) and reinforcement learning (RL); therefore, this method has the advantages of DL and RL. Using an RL method, an agent can learn from the environment by trial and error and make decisions that autonomously obtain high scores. However, when the given environment is complex, the decision function of the agent may be too difficult to implement and then the agent cannot make the correct decision. The DL method has strong fitting ability. A suitable deep neural network can simulate any linear or nonlinear function. If the DL method is used to simulate the decision function in RL, the hybrid method can solve the problem that an agent cannot solve and make a correct decision in a complex environment. The combination of an unmanned swarm and a DRL method has been widely studied. This paper introduces the concept of DRL from the perspective of principles and characteristics. This paper analyzes several typical DRL algorithms, discusses the various control requirements of a UAV swarm, and then focuses on the achievements of combining DRL and a UAV swarm control. Finally, this paper presents viewpoints on the application prospects and challenges related to landing and transformation in the combination field. The concept of an unmanned swarm originated from the study of the behavior of biological groups. Several species of bees, ants, birds, fish, and other creatures exhibit complex group behaviors. These clusters comprise many independent individuals in accordance with certain aggregation rules to form a coordinated, orderly group movement mechanism. Similar to biological clusters, in the field of robotics or UAVs, unmanned swarm systems are crowded intelligent systems. These systems consist of multiple homogeneous or heterogeneous unmanned equipment to achieve mutual behavior coordination and jointly complete specific tasks through interactive feedback and incentive response of information. In practical applications, an unmanned swarm system needs to meet the requirements of an open environment, a changeable situation, limited resources, and real-time responses. This system needs to have multicore collaborative capabilities such as distributed collaborative perception, intelligent collaborative decision-making, and robust collaborative control. The distributed intelligent collaborative control method based on DRL can fully meet the control requirements of high intelligence and robustness of unmanned cluster systems.

Abstract At present, the multi‐object tracking method based on transformer generally uses its powerful self‐attention mechanism and global modelling ability to improve the accuracy of object tracking. However, most existing methods excessively rely on hardware devices, leading to an inconsistency between accuracy and speed in practical applications. Therefore, a lightweight transformer joint position awareness algorithm is proposed to solve the above problems. Firstly, a joint attention module to enhance the ShuffleNet V2 network is proposed. This module comprises the spatio‐temporal pyramid module and the convolutional block attention module. The spatio‐temporal pyramid module fuses multi‐scale features to capture information on different spatial and temporal scales. The convolutional block attention module aggregates channel and spatial dimension information to enhance the representation ability of the model. Then, a position encoding generator module and a dynamic template update strategy are proposed to solve the occlusion. Group convolution is adopted in the input sequence through position encoding generator module, with each convolution group responsible for handling the relative positional relationships of a specific range. In order to improve the reliability of the template, dynamic template update strategy is used to update the template at the appropriate time. The effectiveness of the approach is validated on the MOT16, MOT17, and MOT20 datasets.

Paclitaxel, a potent anti-tumor drug widely recognized for its therapeutic efficacy, has faced limitations in clinical application due to its poor solubility. The use of Cremophor EL (CrEL) as a cosolvent in paclitaxel injections has been associated with hypersensitivity reactions in some patients. To overcome these challenges, we have developed a novel conjugate by linking a neuropilin-1 targeting peptide, RPPR, to paclitaxel, resulting in PTX-RPPR. This innovative approach has significantly enhanced the solubility of paclitaxel, achieving a 3.8 mg/mL concentration, a remarkable 90-fold increase over the native drug. PTX-RPPR has shown potent anti-tumor activity, inhibiting tumor cell proliferation with an IC50 ranging from 0.26 to 1.64 μM and effectively suppressing migration, invasion, and angiogenesis at a concentration of 75 nM. Notably, in a 4T1 mammary carcinoma model, PTX-RPPR administered at a dose of 0.7 μmol/kg exhibited tumor growth inhibition comparable to that of paclitaxel at a higher dose of 3.5 μmol/kg, with superior efficacy in preventing lung metastasis. Furthermore, PTX-RPPR effectively reduced NRP-1 expression in both tumors and lungs post-treatment. In contrast to paclitaxel formulated with CrEL, PTX-RPPR did not induce IL-6 expression, suggesting a safer profile in terms of immunological response. Characterized by a particle size of 200 nm and a zeta potential of +30 mV, the nano-formulation of PTX-RPPR demonstrated remarkable stability over seven days. This study introduced PTX-RPPR as a promising peptide-drug conjugate that addresses the solubility and hypersensitivity issues associated with paclitaxel, offering a safer therapeutic strategy for cancer treatment.

This study proposes an image-based visual servoing (IBVS) method based on a velocity observer for an unmanned aerial vehicle (UAV) for tracking a dynamic target in Global Positioning System (GPS)-denied environments. The proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target. A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is proposed. The depth model ensures image feature accuracy and image trajectory smoothness in rotating target tracking. The relative velocities of the UAV and the dynamic target are estimated using the proposed velocity observer. Thanks to the velocity observer, translational velocity measurements are not required, and the control chatter caused by noise-containing measurements is mitigated. An integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the anti-disturbance ability. The stability of the velocity observer and IBVS controller is analyzed using the Lyapunov method. Comparative simulations and multistage experiments are conducted to illustrate the tracking stability, anti-disturbance ability, and tracking robustness of the proposed method with a dynamic rotating target.

Existing routing protocols without awareness of the topology causes excessive retransmissions, energy holes, and long delay, data routing performance was seriously deteriorated.Considering the relation of topology and routing, an intelligent route planning with jointing topology control (IRPJTC) method was proposed.IRPJTC consisted of two part, the virtual force-based adaptive topology control (VFATC), and the PPO-based geographic routing protocol (PPO-GRP).Based on neighbor’s mobility information, the distance between UAVs was adaptively adjusted by VFATC to provide stable links between UAVs.Combined with link stability metric in VFATC, end-to-end delay and energy consumption, a multi-objective reward function was designed by PPO-GRP to train optimal routing strategy.According to the performance study, the proposed IRPJTC reduces existing routing protocols by 12.11% of end-to-end delay, and 4.56% of energy consumption, and has a better energy balance ability.

In order to meet the demand of the intelligent and efficient picking of fresh citrus fruit in a natural environment, a flexible and independent picking method of fresh citrus fruit based on picking pattern recognition was proposed. The convolutional attention (CA) mechanism was added in the YOLOv7 network model. This makes the model pay more attention to the citrus fruit region, reduces the interference of some redundant information in the background and feature maps, effectively improves the recognition accuracy of the YOLOv7 network model, and reduces the detection error of the hand region. According to the physical parameters of the citrus fruit and stem, an end-effector suitable for picking citrus fruit was designed, which effectively reduced the damage during the picking of citrus fruit. According to the actual distribution of citrus fruits in the natural environment, a citrus fruit-picking task planning model was established, so that the adaptability of the flexible handle can make up for the inaccuracy of the deep learning method to a certain extent when the end-effector picks fruits independently. Finally, on the basis of integrating the key components of the picking robot, a production test was carried out in a standard citrus orchard. The experimental results show that the success rate of the citrus-picking robot arm is 87.15%, and the success rate of picking in the natural field environment is 82.4%, which is better than the success rate of 80% of the market picking robot. In the picking experiment, the main reason for the unsuccessful positioning of citrus fruits is that the position of citrus fruits is beyond the picking range of the end-effector, and the motion parameters of the robot arm joint will produce errors, affecting the motion accuracy of the robot arm, leading to the failure of picking. This study can provide technical support for the exploration and application of the intelligent fruit-picking mode.

The aim of individuals consuming health supplements is to attain a robust state through nutritional regulation. However, some unscrupulous manufacturers, motivated by profit, fraudulently incorporate drugs or unauthorized components with therapeutic effects into the product for instant product performance enhancement. The long-term use of these products may inadvertently inflict harm on human health and fail to promote nutritive healthcare. The illegal inclusion of these substances is prevalent in kidney-tonifying and sexuality-enhancing products. Developing effective analytical methods to identify these products and screen for illegal added ingredients can effectively prevent such products from reaching and remaining on the market. A target screening method for the detection and quantification of 90 phosphodiesterase type 5 inhibitors (PDE-5is) in 5 kinds of health products was developed and validated. The type of dietary supplements varied from tablets, capsules, and protein powder to wine and beverages. Sample preparation was completed with a one-step liquid phase extraction. The screening process of 90 PDE-5is was done efficiently within 25 min by ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) using the dynamic multiple reaction monitoring (dMRM) technique. The LODs of 90 PDE-5is were detected at levels ranging from 25 to 85 ng/g or ng/mL. This novel targeting methodology was effective and can be applied to routine market supervision. Among 286 batches of samples, 8 batches were found to be positive. Three kinds of PDE-5is were first detected in healthy products. The screening method demonstrated herein will be a promising and powerful tool for rapid screening of PDE-5is.

The lack of spatial pose information and the low positioning accuracy of the picking target are the key factors affecting the picking function of citrus-picking robots. In this paper, a new method for automatic citrus fruit harvest is proposed, which uses semantic segmentation and rotating target detection to estimate the pose of a single culture. First, Faster R-CNN is used for grab detection to identify candidate grab frames. At the same time, the semantic segmentation network extracts the contour information of the citrus fruit to be harvested. Then, the capture frame with the highest confidence is selected for each target fruit using the semantic segmentation results, and the rough angle is estimated. The network uses image-processing technology and a camera-imaging model to further segment the mask image of the fruit and its epiphyllous branches and realize the fitting of contour, fruit centroid, and fruit minimum outer rectangular frame and three-dimensional boundary frame. The positional relationship of the citrus fruit to its epiphytic branches was used to estimate the three-dimensional pose of the citrus fruit. The effectiveness of the method was verified through citrus-planting experiments, and then field picking experiments were carried out in the natural environment of orchards. The results showed that the success rate of citrus fruit recognition and positioning was 93.6%, the average attitude estimation angle error was 7.9°, and the success rate of picking was 85.1%. The average picking time is 5.6 s, indicating that the robot can effectively perform intelligent picking operations.

Naturally occurring substances and their derivatives function as vital resources for pesticides that can be used in fields, such as insecticide production and fungicide development. As a botanical entity displaying multifaceted biological functions, wormwood has received thorough scrutiny across multiple sectors. The insect repellency potency combined with antibacterial and antifungal activities of wormwood position it as a potential candidate for prospective development into eco-friendly chemical pesticides. In this research, Wormwood essential oil was procured via ethanol water under ultrasonic scenarios and subsequently diluted with PEG 400 to formulate green chemical pesticides. The defensive efficacy of this green pesticide on plants was validated through 2 weeks of clustered plant growth experiments. Active constituents that exerted their effects were scrutinized by GC-MS. Furthermore, this green pesticide also displays efficacious effects on the prevention and management of aphids, exhibiting a dose-dependent relationship. 4-terpenol, eucalyptol, carvacrol, and L-borneol were identified by GC-MS as the predominant active constituents in this green chemical pesticide. Wormwood can be leveraged to develop green chemical pesticides, which can protect plants without contaminating the environment.

This paper investigates the H∞ consensus problem of discrete-time Markov jump fractional-order multiagent systems (DTMJFOMASs) under denial-of-service (DoS) attacks. By applying the short-memory principle, we can obtain discrete-time Markov jump multiagent systems with partially unknown probabilities. A novel quantized event-triggering mechanism (QETM), based on a mode-dependent logarithmic quantizer, is proposed to enhance transmission efficiency among multiagents. A distributed controller with quantized output is developed. Sufficient conditions are provided to ensure the system achieves H∞ consensus through Lyapunov stability theory. Finally, two examples are given to verify the effectiveness of the proposed model.

The autonomous shape decision-making problem of a morphing aircraft (MA) with a variable wingspan and sweep angle is studied in this paper. Considering the continuity of state space and action space, a more practical autonomous decision-making algorithm framework of MA is designed based on the deep deterministic policy gradient (DDPG) algorithm. Furthermore, the DDPG with a task classifier (DDPGwTC) algorithm is proposed in combination with the long short-term memory (LSTM) network to improve the convergence speed of the algorithm. The simulation results show that the shape decision-making algorithm based on the DDPGwTC enables MA to adopt the optimal morphing strategy in different task environments with higher autonomy and environmental adaptability, which verifies the effectiveness of the proposed algorithm.

This paper addresses the trajectory tracking control problem of a quadrotor under uncertain model parameters, external disturbances, and unmeasurable linear velocity. The main contribution of this paper is to present a geometric control strategy with a simple structure and easily adjustable parameters, which combines the differentiator and uncertainty and disturbance estimator techniques to achieve global trajectory tracking control. First, a cascade control framework is introduced to solve the strong coupling and under actuated problems of quadrotors. Furthermore, an anti disturbance outer-loop differentiator-based position controller is designed based on the uncertainty and disturbance estimators (UDE) to achieve accurate quadrotor tracking of the desired trajectory. The higher order control inputs of the translational dynamics are estimated using a differentiator to determine the desired angular velocity and its derivative. Then, a robust inner-loop geometric controller is derived in Lie algebraic space using the rotation matrix. After that, the stability analysis of the closed-loop system is executed using the Lyapunov stability theory. Finally, the numerical simulation verifies that the method enables the quadrotor to have strong robustness and excellent tracking capability in the presence of large initial angles, time-varying disturbances, and unmeasured linear velocity. [ABSTRACT FROM AUTHOR]