Two-layer formation-containment fault-tolerant control of fixed-wing UAV swarm for dynamic target tracking

This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle(UAV)swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs’actuator and sensor.The fixed-wing UAV swarm under consideration is organized as a“multi-leader-multi-follower”structure,in which only several leaders can obtain the dynamic target information while others only receive the neighbors’information through the communication network.To simultaneously realize the formation,containment,and dynamic target tracking,a two-layer control framework is adopted to decouple the problem into two subproblems:reference trajectory generation and trajectory tracking.In the upper layer,a distributed finite-time estimator(DFTE)is proposed to generate each UAV’s reference trajectory in accordance with the control objective.Subsequently,a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer,where a novel adaptive extended super-twisting(AESTW)algorithm with a finite-time extended state observer(FTESO)is involved in solving the robust trajectory tracking control problem under model uncertainties,actuator,and sensor faults.The proposed controller simultaneously guarantees rapidness and enhances the system’s robustness with fewer chattering effects.Finally,corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.

Path Planning of Multi-Axis Robotic Arm Based on Improved RRT*

An improved RRT∗algorithm,referred to as the AGP-RRT∗algorithm,is proposed to address the problems of poor directionality,long generated paths,and slow convergence speed in multi-axis robotic arm path planning.First,an adaptive biased probabilistic sampling strategy is adopted to dynamically adjust the target deviation threshold and optimize the selection of random sampling points and the direction of generating new nodes in order to reduce the search space and improve the search efficiency.Second,a gravitationally adjustable step size strategy is used to guide the search process and dynamically adjust the step-size to accelerate the search speed of the algorithm.Finally,the planning path is processed by pruning,removing redundant points and path smoothing fitting using cubic B-spline curves to improve the flexibility of the robotic arm.Through the six-axis robotic arm path planning simulation experiments on the MATLAB platform,the results show that the AGP-RRT∗algorithm reduces 87.34%in terms of the average running time and 40.39%in terms of the average path cost;Meanwhile,under two sets of complex environments A and B,the average running time of the AGP-RRT∗algorithm is shortened by 94.56%vs.95.37%,and the average path cost is reduced by 55.28%vs.47.82%,which proves the effectiveness of the AGP-RRT∗algorithm in improving the efficiency of multi-axis robotic arm path planning.

Allosteric Mechanism of Calmodulin Revealed by Targeted Molecular Dynamics Simulation

Calmodulin （CAM） is involved in the regulation of a variety of cellular signaling pathways. To accomplish its physiological functions, CaM binds with Ca2＋ at its EF-hand Ca2＋ binding sites which induce the conformational switching of CaM. However, the molecular mechanism by which Ca2＋ binds with CaM and induces conformational switching is still obscure. Here we combine molecular dynamics with targeted molecular dynamics simulation and achieve the state-transition pathway of CaM. Our data show that Ca2＋ binding speeds up the conformational transition of CaM by weakening the interactions which stabilize the closed state. It spends about 6.5 ns and 5.25 ns for transition from closed state to open state for apo and holo CaM, respectively. Regarding the contribution of two EF-hands, our data indicate that the first EF-hand triggers the conformational transition and is followed by the second one. We determine that there are two interaction networks which contribute to stabilize the closed and open states, respectively.

Tumor microenvironment responsive drug delivery systems

Conventional tumor-targeted drug delivery systems(DDSs)face challenges,such as unsatisfied systemic circulation,low targeting efficiency,poor tumoral penetration,and uncontrolled drug release.Recently,tumor cellular molecules-triggered DDSs have aroused great interests in addressing such dilemmas.With the introduction of several additional functionalities,the properties of these smart DDSs including size,surface charge and ligand exposure can response to different tumor microenvironments for a more efficient tumor targeting,and eventually achieve desired drug release for an optimized therapeutic efficiency.This review highlights the recent research progresses on smart tumor environment responsive drug delivery systems for targeted drug delivery.Dynamic targeting strategies and functional moieties sensitive to a variety of tumor cellular stimuli,including pH,glutathione,adenosine-triphosphate,reactive oxygen species,enzyme and inflammatory factors are summarized.Special emphasis of this review is placed on their responsive mechanisms,drug loading models,drawbacks and merits.Several typical multi-stimuli responsive DDSs are listed.And the main challenges and potential future development are discussed.

Vehicle Real-time Location Based on Visual Perception Model

Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.

An algorithm for moving target detection in IR image based on grayscale distribution and kernel function

A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.

Behavior-based Autonomous Navigation and Formation Control of Mobile Robots in Unknown Cluttered Dynamic Environments with Dynamic Target Tracking

While different species in nature have safely solved the problem of navigation in a dynamic environment, this remains a challenging task for researchers around the world. The paper addresses the problem of autonomous navigation in an unknown dynamic environment for a single and a group of three wheeled omnidirectional mobile robots(TWOMRs). The robot has to track a dynamic target while avoiding dynamic obstacles and dynamic walls in an unknown and very dense environment. It adopts a behavior-based controller that consists of four behaviors: "target tracking", "obstacle avoidance", "dynamic wall following" and "avoid robots". The paper considers the problem of kinematic saturation. In addition, it introduces a strategy for predicting the velocity of dynamic obstacles based on two successive measurements of the ultrasonic sensors to calculate the velocity of the obstacle expressed in the sensor frame. Furthermore, the paper proposes a strategy to deal with dynamic walls even when they have U-like or V-like shapes. The approach can also deal with the formation control of a group of robots based on the leader-follower structure and the behavior-based control, where the robots have to get together and maintain a given formation while navigating toward the target, avoiding obstacles and walls in a dynamic environment. The effectiveness of the proposed approaches is demonstrated via simulation.

Dynamically Optimized Sensor Deployment Based on Game Theory

Sensor network deployment is the key for sensors to play an important performance. Based on game theory, first, the authors propose a multi-type sensor target allocation method for the autonomous deployment of sensors, considering exploration cost, target detection value, exploration ability and other factors. Then, aiming at the unfavorable environment, e.g., obstacles and enemy interference, the authors design a method to maintain the connectivity of sensor network, under the conditions of effective detection of the targets. Simulation result shows that the proposed deployment strategy can achieve the dynamic optimization deployment under complex conditions.

Ion dynamics in ultrafast laser ablation of copper target

We investigate the angular distribution and average kinetic energy of ions produced during ultrafast laser ablation （ULA） of a copper target in high vacuum. Laser produced plasma （LPP） is induced by irradiating the target with Ti：Sapphire laser pulses of -50 fs and 800 nm at an angle of incidence of 45°. An ion probe is moved along a circular path around the ablation spot, thereby allowing characterization of the time-of-flight （TOF） of ions at different angles relative to the normal target. The angular distribution of the ion flux is well-described by an adiabatic and isentropic expansion model of a plume produced by solid-target laser ablation （LA）. The angular width of the ion flux becomes narrower with increasing laser fluence. Moreover, the ion average kinetic energy is forward-peaked and shows a stronger dependence on the laser pulse fluence than on the ion flux. Such results can be ascribed to space charge effects that occur during the early stages of LPP formation.

Extraction and application of the low dimensional dynamical component from underwater acoustic target radiating noise

Signal processing in phase space based on nonlinear dynamics theory is a new method for underwater acoustic signal processing. One key problem when analyzing actual acoustic signal in phase space is how to reduce the noise and lower the embedding dimen- sion. In this paper, local-geometric-projection method is applied to obtain fow dimensional element from various target radiating noise and the derived phase portraits show obviously low dimensional attractors. Furthermore, attractor dimension and cross prediction error are used for classification. It concludes that combining these features representing the geometric and dynamical properties respectively shows effects in target classification.