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Abstract Comprehending the phylogeography of invasive organisms enhances our insight into their distribution dynamics, which is instrumental for the development of effective prevention and management strategies. In China, Pomacea canaliculata and Pomacea maculata are the two most widespread and damaging species of the non-native Pomacea spp.. Given this species’ rapid spread throughout country, it is urgent to investigate the genetic diversity and structure of its different geographic populations, a task undertaken in the current study using the COI and ITS1 mitochondrial and ribosomal DNA genes, respectively. The result of this study, based on a nationwide systematic survey, a collection of Pomacea spp., and the identification of cryptic species, showed that there is a degree of genetic diversity and differentiation in P. canaliculata, and that all of its variations are mainly due to differences between individuals within different geographical populations. Indeed, this species contains multiple haplotypes, but none of them form a systematic geographical population structure. Furthermore, the COI gene exhibits higher genetic diversity than the ITS1 gene. Our study further clarifies the invasive pathways and dispersal patterns of P. canaliculata in China to provide a theoretical basis.

For mobile robots working in the power inspection environment, navigation algorithms and collaborative path planning are the keys to understanding and reproducing human power inspection’s thinking mode. Firstly, the paper proposed a hybrid navigation algorithm of reflector and laser based on the generalized inverse of the least squares solution. Secondly, to solve the problem of multi-robot inspection cooperative path planning, the traffic rule reservation table generated a dynamic weighted map representing traffic congestion in power inspection to prevent multi-robot collision. Thirdly, through the distributed control method, the robot used the dynamic weighted map to improve the RRT (Rapidly-Exploring Random Trees)algorithm to achieve the purpose of active cooperative path planning, reducing the trajectory’s critical path points and solving the cooperative congestion. Finally, the positioning and calibration compensation can be carried out through the experiment by the reflector navigation algorithm in a large-scale complex environment. Comparing the path planning methods of the A* algorithm and the dynamic weighted RRT algorithm, the dynamic weighted RRT algorithm reduces the number of extended nodes. It solves the multi-robot planning collision problem while ensuring the search path’s completeness. Meanwhile, the dynamic weighted RRT algorithm can save the running time of the multi-robot system and effectively improve the efficiency of the power inspection system.

This paper proposes a solution to the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Second, the concept of voxels is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph-building and loop-closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.

Since the wireless sensor network (WSN) has the performance of sensing, processing, and communicating, it has been widely used in various environments. The node localization is a key technology for WSN. The accuracy localization results can be achieved in ideal environment. However, the measurement may be contaminated by NLOS errors in rough environment. The NLOS errors could result in big localization error. To overcome this problem, we present a mobile node localization algorithm using TDOA and RSS measurements. The proposed method is based on Kalman framework and utilizes the general likelihood ratio method to identify the propagation condition. Then the modified variational Bayesian approximation adaptive Kalman filtering is used to mitigate the NLOS error. It could estimate the mean and variance of measurement error. The simulation results demonstrate that the proposed method outperforms the other methods such as Kalman filter and H ∞ filter.

The dynamic localization is a kind of technology by which the mobile robot tries to localize the position by itself. According to the dynamic localization failure of mobile robots in indoor network blind areas, an autonomous-dynamic localization system which dynamically chooses beacon node and establishes grids is proposed in this paper. This method applies received signal strength indication (RSSI) for distance measurement. Furthermore, the proposed grid-based improved maximum likelihood estimation (GIMLE) fulfills the localization. Finally, the localization error correction is implemented by Kalman filter. The approach combines the classical Kalman filter with the other localization algorithms. The purpose is to smooth and optimize the results of the algorithms, in order to improve the localization accuracy. In particular, in network blind spots, the Kalman filter provides better performance than the other algorithms listed in the paper. Experimental results show the accuracy, adaptivity, and robustness of the dynamic self-localization of mobile robots.

In this paper, we propose a novel approach to mini-UAV localization in a wireless sensor network. We firstly employ the environment adaptive RSS parameters' estimation method to estimate the parameters of range estimation model. However, the direct path from the target to a beacon is blocked by obstacles in a complicated indoor environment. So the proposed method, which employs a sequential probability ratio test to identify whether the measurement contains non-line of sight (NLOS) errors, is tolerant to parameter fluctuations. Finally, a particle swarm optimization-based method is proposed to solve the established objective function. Simulation results show that the proposed method achieved relatively higher localization accuracy. In addition, the performance analyses, carried out for a realistic indoor environment, shows that the proposed method still preserves the same localization accuracy.

This study is concerned with dynamic output feedback (DOF) H∞ control for continuous-time networked control systems (NCSs). The packet dropouts and network-induced delays in the sensor-to-controller channel, and network-induced delays in the controller-to-actuator channel are taken into account. A linear estimation-based method is proposed to estimate the measurement output, and the non-uniform distribution characteristic of the measurement output arrival instant is taken into full consideration to establish a new model for the considered NCSs. Based on the newly established model and a new Lyapunov functional, the problem of DOF controller design is studied. A numerical example is given to illustrate the effectiveness of the proposed DOF controller design. [ABSTRACT FROM AUTHOR]

This study is concerned with the problem of dual communication channels sharing-based network-induced delay and packet dropout compensation for continuous-time networked control systems (NCSs). By introducing the dual communication channels sharing-based compensation method, a new closed-loop model for continuous-time NCSs, which can receive more than one control input during a control input updating time interval, is established. Based on the newly established model and a new Lyapunov functional, an effective controller design criterion is derived to optimise the H1 performance of the considered systems. Even for NCSs without considering network-induced delay and packet dropout compensation, the newly derived controller design method is also applicable. Two numerical examples are given to illustrate the merits and effectiveness of the proposed compensation method. [ABSTRACT FROM AUTHOR]

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption. [ABSTRACT FROM AUTHOR]