This article investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties. First, a novel signal selection method based on event trigger is developed to handle network-induced packet dropouts, as well as packet disorders resulting from random transmission delays, where the ${H_{2}}/{H_{\infty } }$ performance of the system is analyzed in different noise environments. In addition, a linear delay compensation strategy is further employed for solving the complex network-induced problem, which may deteriorate system performance. Moreover, a weighted fusion scheme is used to integrate multiple resources through an error cross-covariance matrix. Several case studies validate the proposed algorithm and demonstrate satisfactory system performance in target tracking.

The dynamic positioning system of unmanned underwater vehicles (UUVs) is a complex and large-scale system mainly due to the nonlinear dynamics, uncertainty in model parameters, and external disturbances. With the aid of the bio-inspired computing (BIC) method, the designed three-dimensional (3D) spatial positioning system is used for enlarging communication constraints and increasing signal coordination processing. With the growing of measurement scales, the issue of the networked high-precision positioning has been developed rapidly. Then, an information fusion estimation approach is presented for the distributed networked systems with data random transmission time delays and lost and disordered packets. To reduce the communication burden, an adaptive signal selection scheme is employed to reorganize the measurement sequence, and the parameter uncertainties as well as cross-correlated noise are used to describe the uncertain disturbances. Moreover, a reoptimal weighted fusion state estimation is designed to alleviate the information redundancy and maintain higher measurement accuracy. An illustrative example obtained from the 3D spatial positioning system is given to validate the effectiveness of the proposed method.

This paper presents a new approach to filter signals for discrete-time physical problems with stochastic uncertain in the presence of random data transmission delays, out-of-order packets and correlated noise. To deal with the packet disorder, the system model synthesizing the transmission delays and out-of-order packets from the plant to the filter is established by utilizing signal reconstruction schemes based on the zero-order-holder and logic zero-order-holder. A robust finite horizon Kalman filter is proposed by augmenting the state-space model and minimizing the error covariance. To further improve the filtering performance, a linear estimation-based delay compensation strategy is proposed by employing the reorganized time-stamped measurements. Moreover, for solving the missing measurement problem whilst reducing the computational costs, an artificial delay compensation approach is established using an one-step prediction approach. Simulation results show the effectiveness of the proposed method.

To solve the dynamic and real-time problem of multirobot task allocation in intelligent warehouse system under parts-to-picker mode, this paper presents a combined solution based on adaptive task pool strategy and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) algorithm. In the first stage of the solution, a variable task pool is used to store dynamically added tasks, which can dynamically divide continuous and large-scale task allocation problems into small-scale subproblems to solve them to meet dynamic requirements. And an adaptive control strategy is used to automatically adjust the total number of tasks in the task pool to achieve a trade-off among throughput, energy consumption, and waiting time, which has better adaptability than manually adjusting the size of the task pool. In the second stage of the solution, when the task pool is full, tasks in the task pool will be assigned to robots. For the task allocation problem, this paper regards it as an optimization problem and uses the CMA-ES algorithm to find the optimal task assignment solution for all the robots. By comparing with fixed threshold method under 56 different task pool sizes, the experimental results show that the throughput can be close to reaching the optimal level, and the average distance traveled by robots to handle each unit is lower using adaptive threshold method; so, adaptive task pool solution has better adaptability and can find the optimal task pool size by itself. This method can satisfy the dynamic and real-time requirements and can be effectively applied to the intelligent warehouse system.