Xin Li

To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the AUVs by the improved SOM neural network method based on workload balance and neighborhood function. When there exists kinematic constraints or obstacles which may cause failure of trajectory planning, task re-assignment will be implemented by changing the weights of SOM neurals, until the AUVs can have paths to reach all the targets. Then, the Dubins paths are generated in several limited cases. The AUV’s yaw angle is limited, which results in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realize the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for a multi-AUV system.

Delayed graft function (DGF) is one of the major obstacles for graft survival for kidney recipients. It is profound to reduce the incidence of DGF for maintaining long-term graft survival. However, the molecular regulation of DGF is still not adequately explained and the biomarkers for DGF are limited. Exosomes are cell-derived membrane vesicles, contents of which are stable and could be delivered into recipient cells to exert their biological functions. Consequently, exosome-derived proteomic and RNA signature profiles are often used to account for the molecular regulation of diseases or reflect the conditional state of their tissue as biomarkers. Few researches have been done to demonstrate the function of exosomes associated with DGF. In this study, high-throughput sequencing was used to explore the miRNA expression profiling of exosomes in the peripheral blood of kidney recipients with DGF. We identified 52 known and 5 conserved exosomal miRNAs specifically expressed in recipients with DGF. Three coexpressed miRNAs, hsa-miR-33a-5p_R-1, hsa-miR-98-5p, and hsa-miR-151a-5p, were observed to be significantly upregulated in kidney recipients with DGF. Moreover, hsa-miR-151a-5p was positively correlated with the first-week serum CR, BUN, and UA levels of the kidney recipients after transplantation. Furthermore, we also analyzed functions and signaling pathways of the three upregulated miRNAs target genes to uncover putative mechanism of how these exosomal miRNAs functioned in DGF. Overall, these findings identified biomarker candidates for DGF and provided new insights into the important role of the exosomal miRNAs regulation in DGF.

Task assignment is an important research topic in multiple AUV (Autonomous Underwater Vehicle) cooperative working system. However, many studies concentrate on minimizing total distance of AUVs serving targets at different locations, and mostly do not pay attention to workload balance among inhomogeneous AUVs. What is more, most of them do not think of the effect of ocean current while distributing tasks. To solve these problems, a novel dual competition strategy based on SOM (self-organizing map) neural network is put forward. An AUV makes use of surplus sailing distance to a target when it competes with others for engaging the target. In order to fulfill a balanced task assignment among AUVs, a task balance coefficient is also proposed. Meanwhile, a hybrid path planning approach is applied to guide AUVs to reach their targets safely. The good performance of the proposed algorithm for distributing tasks among AUVs is demonstrated through simulation studies. From the comparison study of SOM algorithm, Hungarian algorithm, k-means algorithm and the proposed dual competition strategy, it can be found that the task assignment with the proposed strategy is more rational and fair.

For the complete coverage path planning of Autonomous Underwater Vehicles (AUVs), a new strategy with GBNN algorithm (Glasius Bio-inspired Neural Network) with discrete and centralized programming is proposed. The basic modelling for multi-AUVs complete coverage problem based on grid map and neural network is discussed first. Then, the design for single AUV complete coverage is introduced based on GBNN algorithm which is a new developed tool with small amount of calculation and high efficiency. In order to solve the difficulty of single AUV full coverage task of large water range, the multi-AUV full coverage discrete and centralized programming is proposed based on GBNN algorithm. The simulation experiment is conducted to confirm that through the proposed algorithm, multi-AUVs can plan reasonable and collision-free coverage path and reach full coverage on the same task area with division of labor and cooperation.

An adaptive self-organizing map (SOM) neural network method is proposed for distributed formation control of a group of autonomous underwater vehicles (AUVs). This method controls the AUVs holding their positions in the formation when the formation moves as a whole. The group of AUVs can reach the desired locations in an expected formation shape along preplanned trajectories. The proposed control law is distributed in the sense that the controller of each AUV only uses its own information and limited information of its neighboring AUVs. Formation-control strategies based on self-organizing competitive calculations are carried out with workload balance taken into consideration, so that a group of AUVs can reach the desired locations on the premise of workload balance and energy sufficiency. Moreover, the formation can avoid obstacles and change its shape as needed. The formation is in a distributed leader-follower-like structure, but there is no need to designate the leader and the followers explicitly. All the AUVs in the formation are treated equal to be the leader and the followers, so that important characteristics such as adaption and fault tolerance are achieved. Comparison results with traditional methods and experiments demonstrate the effectiveness of the proposed method.