Underwater nodes are prone to drift under the water flow action, which makes the topological structure of underwater wireless sensor networks (UWSNs) have great uncertainty. It is bound to bring about the occurrence of node nonuniform distribution over time. Given the obstacles and boundaries constrained areas, the intention of this article is to redeploy the drifted underwater nodes for regaining higher coverage rate. Hence, we propose a 3-D virtual force coverage algorithm (3D-VFCA). Inspired by the physical water flow action, the position evolution model of drifted underwater nodes is derived under the continuous gravity, buoyancy, propulsion, and resistance forces, which can reveal the mechanism of UWSNs deformation and coverage holes caused by the underwater node drift. Then, the coverage problem of dense and sparse node distributions is transformed into the optimization problem of weighted distance between node positions and clustering centers in the precoverage process, which can reduce large moving distances caused by node blind movements and solve the problem of inaccurate clustering centers caused by outliers. Furthermore, the improved virtual force algorithm is designed in consideration of underwater nodes, obstacles, and boundaries, which can drive the precovered underwater nodes to more optimal positions based on the adaptive moving distance per step. Finally, coverage performance evaluations of drifted underwater nodes are performed under different coverage algorithms, node numbers, and obstacles. The experimental results indicate that the proposed 3D-VFCA can improve the coverage rates in UWSNs.

The accurate measurement of position and orientation for shearers is a key technology in realizing an automated, fully-mechanized, coal mining face. Since Global Positioning System (GPS) signal cannot arrive at the coal mine underground, wireless sensor network positioning system cannot operate stably in the coal mine; thus a strap-down inertial navigation system (SINS) is used to measure the position and orientation of the shearer. Aiming at the problem of the SINS accumulative error, this paper proposes a positioning error correction method based on the motion constraint-aided SINS zero velocity updated (ZUPT) model. First of all, a stationary state detection model of the shearer is built with median filter based on the acceleration and angular rate measured by the SINS. Secondly, the motion of the shearer is analyzed using coal mining technology, then the motion constraint model of the shearer is established. In addition, the alternate action between the motion constraint model and the ZUPT model is analyzed at the process of movement and cessation of the shearer, respectively; hence, the motion constraint-aided SINS ZUPT model is built. Finally, by means of the experimental platform of the SINS for the shearer, the experimental results show that the maximum position error with the positioning model proposed in this paper is 1.6 m in 180 s, and increases by 92.0% and 88.1% compared with the single motion constraint model and single ZUPT model, respectively. It can then restrain the accumulative error of the SINS effectively.

Multi-AUV cooperative target search problem in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To complete this task, each autonomous underwater vehicle (AUV) needs to move quickly without collision and cooperate with other AUVs to find the target. In this paper, an improved dolphin swarm algorithm- (DSA-) based approach is proposed, and the search problem is divided into three stages, namely, random cruise, dynamic alliance, and team search. In the proposed approach, the Levy flight method is used to provide a random walk for AUV to detect the target information in the random cruise stage. Then the self-organizing map (SOM) neural network is used to build dynamic alliances in real time. Finally, an improved DSA algorithm is presented to realize the team search. Furthermore, some simulations are conducted, and the results show that the proposed approach is capable of guiding multi-AUVs to achieve the target search task in unknown 3D underwater environment efficiently.

The accurate stable measurement of position for shearer is a key technology in realizing an automated fully-mechanized coal mining face. Using as a base of SINS/UWB integrated positioning system, the signal interference and node damage of UWB often cause invalid integrated positioning system, because of the bad working environment of shearer in the coal mine. Consequently, positioning accuracy of SINS/UWB integrated system decreases quickly, due to the drift error of SINS. Aiming at two invalid styles include partial anchor node failure and all anchor nodes failure of UWB, this paper proposes a stable SINS/UWB integrated positioning system of shearer using the multi-model intelligent switching method based on tightly- coupled integrated model and decision tree fault-tolerant model. First of all, the tightly-coupled integrated model and decision tree fault-tolerant model are built using measured distance and the final position of UWB respectively. Secondly, the multi-model intelligent switching algorithm can be established based on the shearer Markoff movement state and residual threshold judgment of Kalman Filter. Finally, the experimental results show that the multi-model intelligent switching fault-tolerant positioning algorithm can effectively overcome increased positioning error of tightly-coupled model and decision tree model based on these two invalid styles. The maximum position error with the positioning model proposed in this paper is 0.93 m in 327 s, and the positioning accuracy with multi-model intelligent switching model is much greater than that with tightly-coupled integration and the decision tree fault tolerant models.