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This research aims to improve autonomous navigation of coal mine rescue and detection robot, eliminate the danger for rescuers, and enhance the security of rescue work. The concept of model predictive control is introduced into path planning of rescue and detection robot in this paper. Sampling-Based Model Predictive Control (SBMPC) algorithm is proposed basing on the construction of cost function and predictive kinematics model. Firstly, input sampling is conducted in control variable space of robot motion in order to generate candidate path planning solutions. Then, robot attitude and position in future time, which are regarded as output variables of robot motion, can be calculated through predictive kinematics model and input sampling data. The optimum solution of path planning is obtained from candidate solutions through continuous moving optimization of the defined cost function. The effects of the three sampling methods (viz., uniform sampling, Halton’s sampling, and CVT  sampling) on path planning performance are compared in simulations. Statistical analysis demonstrates that CVT sampling has the most uniform coverage in two-dimensional plane when sample amount is the same for three methods. Simulation results show that SBMPC algorithm is effective and feasible to plan a secure route for rescue and detection robot under complex environment.

In the case of Autonomous Underwater Vehicle (AUV) navigating with low speed near water surface, a new method for design of roll motion controller is proposed in order to restrain wave disturbance effectively and improve roll stabilizing performance. Robust control is applied, which is based on uncertain nonlinear horizontal motion model of AUV and the principle of zero speed fin stabilizer. Feedback linearization approach is used to transform the complex nonlinear system into a comparatively simple linear system. For parameter uncertainty of motion model, the controller is designed with mixed-sensitivity method based on H-infinity robust control theory. Simulation results show better robustness improved by this control method for roll stabilizing of AUV navigating near water surface.

Skin cutaneous melanoma (SKCM) is the most lethal form of skin cancers owing to high invasiveness and high metastatic potential. Tumor microenvironment (TME) provides powerful evidences for discerning SKCM, raising the prospect to identify biomarkers of SKCM. Based on the transcriptome profiles of patients with SKCM and the corresponding clinical information from The Cancer Genome Atlas (TCGA), we used ESTIMATE algorithm to calculate ImmuneScore and StromalScore and identified the TME-Related differentially expressed genes (DEGs), than the intersected TME-Related DEGs were used for subsequent functional enrichment analysis. Protein–protein interaction (PPI) analysis was used to identify the functionality-related DEGs and univariate Cox regression analysis was used to identify the survival-related DEGs. Furthermore, SKCM-related DEGs were identified based on two Gene Expression Omnibus (GEO) datasets. Finally, we intersected functionality-related DEGs, survival-related DEGs, and SKCM-related DEGs, ascertaining that six DEGs (CCL4, CXCL10, CCL5, GZMB, C1QA, and C1QB) function as core TME-related genes (CTRGs). Significant differences of GZMB, C1QA, and C1QB expressions were found in gender and clinicopathologic staging of SKCM. High levels of GZMB, C1QA, and C1QB expressions were associated with favorable prognosis. Gene set enrichment analysis (GSEA) showed that cell–cell interaction, cell behavior, and intracellular signaling transduction may be mainly involved in both C1QA, C1QB and GZMB expressions and metabolism of phospholipid and amino acid, transcription, and translation may be implicated in low GZMB expressions. C1QA, C1QB, and GZMB are novel SKCM-relating CTRGs, providing promising immune-related prognostic biomarkers for SKCM.

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