Path planning is the focus and difficulty of research in the field of mobile robots, and it is the basis for further research and applications of robots. In order to obtain the global optimal path of the mobile robot, an improved moth-flame optimization (IMFO) algorithm is proposed in this paper. The IMFO features the following two improvement. Firstly, referring to the spotted hyena optimization (SHO) algorithm, the concept of historical best flame average is introduced to improve the moth-flame optimization (MFO) algorithm update law to increase the ability of the algorithm to jump out of the local optimum; Secondly, the quasi-opposition-based learning (QOBL) is used to perturb the location, increase the population diversity and improve the convergence rate of the algorithm. In order to evaluate the performance of the proposed algorithm, the IMFO algorithm is compared with three existing algorithms on three groups of different types of benchmark functions. The comparative results show that the IMFO algorithm is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Finally, the IMFO algorithm is applied to the path planning of the mobile robot, and computer simulations confirmed the algorithm’s effectiveness.

To solve the problem of automatic recharging path planning for cleaning robots in complex industrial environments, this paper proposes two environmental path planning types based on designated charging location and multiple charging locations. First, we use the improved Maklink graph to plan the complex environment; then, we use the Dijkstra algorithm to plan the global path to reduce the complex two-dimensional path planning to one dimension; finally, we use the improved fruit fly optimization algorithm (IFOA) to adjust the path nodes for shorting the path length. Simulation experiments show that the effectiveness of using this path planning method in a complex industrial environment enables the cleaning robot to select a designated location or the nearest charging location to recharge when the power is limited. The proposed improved algorithm has the characteristics of a small amount of calculation, high precision, and fast convergence speed.

In combination with gene expression profiles, the protein interaction network (PIN) constructs a dynamic network that includes multiple functional modules. Previous studies have demonstrated that rifampin can influence drug metabolism by regulating drug-metabolizing enzymes, transporters, and microRNAs (miRNAs). Rifampin induces gene expression, at least in part, by activating the pregnane X receptor (PXR), which induces gene expression; however, the impact of rifampin on global gene regulation has not been examined under the molecular network frameworks. In this study, we extracted rifampin-induced significant differentially expressed genes (SDG) based on the gene expression profile. By integrating the SDG and human protein interaction network (HPIN), we constructed the rifampin-regulated protein interaction network (RrPIN). Based on gene expression measurements, we extracted a subnetwork that showed enriched changes in molecular activity. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we identified the crucial rifampin-regulated biological pathways and associated genes. In addition, genes targeted by miRNAs that were significantly differentially expressed in the miRNA expression profile were extracted based on the miRNA-gene prediction tools. The miRNA-regulated PIN was further constructed using associated genes and miRNAs. For each miRNA, we further evaluated the potential impact by the gene interaction network using pathway analysis. We extracted the functional modules, which included 84 genes and 89 interactions, from the RrPIN, and identified 19 key rifampin-response genes that are associated with seven function pathways that include drug response and metabolism, and cancer pathways; many of the pathways were supported by previous studies. In addition, we identified that a set of 6 genes (CAV1, CREBBP, SMAD3, TRAF2, KBKG, and THBS1) functioning as gene hubs in the subnetworks that are regulated by rifampin. It is also suggested that 12 differentially expressed miRNAs were associated with 6 biological pathways. Our results suggest that rifampin contributes to changes in the expression of genes by regulating key molecules in the protein interaction networks. This study offers valuable insights into rifampin-induced biological mechanisms at the level of miRNAs, genes and proteins.

Since the complexity and variety of indoor environments, the ability of simultaneous localization and mapping for autonomous mobile robots restricted their applications. A novel approach, which is based on clustering algorithm, fuzzy logic and neural networks, is proposed, and solve simultaneous mapping and localization problem. It adopts self-organizing fuzzy neural networks to model the environment and to implement localization. The neural map can be built on-line automatically. Meanwhile, the neural network exports robotpsilas pose information.

There is a lot of applications for multi-robot systems instead of single robot systems. Therefore, coordination algorithms have become popular in the field of robotics for two decades. This paper analyzes and summarizes the current research status of coordinated algorithms for multi-robot systems, from perspectives of partitioning the environment, and path planning.