Xuefeng Dai

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.

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.

Summary: To improve time efficiency for structured environment exploration of multi‐robot systems, a novel coordinated strategy based on predicate logic reasoning is proposed in this paper. The strategy assigns the room with maximal utility to the robot with the optimal exploring property, and realizes cooperative exploration by considering the room capacity. A set of predicate logic reasoning formulas is established to implement the coordination. The coordination takes into account robot performance and environment property and is conducted in a distributed manner. In addition, to save computing and storage resources, a triple set named as task information was proposed instead of traditional unfinished job list for environment information sharing among robots. The results are validated by computer simulations for two simulated office like environments.

SUMMARY: Exploration is a fundamental problem in robotics, and multi-robot systems exploration has been extensively studied in this field. In order to overcome the problem of a non-optimal target being selected in the exploration process, a revised single linkage clustering frontier cell (CFC) algorithm is proposed to calculate the exploration benefit of all available frontier cells. Moreover, there exist unexplored islands for most of the bidding-based multi-robot coordination algorithms in the exploration of unknown environments. To deal with this problem, some rules switched by emotion states are proposed. So, the proposed bidding coordination algorithm with CFC and an emotion switch has a hierarchical architecture. The upper level is modeled as an automaton, which is used to represent emotion status, and the emotion variables decide whether a robot will participate in a bid and explore an unknown area abiding by the walking rules. In the lower level, the robots perform bidding activities with CFC and the walking rules according to the emotion variables. We tested and evaluated our approach by means of experiments both in a simulated environment and with real robots. The experiments results demonstrate that the exploration efficiency is improved, and our algorithm has a greater coverage rate than classic bidding-based coordination algorithms.

To realize efficient exploration, a novel coordinated algorithm which integrated planned and reactive behaviors is proposed in this paper. The planned behaviors realize dispersion of robots over an environment, cooperation among robots and task assignments to robots. The behaviors are discussed from a case that the robot team size equals four in a kind of obstacle free environments. Afterwards, the behaviors are used to coordinate robot team with any size by utilizing a concept of compound robot. Reactive behaviors are used to deal with appearance of different outline obstacles in a kind of sparsely occupied environments. Then, a finite state machine is utilized to integrate these behaviors. For the two kinds of environments, robots conduct a set of zigzag and distorted zigzag trajectories, respectively. Finally, the results are validated by computer simulations.

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.

Exploration is a fundamental research problem in robotics, andmulti-robot exploration has been widely studied in this field.

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.