In the era of humanoid robotics, navigation and path planning of humanoids in complex environments have always remained as one of the most promising area of research. In this paper, a novel hybridized navigational controller is proposed using the logic of both classical technique and computational intelligence for path planning of humanoids. The proposed navigational controller is a hybridization of regression analysis with adaptive particle swarm optimization. The inputs given to the regression controller are in the forms of obstacle distances, and the output of the regression controller is interim turning angle. The output interim turning angle is again fed to the adaptive particle swarm optimization controller along with other inputs. The output of the adaptive particle swarm optimization controller termed as final turning angle acts as the directing factor for smooth navigation of humanoids in a complex environment. The proposed navigational controller is tested for single as well as multiple humanoids in both simulation and experimental environments. The results obtained from both the environments are compared against each other, and a good agreement between them is observed. Finally, the proposed hybridization technique is also tested against other existing navigational approaches for validation of better efficiency.

At present, with the increase of production capacity and the promotion of production, the reserves

of most mining enterprises under the original industrial indexes are rapidly consumed, and the full

use of low-grade resources is getting more and more attention. If mining enterprises want to make

full use of low-grade resources simultaneously and obtain good economic benefits to strengthening

the analysis and management of costs is necessary. For metal underground mines, with the gradual

implementation of exploration and mining projects, capital investment and labor consumption are

dynamic and increase cumulatively in stages. Consequently, in the evaluation of ore value, we should

proceed from a series of processes such as: exploration, mining, processing and the smelting of

geological resources, and then study the resources increment in different stages of production and the

processing. To achieve a phased assessment of the ore value and fine evaluation of the cost, based on

the value chain theory and referring to the modeling method of computer integrated manufacturing

open system architecture (CIMOSA), the analysis framework of gold mining enterprise value chain is

established based on the value chain theory from the three dimensions of value-added activities, value

subjects and value carriers. A value chain model using ore flow as the carrying body is built based on

Petri nets. With the CPN Tools emulation tool, the cycle simulation of the model is carry out by the

colored Petri nets, which contain a hierarchical structure. Taking a large-scale gold mining enterprise

as an example, the value chain model is quantified to simulate the ore value formation, flow, transmission

and implementation process. By analyzing the results of the simulation, the ore value at different

production stages is evaluated dynamically, and the cost is similarly analyzed in stages, which can improve mining enterprise cost management, promote the application of computer modeling and

simulation technology in mine engineering, more accurately evaluate the economic feasibility of ore

utilization, and provide the basis for the value evaluation and effective utilization of low-grade ores.

Long-duration human space missions require intelligent regenerative life support systems that can recycle resources and automatically manage failures. This paper explores using Petri nets to model the reliability and complex interactions of such closed-loop systems. An architecture consisting of primary systems, backups, and consumable reserves is outlined. The automation system that controls everything is described. Petri nets can capture concurrency, failure modes, redundancy, and dynamic behavior. A modular modeling methodology is presented to develop hierarchical Petri net models that scale in fidelity. Elementary fragments represent failures and redundancy. Subsystem modules can be substituted for more detailed models. Analysis and simulation assess system reliability and failure response. This supports designing ultra-reliable systems to safely sustain human life in space.

The article presents the results of the simulation studies concerning the impact of random production interruptions on the efficiency of multi-spindle machining centers. Four different machining center configuration models were developed using a dedicated class of stochastic Petri nets. In addition to the number of machine spindles, the number of simultaneously mounted parts, loading time of parts, their machining time, and reliability parameters regarding the frequency of machine interruptions caused by random factors were also taken into account as model parameters. A series of virtual tests was carried out for machining processes over a period of 1000 hours of operation. Analysis of the results confirmed the purpose of conducting simulation tests prior to making a decision regarding the purchase of a multispindle milling center. This work fills the existing research gap, as there are no examples in the technical literature of evaluating the effectiveness of multi-spindle machining centers.

In the era of smart manufacturing and Industry 4.0, the rapid development of modelling in production processes results in the implementation of new techniques, such as additive manufacturing (AM) technologies. However, large invest-ments in the devices in the field of AM technologies require prior analysis to identify the possibilities of improving the production process flow. This paper proposes a new approach to determine and optimize the production process flow with improvements made by the AM technologies through the application of the Petri net theory. The existing produc-tion process is specified by a Petri net model and optimized by AM technology. The modified version of the system is verified and validated by the set of analytic methods safeguarding against the formal errors, deadlocks, or unreachable states. The proposed idea is illustrated by an example of a real-life production process.

Modeling and simulation are key performance analysis and control techniques to optimize decision-making as well as design and operate complex production systems. They are also indicated as one of the technological pillars of modern industry and IT solutions supporting the implementation of the roadmap toward Industry 4.0 in the areas of digital transformation and automation. In the context of the required rapid transformation of today’s enterprises, it becomes extremely important to look for solutions that allow the use of the existing infrastructure, information, and energy, so as to minimize the negative impact of new technologies and the transformation process itself on the environment. The article presents an approach to modeling large and complex production systems with the use of distributed Petri net models allowing the use of the possessed IT infrastructure as consistent with the idea of sustainable development in the activities of enterprises. This eliminates two major problems that render traditional models unusable. The first is related to the difficulties in analyzing and verifying models of enormous size and infinite space of states. The second is related to the required computing power, if such analyzes are to be performed on one computing unit, which would force the producers to replace the IT infrastructure. For this purpose, modular Petri nets are introduced. Other benefits of modularization, such as smaller components that can be independently analyzed, are also presented in the paper. The proposed modular Petri net has been implemented in the proprietary GPenSIM software. The paper is complemented by a practical example of industrial modeling of production systems with automated guided vehicles (AGVs) using the Modular Model with Intelligent Petri Modules.