Triboelectric nanogenerators (TENGs) are emerging as a form of sustainable and renewable technology for harvesting wasted mechanical energy in nature, such as motion, waves, wind, and vibrations. TENG devices generate electricity through the cyclic working principle of contact and separation of tribo-material couples. This technology is used in outstanding applications in energy generation, human care, medicinal, biomedical, and industrial applications. TENG devices can be applied in many practical applications, such as portable power, self-powered sensors, electronics, and electric consumption devices. With TENG energy technologies, significant energy issues can be reduced or even solved in the near future, such as reducing gas emissions, increasing environmental protection, and improving human health. The performance of TENGs can be enhanced by utilizing materials with a significant contrast in their triboelectrical characteristics or by implementing advanced structural designs. This review comprehensively examines the recent advancements in TENG technologies for harnessing mechanical waste energy sources, with a primary focus on their sustainability and renewable energy attributes. It also delves into topics such as optimizing tribo-surface structures to enhance output performance, implementing energy storage systems to ensure stable operation and prolonged usage, exploring energy collection systems for efficient management of harvested energy, and highlighting practical applications of TENG in various contexts. The results indicate that TENG technologies have the potential to be widely applied in sustainable energy generation, renewable energy, industry, and human care in the near future.

In recent times, industrial robots have gained immense significance and popularity in various industries. They not only enhance labor safety and reduce costs but also greatly improve productivity and efficiency in the production process. However, selecting the most suitable robot for a specific production process is a complex task. There are numerous criteria to consider, often conflicting with each other, making decision-making challenging. In order to tackle this problem, the multi-criteria decision-making (MCDM) method is employed, which aids in ranking decisions based on criteria weights. However, traditional MCDM methods are now considered outdated, and researchers are concentrating on hybrid models that include multiple MCDM techniques to tackle decision-making problems effectively. This study presents an effective MCDM model that integrates Fuzzy-AHP-TOPSIS to evaluate and choose the best robot. The Fuzzy-AHP is utilized to establish a set of weights for the evaluation criteria. Subsequently, the proposed technique analyzes, prioritizes, and chooses the best robot option from the ranking list for the factory. The experimental results demonstrate that by employing the integrated fuzzy analytical hierarchy process, taking into account parameter weights and expert judgment, the robots are identified in order of best to worst alternatives to factories. The outcomes of this research possess significant implications for robot selection and can be applied in various fields to cater to production requirements.

Renewable energy is the best choice for clean and sustainable energy development. A single renewable energy system reveals an intermittent disadvantage during the energy production process due to the effects of weather, season, day/night, and working environment. A generally hybrid renewable energy system (HRES) is an energy production scheme that is built based on a combination of two or more single renewable energy sources (such as solar energy, wind power, hydropower, thermal energy, and ocean energy) to produce electrical energy for energy consumption, energy storage, or a power transmission line. HRESs feature the outstanding characteristics of enhancing energy conversion efficiency and reducing fluctuations during the energy production process. Triboelectric nanogenerator (TENG) technology transduces wasted mechanical energies into electrical energy. The TENG can harvest renewable energy sources (such as wind, water flow, and ocean energy) into electricity with a sustainable working ability that can be integrated into an HRES for high power efficiency in sustainable renewable energy production. This article reviews the recent techniques and methods using HRESs and triboelectric nanogenerators (TENGs) in advanced hybrid renewable energy systems for improvements in the efficiency of harvesting energy, sustainable energy production, and practical applications. The paper mentions the benefits, challenges, and specific solutions related to the development and utilization of HRESs. The results show that the TENG is a highly potential power source for harvesting energy, renewable energy integration, application, and sustainable energy development. The results are a useful reference source for developing HRES models for practical applications and robust development in the near future.

Smart devices and emerging technologies are highly popular devices and technologies that considerably improve our daily living by reducing or replacing human workforces, treating disease, monitoring healthcare, enhancing service performance, improving quality, and protecting the natural environment, and promoting non-gas emissions, sustainable working, green technologies, and renewable energy. Triboelectric nanogenerators (TENGs) have recently emerged as a type of advanced energy harvesting technology that is simple, green, renewable, flexible, and endurable as an energy resource. High-performance TENGs, denoted as advanced TENGs, have potential for use in many practical applications such as in self-powered sensors and sources, portable electric devices, power grid penetration, monitoring manufacturing processes for quality control, and in medical and healthcare applications that meet the criteria for smart devices and emerging technologies. Advanced TENGs are used as highly efficient energy harvesters that can convert many types of wasted mechanical energy into the electric energy used in a range of practical applications in our daily lives. This article reviews recently advanced TENGs and their potential for use with smart devices and emerging technology applications. The work encourages and strengthens motivation to develop new smart devices and emerging technologies to serve us in many fields of our daily living. When TENGs are introduced into smart devices and emerging technologies, they can be applied in a variety of practical applications such as the food processing industry, information and communication technology, agriculture, construction, transportation, marine technology, the energy sector, mechanical processing, manufacturing, self-powered sensors, Industry 4.0, drug safety, and robotics due to their sustainable and renewable energy, light weight, cost effectiveness, flexibility, and self-powered portable energy sources. Their advantages, disadvantages, and solutions are also discussed for further research.

Selecting the optimal industrial robot is a crucial factor in enhancing production efficiency and reducing operational costs. However, conflicts among evaluation criteria make this process challenging, requiring a structured decision-making approach. The present study proposes a hybrid Multi-Criteria Decision-Making (MCDM) model that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and the Fuzzy Additive Ratio Assessment (FARAS). The model consists of six main steps: constructing the pairwise contribution matrix from experts regarding the relationships between criteria, developing the fuzzy pairwise comparison matrix for weights, determining the fuzzy weights of each criterion, obtaining expert evaluations of the alternatives, normalizing the data, and ranking the alternatives. Industrial robot selection must achieve a balance between performance and cost considerations. High-performance robots offer superior accuracy and reliability but come with high investment costs, whereas low-cost options may not meet technical requirements. The proposed approach utilizes fuzzy set theory to address the uncertainties inherent in expert evaluations. FAHP determines the criterion weights, whereas FARAS ranks robots based on aggregated performance scores. A case study involving ten industrial robots was conducted to validate the model's effectiveness. The results indicate that Robot 1 is the optimal choice, whereas Robot 6 ranks the lowest. The findings of the study demonstrate that the hybrid fuzzy MCDM approach not only improves accuracy but also provides a comprehensive decision-support tool, enabling manufacturing organizations to select suitable robots based on scientifically grounded data.

This paper deals with mobile robot navigation and obstacle avoidance, presenting a probabilistic search algorithm, Rapidly exploring Random Tree (RRT) method, to ensure stability and flexibility of robots in the face of unexpected events. To optimize the map updating process, the improved RRT automatically builds the map by combining global and local paths. The path is optimized using the Dijkstra algorithm to increase the real-time performance. As the robot moves, the map is continuously updated to detect dynamic obstacles. When an obstacle is detected, a new optimal path is generated to guide the robot to the goal. Experiments on the robot operating system have shown that the optimization works and the robot can automatically avoid static and dynamic obstacles in the simulated environment, map quickly, and avoid small spaces. The results of the study can be extended to movements in unstructured environments and can be used at boundary nodes.