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The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs).

Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management.

Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioning system/inertial navigation system.

The main contribution of this paper is the theoretical derivation and experimental application of the Rao–Blackwellized visual SLAM algorithm with vehicle model partition for small UAVs.

This paper aims to present a technique for optimal trajectory planning of industrial robots that applies a new harmony search (HS) algorithm.

The new HS optimization algorithm adds one more operation to the original HS algorithm. The objective function to be minimized is the trajectory execution time subject to kinematical and mechanical constraints. The trajectory is built by quintic B‐spline curves and cubic B‐spline curves.

Simulation experiments have been undertaken using a 6‐DOF robot QH165. The results show that the proposed technique is valid and that the trajectory obtained using quintic B‐spline curves is smoother than the trajectory using cubic B‐spline curves.

The proposed new HS algorithm is more efficient than the sequential quadratic programming method (SQP) and the original HS method. The proposed technique is applicable to any industrial robot and yields smooth and time‐optimal trajectories.

This study aims to propose a new variable stiffness robot joint (VSR-joint) for operating safely. More and more variable stiffness actuators are being designed and implemented because of their ability to minimize large forces due to shocks, to safely interact with the user and their ability to store and release energy in passive elastic elements.

The design of VSR-joint is compact and integrated highly and the operating is simply. The mechanics, the principle of operation and the model of the VSR-joint are proposed. The principle of operation of VSR-joint is based on a lever arm mechanism with a continuously regulated pivot point. The VSR-joint features a highly dynamic stiffness adjustment along with a mechanically programmable system behavior. This allows an easy adaption to a big variety of tasks.

Preliminary results are presented to demonstrate the fast stiffness regulation response and the wide range of stiffness achieved by the proposed VSR-joint design.

In this paper, a new variable stiffness joint is proposed through changing the cantilever arm to change the performance of the elastic element, which is compact, small size and simple adjustment.

The purpose of this paper is to determine the effect of flash fire exposure on the mechanical properties of single-layer thermal protective clothing.

The full-scale flame manikin tests were performed to simulate flash fire exposure. Two typical fire-resistant fabrics were investigated. The manikin was divided into seven body parts and the specimens meeting the requirements of tensile and tear strength standards were sampled. Fabric thickness, mass per unit area, tensile strength and tear strength were measured and analyzed.

The results revealed the significant influence of heat flux on both of tensile and tear strength. However, the regression analysis indicated the low R² of the liner models. When the tensile and tear strength retention were reorganized based on the body parts, both of the multiple linear regression models for tensile and tear strength showed higher R² than the one-variable linear regressions. Furthermore, the R² of the multiple linear regression model for tear strength retention was remarkably higher than that of the tensile strength.

The findings suggested that greater attention should be paid to the local part of human body and more factors such as the air gap should be considered in the future thermal aging of firefighters’ clothing studies.

The outcomes provided useful information to evaluate the mechanical properties of thermal protective clothing and predict its service life.

The purpose of this paper is to study heat and steam transfer in a vertical air gap and improve thermal protective performance of protective clothing under thermal radiation and hot steam.

An experiment-based model was introduced to analyze heat and moisture transfer in the vertical air gap between the protective clothing and human body. A developed test apparatus was used to simulate different air gap sizes (3, 6, 9, 12, 15, 18, 21 and 24 mm). The protective clothing with different air gap sizes was subjected to dry and wet heat exposures.

The increase of the air gap size reduced the heat and moisture transfer from the protective clothing to the skin surface under both heat exposures. The minimum air gap size for the initiation of natural convection in the dry heat exposure was between 6 and 9 mm, while the air gap size for the occurrence of natural convection was increased in the wet heat exposure. In addition, the steam mass flux presented a sharp decrease with the rising of the air gap size, followed by a stable state, mainly depending on the molecular diffusion and the convection mass transfer.

This research provides a better understanding of the optimum air gap under the protective clothing, which contributes to the design of optimum air gap size that provided higher thermal protection against dry and wet heat exposures.

The purpose of this paper is to gain an in-depth understanding of the research progress, hotspots and future trends in the field of functional clothing.

The records of 4,153 pieces of literature related to functional clothing were retrieved from Web of Science by using a comprehensive retrieval strategy. A piece of software, CiteSpace was used as a tool to visualize the results of specific terms, such as author, institution and keyword. By analyzing the knowledge maps with several indicators, the intellectual basis and research fronts for the functional clothing domain could then be demonstrated.

The result indicated that functional clothing was a popular research field, with approximately 500 papers published worldwide in 2020. Its main research area was material science and involved public environmental and occupational health, engineering, etc. showing the characteristic of multi-interdisciplinary. Textile Research Journal and International Journal of Clothing Science and Technology were the top two journals in this field. The USA, China, Australia, England and Germany have been active and frequently cooperating with each other. Donghua University, the Hong Kong Polytechnic University and NASA, with the largest number of publications, were identified as the main research drivers. According to the co-citation analysis, thermal stress, nanogenerator and electrospinning were the topics of most cited articles during the past 20 years.

The findings identified smart clothing and protective clothing to be the research frontiers in the field of functional clothing, which deserved further study in the future.

The outcomes offered an overview of the research status and future trends of the functional clothing field. It could not only provide scholars with convenience in identifying research hotspots and building potential cooperation in the follow-up research, but also assist beginners in searching core scholars and literature of great significance.

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

The purpose of this study is to determine the effect of ventilation openings and fire intensity on heat transfer and fluid flow within the microclimate between 3D human body and clothing.

On account of interaction effects of fire and ventilation openings on heat transfer process, a 3D transient computational fluid dynamics model considering the real shape of human body and clothing was developed. The model was validated by comparing heat flux history and distribution with experimental results. Heat transfer modes and fluid flow were investigated under three levels of fire intensity for the microclimate with ventilation openings and closures.

Temperature distribution on skin surface with open microclimate was heavily depended on the heat transfer through ventilation openings. Higher temperature for the clothing with confined microclimate was affected by the position and direction of flames injection. The presence of openings contributed to the greater velocity at forearms, shanks and around neck, which enhanced the convective heat transfer within microclimate. Thermal radiation was the dominant heat transfer mode within the microclimate for garment with closures. On the contrary, convective heat transfer within microclimate for clothing with openings cannot be neglected.

The findings provided fundamental supports for the ease and pattern design of the improved thermal protective systems, so as to realize the optimal thermal insulation of the microclimate on the garment level in the future.

The outcomes broaden the insights of results obtained from the mesoscale models. Different high skin temperature distribution and heat transfer modes caused by thermal environment and clothing structure provide basis for advanced thermal protective clothing design.

The purpose of this paper is to demonstrate that the sustainable development thought is one good reason why Chinese civilization is continuously developing, and it can be used as a reference for the development of Chinese agriculture today.

The paper employs a historical analysis approach to examine the sustainable thoughts concerning Chinese traditional agriculture, including view of sancai, farming season, fertility, the nature of matters, recycling, and economization.

The results reveal that the nature of Chinese traditional agriculture is akin to ecological agriculture, which is precious heritage for China and the whole world.

The originality of this paper is that it confirms the fundamental reason of the continuous development of Chinese civilization which, based on organization of sustainable development thought, lies in traditional agriculture.