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The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It mainly focuses on reducing the memory consumption and alleviating the loop closure problem.

The proposed method alleviates the loop closure problem by improving the accuracy of the robot’s pose. First, two improvements were applied to enhance the accuracy of the hill climbing scan matching. Second, a particle filter was used to maintain the diversity of the robot’s pose and then to supply potential seeds to the hill climbing scan matching to ensure that the best match point was the global optimum. The proposed method reduces the memory consumption by maintaining only a single grid map.

Simulation and experimental results have proved that this method can build a consistent map of a complex environment. Meanwhile, it reduced the memory consumption and alleviates the loop closure problem.

In this paper, a new SLAM algorithm has been proposed. It can reduce the memory consumption and alleviate the loop closure problem without lowering the accuracy of the generated grid map.

This paper aims to propose a novel robot kinematic calibration method based on the common perpendicular line (CPL) model to improve the absolute accuracy of industrial robots.

The deviation between the nominal and actual twists is considered the CPL transformation, which includes the rotation about the CPL and the translation along the CPL. By using the invariance of the reciprocal product of the two spatial lines, the previous deviation was analyzed in the neighbor space of the base frame origin. In this space, the line vector of the CPL contained only four independent parameters: two orientation elements and two moment elements. Thus, the CPL model has four independent parameters for the revolute joint and two parameters for the prismatic joint.

By simulations and experiment conducted on a SCARA robot and a 6-DOF PUMA robot, the effectiveness of the novel method for calibration of industrial robot is validated.

The CPL model avoided the normalization and orthogonalization in the iterative identification procedure. Therefore, identifying the CPL model was not only simpler but also more accurate than that of the traditional model. In addition, the results of the CPL transformation strictly conformed to the constraints of the twist.

Because submaps including a subset of the global map contain more environmental information, submap-based graph simultaneous localization and mapping (SLAM) has been studied by many researchers. In most of those studies, helpful environmental information was not taken into consideration when designed the termination criterion of the submap construction process. After optimizing the graph, cumulative error within the submaps was also ignored. To address those problems, this paper aims to propose a two-level optimized graph-based SLAM algorithm.

Submaps are updated by extended Kalman filter SLAM while no geometric-shaped landmark models are needed; raw laser scans are treated as landmarks. A more reasonable criterion called the uncertainty index is proposed to combine with the size of the submap to terminate the submap construction process. After a submap is completed and a loop closure is found, a two-level optimization process is performed to minimize the loop closure error and the accumulated error within the submaps.

Simulation and experimental results indicate that the estimated error of the proposed algorithm is small, and the maps generated are consistent whether in global or local.

The proposed method is robust to sparse pedestrians and can be adapted to most indoor environments.

In this paper, a two-level optimized graph-based SLAM algorithm is proposed.

The interval multi-objective optimization problems (IMOPs) are universal and vital uncertain optimization problems. In this study, an interval multi-objective grey wolf optimization algorithm (GWO) based on fuzzy system is proposed to solve IMOPs effectively.

First, the classical genetic operators are embedded into the interval multi-objective GWO as local search strategies, which effectively balanced the global search ability and local development ability. Second, by constructing a fuzzy system, an effective local search activation mechanism is proposed to save computing resources as much as possible while ensuring the performance of the algorithm. The fuzzy system takes hypervolume, imprecision and number of iterations as inputs and outputs the activation index, local population size and maximum number of iterations. Then, the fuzzy inference rules are defined. It uses the activation index to determine whether to activate the local search process and sets the population size and the maximum number of iterations in the process.

The experimental results show that the proposed algorithm achieves optimal hypervolume results on 9 of the 10 benchmark test problems. The imprecision achieved on 8 test problems is significantly better than other algorithms. This means that the proposed algorithm has better performance than the commonly used interval multi-objective evolutionary algorithms. Moreover, through experiments show that the local search activation mechanism based on fuzzy system proposed in this study can effectively ensure that the local search is activated reasonably in the whole algorithm process, and reasonably allocate computing resources by adaptively setting the population size and maximum number of iterations in the local search process.

This study proposes an Interval multi-objective GWO, which could effectively balance the global search ability and local development ability. Then an effective local search activation mechanism is developed by using fuzzy inference system. It closely combines global optimization with local search, which improves the performance of the algorithm and saves computing resources.

The present study assesses the impact resistance of the shear thickening fluids-filled (STFs-filled) foam through drop-hammer impact tests.

The maximum residual impact load and specific impact energy absorption rate of STF-filled foam are studied with varying thickness (4–14 mm), densities (0.35–0.6 g/cm3) and hardness (40–50 Rockwell Hardness C Scale (HRC)) under different ambient temperatures (−20−20 °C) and impact energies (25–75 J).

The following conclusions are obtained from this study: (1) the higher the impact energy, the greater the maximum residual impact force and energy absorption efficiency of the material; (2) the impact resistance of STF-filled foam can be improved with the decrease of ambient temperature, achieving the highest energy absorption rate at −10?. (3) STF-filled foam substrate has the highest impact resistance, the lowest maximum residual impact force and the highest energy absorption coefficient when the density is 0.35  g/cm3, the hardness is 45HC and the thickness is 10 mm.

This is the first paper to analyze the impact of both environmental factors and material properties on the impact resistance of STF-filled foam. The results show that the decrease in temperature and the increase in hardness can enhance the impact resistance of STF-filled foam.

For coastal bridges, the ability to recover traffic functions after the earthquake has crucial implications for post-disaster reconstruction, which makes resilience become a significant index to evaluate the seismic behavior. However, the deterioration of the material is particularly prominent in coastal bridge, which causes the degradation of the seismic behavior. As far, the research studies on resilience of coastal bridges considering multiple degradation factors and different disaster prevention capability are scarce. For further evaluating the seismic behavior of coastal bridge in the long-term context, the seismic resilience is conducted in this paper with considering multiple durability damage.

The fuzzy theory and time-varying fragility analysis are combined in this paper to obtain the life-cycle resilience of coastal bridges.

The results show that durability damage has a remarkable impact on the resilience. After 100 years of service, the seismic resilience of bridge with poor disaster-prevention capability has greatest reduction, about 18%. In addition, the improvement of the disaster prevention capability can stabilize the resilience of the bridge at a higher level.

In this paper, the time-varying fragility analysis of case bridge are evaluated with considering chloride ion erosion and concrete carbonization, firstly. Then, combining fuzzy theory and fragility analysis, the triangular fuzzy values of resilience parameters under different service period are obtained. Finally, the life-cycle resilience of bridge in different disaster prevention capability is analyzed.

The purpose of this study is to develop a method of thermodynamic and kinetic evaluation of corrosion properties of alloys.

Method of estimation of corrosion-electrochemical behaviour of multicomponent alloys is proposed. The method takes into account both thermodynamic and kinetic data and is based on mutual construction of equilibrium and polarization potential – pH diagrams. The usage of the proposed method is illustrated in the example of the structural steel 20KT.

Passivation of steel 20KT is determined by formation of oxide film based on magnetite (Fe₃O₄); silicon, manganese and copper oxides as well as manganese sulphides can be locally included into the inner side of the passivation layer. An experimental potential – pH diagram of steel 20KT is constructed. Interpreting the results of polarization measurements revealed good agreement between equilibrium and polarization potential – pH diagrams.

It is shown in the example of structural steel 20KT that for interpretation of experimental potential – pH diagrams, one should compare them with corresponding equilibrium diagrams for multicomponent alloys rather than with Pourbaix diagrams for pure metals. The corrosion properties of steel 20KT are estimated using equilibrium and polarization potential – pH diagrams.

Shaft orbit is an important characteristic for vibration monitoring and diagnosing system of hydroelectric generating set. Because of the low accuracy and poor reliability of traditional methods in identifying the shaft orbit moving direction (MD), the purpose of this paper is to present a novel automatic identification method based on trigonometric function and polygon vector (TFPV).

First, some points on shaft orbit were selected with inter‐period acquisition method and joined together orderly to form a complex plane polygon. Second, by using the coordinate transformation and rotation theory, TFPV were applied comprehensively to judge the concavity or convexity of the polygon vertices. Finally, the shaft orbit MD is identified.

The simulation and experiment demonstrate that the method proposed can effectively identify the common shaft orbit MD.

In order to identity the shaft orbit MD effectively, a novel automatic identification method based on TFPV is proposed in this paper. The problem of identifying the shaft orbit MD is transformed into the problem about orientation of complex polygons, which are formed orderly by points on orbit shaft, and TFPV are applied comprehensively to judge the concavity or convexity of the polygon vertices.