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The purpose of this paper is to search the optimal arrangement scheme of random motion accuracy of joints for optimal synthesis of pose repeatability which can make robot design more reasonable and reduce the development cost of robots.

In this paper, a mathematical model of pose repeatability, which includes positioning repeatability and orientation repeatability of robots, is established. According to the ISO 9283 standard, an optimal synthesis method of pose repeatability for collaborative robots is introduced, and three optimization objective functions are proposed. The optimization model is solved by using numerical analysis software, and the optimal arrangement scheme of random motion accuracy of joints is obtained which meets the requirements of pose repeatability of robot.

It is found that, in three optimization objective functions, the single-objective evaluation function of maximization of joint motion error is more suitable for optimal synthesis of pose repeatability. In practice, due to the safety factor, the test results of pose repeatability are better than the results of optimal synthesis of pose repeatability.

This method makes robot design more reasonable and reduces the development cost of robots.

This work is the first time to optimize the orientation repeatability of collaborative robots. Because the pose repeatability of most robots is tested by the ISO 9283 standard, so this method which is based on this standard is more suitable for the performance requirements of robot products.

Collaborative robots (cobots) are widely used in various manipulation tasks within complex industrial environments. However, the manipulation capabilities of cobot manipulation planning are reduced by task, environment and joint physical constraints, especially in terms of force performance. Existing motion planning methods need to be more effective in addressing these issues. To overcome these challenges, the authors propose a novel method named force manipulability-oriented manipulation planning (FMMP) for cobots.

This method integrates force manipulability into a bidirectional sampling algorithm, thus planning a series of paths with high force manipulability while satisfying constraints. In this paper, the authors use the geometric properties of the force manipulability ellipsoid (FME) to determine appropriate manipulation configurations. First, the authors match the principal axes of FME with the task constraints at the robot’s end effector to determine manipulation poses, ensuring enhanced force generation in the desired direction. Next, the authors use the volume of FME as the cost function for the sampling algorithm, increasing force manipulability and avoiding kinematic singularities.

Through experimental comparisons with existing algorithms, the authors validate the effectiveness and superiority of the proposed method. The results demonstrate that the FMMP significantly improves the force performance of cobots under task, environmental and joint physical constraints.

To improve the force performance of manipulation planning, the FMMP introduces the FME into sampling-based path planning and comprehensively considers task, environment and joint physical constraints. The proposed method performs satisfactorily in experiments, including assembly and in situ measurement.

This study aims to provide a new method of multiscale directional Lyapunov exponents (MSDLE) calculated based on the state space reconstruction for the nonstationary time series, which can be applied to detect the small target covered by sea clutter.

Reconstructed state space is divided into non-overlapping submatrices whose columns are equal to a predetermined scale. The authors compute eigenvalues and eigenvectors of the covariance matrix of each submatrix and extract the principal components σ_ip and their corresponding eigenvectors. Then, the angles ψ_ip of eigenvectors between two successive submatrices were calculated. The curves of (σ_ip, ψ_ip) reflect the nonlinear dynamics both in kinetic and directional and form a spectrum with multiscale. The fluctuations of (σ_ip, ψ_ip), which are sensitive to the differences of backscatter between sea wave and target, are taken out as the features for the target detection.

The proposed method can reflect the local dynamics of sea clutter and the small target within sea clutter is easily detected. The test on the ice multiparameter imaging X-ban radar data and the comparison to K distribution based method illustrate the effectiveness of the proposed method.

The detection of a small target in sea clutter is a compelling issue, as the conventional statistical models cannot well describe the sea clutter on a larger timescale, and the methods based on statistics usually require the stationary sea clutter. It has been proven that sea clutter is nonlinear, nonstationary or cyclostationary and chaotic. The new method of MSDLE proposed in the paper can effectively and efficiently detect the small target covered by sea clutter, which can be also introduced and applied to military, aerospace and maritime fields.

Autonomous flight of unmanned aerial vehicles (UAVs) in global position system (GPS)-denied environments has become an increasing research hotspot. This paper aims to realize the indoor fixed-point hovering control and autonomous flight for UAVs based on visual inertial simultaneous localization and mapping (SLAM) and sensor fusion algorithm based on extended Kalman filter.

The fundamental of the proposed method is using visual inertial SLAM to estimate the position information of the UAV and position-speed double-loop controller to control the UAV. The motion and observation models of the UAV and the fusion algorithm are given. Finally, experiments are performed to test the proposed algorithms.

A position-speed double-loop controller is proposed, by fusing the position information obtained by visual inertial SLAM with the data of airborne sensors. The experiment results of the indoor fixed-points hovering show that UAV flight control can be realized based on visual inertial SLAM in the absence of GPS.

A position-speed double-loop controller for UAV is designed and tested, which provides a more stable position estimation and enabled UAV to fly autonomously and hover in GPS-denied environment.

This paper aims to use a fractional constitutive model with a nonlocal velocity gradient for replacing the nonlinear constitutive model to characterize its complex rheological behavior, where non-linear characteristics exist, for example, the inherent viscous behavior of the crude oil. The feasibility and flexibility of the fractional model are tested via a case study of non-Newtonian fluid. The finite element method is non-Newtonian used to numerically solve both momentum equation and energy equation to describe the fluid flow and convection heat transfer process.

This paper provides a comprehensive theoretical and numerical study of flow and heat transfer of non-Newtonian fluids in a pipe based on the fractional constitutive model. Contrary to fractional order a, the rheological property of non-Newtonian fluid changes from shear-thinning to shear-thickening with the increase of power-law index n, therefore the flow and heat transfer are hindered to some extent.

This paper discusses two dimensionless parameters on flow regime and thermal patterns, including Reynolds number (Re) and Nusselt number (Nu) in evaluating the flow rate and heat transfer rate. Analysis results show that the viscosity of the non-Newtonian fluid decreases with the rheological index (order α) increasing. While large fractional (order α) corresponds to the enhancement of heat transfer capacity.

First, it is observed that the increase of the Re results in an increase of the local Nusselt number (Nul). It means the heat transfer enhancement ratio increases with Re. Meanwhile, the increasement of the Nul indicating the enhancement in the heat transfer coefficient, produces a higher speed flow of crude oil.

This study presents a new numerical investigation on characteristics of steady-state pipe flow and forced convection heat transfer by using a fractional constitutive model. The influences of various non-dimensional characteristic parameters of fluid on the velocity and temperature fields are analyzed in detail.

An accurate detection of overhead ground wire under open surroundings with varying illumination is the premise of reliable line grasping with the off-line arm when the inspection robot cross obstacle automatically. This paper aims to propose an improved approach which is called adaptive homomorphic filter and supervised learning (AHSL) for overhead ground wire detection.

First, to decrease the influence of the varying illumination caused by the open work environment of the inspection robot, the adaptive homomorphic filter is introduced to compensation the changing illumination. Second, to represent ground wire more effectively and to extract more powerful and discriminative information for building a binary classifier, the global and local features fusion method followed by supervised learning method support vector machine is proposed.

Experiment results on two self-built testing data sets A and B which contain relative older ground wires and relative newer ground wire and on the field ground wires show that the use of the adaptive homomorphic filter and global and local feature fusion method can improve the detection accuracy of the ground wire effectively. The result of the proposed method lays a solid foundation for inspection robot grasping the ground wire by visual servo.

This method AHSL has achieved 80.8 per cent detection accuracy on data set A which contains relative older ground wires and 85.3 per cent detection accuracy on data set B which contains relative newer ground wires, and the field experiment shows that the robot can detect the ground wire accurately. The performance achieved by proposed method is the state of the art under open environment with varying illumination.

Post-disaster population resettlement is a complicated process, during which the restoration of livelihood and lifestyle plays a critical role in achieving a successful resettlement outcome. This paper attempts to examine how recovery policies and relocation approaches influence people's livelihood recovery and perception of wellbeing. It specifically investigates the role of farmland in producing a livelihood and maintaining a rural lifestyle among displaced people.

Through face-to-face questionnaire surveys and in-depth interviews with rural residents displaced from their villages after the Wenchuan earthquake in Sichuan, China, this study presents both quantitative and qualitative evidence to investigate how post-disaster policies and particularly the availability of farmland influence people's recovery and their satisfaction with the post-resettlement life.

Data suggest that availability of farmland, in spite of the size, makes big differences in post-disaster recovery because farmland provides resettled people with not only a livelihood to secure basic living but also a guarantee to maintain a rural lifestyle.

More samples are needed for analyzing factors that significantly influence disaster-displaced farmers' recovery and wellbeing post resettlement.

This study can be used as an important reference for making plans for post-disaster recovery and population resettlement programs in other disaster-prone countries across the world.

Land-based relocation is proposed as a desirable approach to addressing challenges of livelihood restoration amongst the resettled population in rural areas of developing countries.

Travel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.

The authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.

Case studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.

This study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

The purpose of this paper is to obtain the mechanical behavior and damage mechanism of the coal and rock near the stope under the stress state and stress paths of the surrounding rock with the dynamic mining.

Through the three-axial compression test and the uniaxial compression test by meso experiment device, the mechanical behavior and fracture evolution process of coal and rock were studied, and the acoustic emission (AE) characteristics under uniaxial compression of the coal and rock were contrasted.

Under the three-axial compression, the strength of coal and rock enhance significantly by confining pressure. The volume of outburst coal shows obvious stages: compression is followed by expansion. The coal first appear to undergo compaction under vertical stress due to volume decrease, but with the development of micro- and macro-cracks, the specimens appeared to expand; under the uniaxial compression, through the comparison of stress–strain relationship and the crack propagation process, stress drop and fracture of coal have obvious correlation. The destruction of coal was gradual due to the slow and steady accumulation of internal damage. Due to the influence of the end effect, the specimens show the “conjugate double shear failure”. The failure process of the coal and rock and the characteristics of the AEs have a corresponding relationship: the failure causes a large number of AE events. Before the events peak, there was an initial stage, calm growth stage and explosive growth stage. There were some differences between the rock and coal in the characteristics of the AE.

These research studies are conducted to provide guidance on the basis of mine disaster prevention and control.

The purpose of this paper is to develop a new methodology to find out the best robotic assembly sequence amongst feasible robotic sequences.

The feasible robotic assembly sequences were generated based on the assembly constraints and later and artificial immune system (AIS) was implemented to find out the best assembly sequence.

The paper reveals the best assembly sequence.

Robotic assembly has expanded the process capabilities in the manufacturing world because of the fact that it is faster, more efficient, precise and cost‐effective process than any conventional mechanized process. Since a robotic system is a cost‐intensive one it is necessary to find out the correct and optimal sequence with the constraints of the process in mind while dealing with assembled products with large number of parts.