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The purpose of this paper is to propose a new smooth online near time‐optimal trajectory planning approach to reduce the consuming time compared to the conventional dynamics trajectory planning methods.

In the proposed method, the robot path is expressed by a scalar path coordinate. The joints torque boundary and speed boundary are transformed into the plane, which can generate the limitation curves of pseudo‐velocity. The maximum pseudo‐velocity curve that meets the limits of torque and speed is built up through the feature points and control points in the plane by using cubic polynomial fitting method. Control points used for cubic polynomial construction are optimized by the Golden‐Section method.

The proposed method can avoid Range's phenomenon and also guarantee the continuity of torque.

The algorithm designed in this paper is used for the controller of a new industrial robot which will be equipped for the welding automatic lines of Chery Automobile Co. Ltd.

Compared to the five‐order polynomial trajectory optimization method proposed by Macfarlane and Croft, the approach described in this paper can effectively take advantage of joints maximum speed, and the calculation time of this method is shorter than conventional dynamics methods.

The purpose of this paper is to propose a new velocity prediction navigation algorithm to develop a conflict-free path for robots in dynamic crowded environments. The algorithm BP-prediction and reciprocal velocity obstacle (PRVO) combines the BP neural network for velocity PRVO to accomplish dynamic collision avoidance.

This presented method exhibits innovation by anticipating ahead velocities using BP neural networks to reconstruct the velocity obstacle region; determining the optimized velocity corresponding to the robot’s scalable radius range from the error generated by the non-holonomic robot tracking the desired trajectory; and considering acceleration constraints, determining the set of multi-step reachable velocities of non-holonomic robot in the space of velocity variations.

The method is validated using three commonly used metrics of collision rate, travel time and average distance in a comparison between simulation experiments including multiple differential drive robots and physical experiments using the Turtkebot3 robot. The experimental results show that our method outperforms other RVO extension methods on the three metrics.

In this paper, the authors propose navigation algorithms capable of adaptively selecting the optimal speed for a multi-robot system to avoid robot collisions during dynamic crowded interactions.

The purpose of this study is to develop a new parallel metaheuristic algorithm for solving unconstrained continuous optimization problems.

The proposed method brings several metaheuristic algorithms together to form a coalition under Weighted Superposition Attraction-Repulsion Algorithm (WSAR) in a parallel computing environment. The proposed approach runs different single solution based metaheuristic algorithms in parallel and employs WSAR (which is a recently developed and proposed swarm intelligence based optimizer) as controller.

The proposed approach is tested against the latest well-known unconstrained continuous optimization problems (CEC2020). The obtained results are compared with some other optimization algorithms. The results of the comparison prove the efficiency of the proposed method.

This study aims to combine different metaheuristic algorithms in order to provide a satisfactory performance on solving the optimization problems by benefiting their diverse characteristics. In addition, the run time is shortened by parallel execution. The proposed approach can be applied to any type of optimization problems by its problem-independent structure.

The purpose of this paper is to present, an optimization problem based on the imperialistic competitive algorithm (ICA) approach for optimizing the needle velocity and variation of needle acceleration in a link drive mechanism of a sewing machine. The optimal geometry of the link drive has been achieved using a non-linear optimization procedure.

As an important study in this case, the authors might refer to a previous work in which they introduced the possibility of replacing the slider-crank mechanism, that is typically used in sewing machines, with a link drive mechanism. The authors regenerate the optimization problem by modifying the objective function and follow a novel optimization method based on the ICA to overcome the drawbacks of that work. In addition, further modification of the objective function with respect to the variation of needle acceleration is applied to assure smooth movement of the needle during sewing process.

The results showed a significant improvement with respect to the optimization of needle velocity and variation of needle acceleration in comparison to that previous work. This clearly justifies the efficiency and reliability of the optimization formulation based on the ICA approach.

Needle temperature is considered as an effective parameter on sewing process efficiency and stitch quality. Needle heat generated during sewing process is directly related to needle velocity in penetration zone which in turn depends on the needle driver mechanism of sewing machine. According to literature survey, few researches have focussed to design a driver mechanism of the sewing machine to reduce the generated needle heat. This mechanism has the ability of reducing the penetration velocity of the needle without affecting sewing speed which consequently can reduce the needle heat generated during needle penetration. The work here is novel regarding implementation of optimization algorithm for this mechanism.

Reliability-based design optimizations (RBDOs) of engineering structures involve complex non-linear/non-differentiable performance functions, including both continuous and discrete variables. The gradient-based RBDO algorithms are less than satisfactory for these cases. The simulation-based approaches could also be computationally inefficient, especially when the double-loop strategy is used. This paper aims to present a pseudo-double loop flexible RBDO, which is efficient for solving problems, including both discrete/continuous variables.

The method is based on the hybrid improved binary bat algorithm (BBA) and weighed simulation method (WSM). According to this method, each BBA’s movement generates proper candidate solutions, and subsequently, WSM evaluates the reliability levels for design candidates to conduct swarm in a low-cost safe-region.

The accuracy of the proposed enhanced BBA and also the hybrid WSM-BBA are examined for ten benchmark deterministic optimizations and also four RBDO problems of truss structures, respectively. The solved examples reveal computational efficiency and superiority of the method to conventional RBDO approaches for solving complex problems including discrete variables.

Unlike other RBDO approaches, the proposed method is such organized that only one simulation run suffices during the optimization process. The flexibility future of the proposed RBDO framework enables a designer to present multi-level design solutions for different arrangements of the problem by using the results of the only one simulation for WSM, which is very helpful to decrease computational burden of the RBDO. In addition, a new suitable transfer function that enhanced convergence rate and search ability of the original BBA is introduced.

The purpose of this paper is to improve the hydraulic efficiency without changing the overall dimension. The blade profile optimization design of the aero-centrifugal pump based on the biharmonic equation surrogate model has been studied.

First of all, Bezier curves and linear function are used to control the annular angle distribution and the stacking angle of blade profile under the MATLAB platform. Grid independence analysis has been studied to find the finest mesh scheme. After the precision comparison of test data and computation fluid dynamics 15 sets of design parameters are carried out as the boundary condition of the biharmonic equation. The efficiency surrogate model of the biharmonic equation is constructed via iteratively solving of a discrete difference equation. The other two surrogate models of response surface model (RSM) and radial basis function neural network surrogate model (RBFNNSM) are compared with the biharmonic equation surrogate model by the standard of modified complex correlation coefficient R² and root mean square deviation (RSME). Finally, the artificial fish swarm algorithm has been used to find the global optimal design parameters with the objective function of highest efficiency.

The results show that the design parameters code conversion method can reduce the number of optimization parameters from five to three, makes the design space become a cube, and compared with RSM and RBFNNSM, the biharmonic equation surrogate model has higher precision with R² is 0.8958, RSME is 0.1382. The final optimum result of AFSA is at the point of [1 −1 −1]. The internal flow field analysis shows that after optimization the outlet relative velocity becomes more uniform and the wake effect has been significantly decreased. The hydraulic efficiency of the optimized pump is about 59.45 per cent increasing 5.4 per cent compared with a prototype pump.

This study developed a new method to optimize the design parameters of aero-centrifugal pump impeller based on biharmonic equation surrogate model, which had a good agreement with experimental values within just 15 sets of the original design. The optimization results shows that the method can improve the hydraulic efficiency significantly.

Computational efficiency is always the major concern in aircraft design. The purpose of this paper is to investigate an efficient aeroelasticity optimization design method. Analysis of composite wing elastic axis is presented in the current study and its application on aeroelasticity optimization design is discussed.

Elastic axis consists of stiffness centers. The stiffness centers of eight cross sections are analyzed and the wing elastic axis is obtained through least‐squares procedure. In the analysis of the cross section stiffness center, the wing model is approximated by assuming the wing cross section as a thin walled structure with a single cell closed section and assuming the composite material to be a 3D anisotropic material. In aeroelasticity optimization design, objective functions are taken to be the wing weight and elastic axis position. Design variables are the thickness and area of wing components.

After aeroelasticity optimization design, the wing weight decreases while the divergent velocity increases. Meanwhile, it can achieve an expected result but costs much less computational time than the conventional method.

The results can be used for aircraft design or as an initial value for the next detailed optimization design.

The computational time can be dramatically reduced through the aeroelasticity optimization design based on the elastic axis. It is suitable for engineering applications.

This study aims to complete the optimization design of a centrifugal impeller with both high aerodynamic efficiency and good structural machinability.

First, the design parameters were derived from the blade loading distribution and the meridional geometry in the impeller three-dimensional (3D) inverse design. The blade wrap angle at the middle span surface and the spanwise averaged blade angle at the blade leading edge obtained from inverse design were chosen as the machinability objectives. The aerodynamic efficiency obtained by computational fluid dynamics was selected as the aerodynamic performance objective. Then, using multi-objective optimization with the optimal Latin hypercube method, quadratic response surface methodology and the non-dominated sorting genetic algorithm, the trade-off optimum impellers with small blade wrap angles, large blade angles and high aerodynamic efficiency were obtained. Finally, computational fluid dynamics and computer-aided manufacturing were performed to verify the aerodynamic performance and structural machinability of the optimum impellers.

Providing the fore maximum blade loading distribution at both the hub and shroud for the 3D inverse design helped to promote the structural machinability of the designed impeller. A straighter hub coupled with a more curved shroud also facilitated improvement of the impeller’s structural machinability. The preferred impeller was designed by providing both the fore maximum blade loading distribution at a relatively straight hub and a curved shroud for 3D inverse design.

The machining difficulties of the designed high-efficiency impeller can be reduced by reducing blade wrap angle and enlarging blade angle at the beginning of impeller design. It is of practical value in engineering by avoiding the follow-up failure for the machining of the designed impeller.

This paper aims to investigate the problem of on-line orbit planning and guidance for an advanced upper stage.

The double impulse optimal transfer orbit is planned by the Lambert algorithm and the improved particle swarm optimization (IPSO) method, which can reduce the total velocity increment of the transfer orbit. More specially, a simplified formula is developed to obtain the working time of the main engine for two phases of flight based on the theorem of impulse. Subsequently, the true anomalies of the start position and the end position for both two phases are planned by the Newton iterative algorithm and the Kepler equation. Finally, the first phase of flight is guided by a novel iterative guidance (NIG) law based on the true anomaly update with respect to the geometrical relationship. Also, a completely analytical powered explicit guidance (APEG) law is presented to realize orbital injection for the second phase of flight.

Simulations including Monte Carlo and three typical orbit transfer missions are carried out to demonstrate the efficiency of the proposed scheme.

A novel on-line orbit planning algorithm is developed based on the Lambert problem, IPSO optimization method and Newton iterative algorithm. The NIG and APEG are presented to realize the designed transfer orbit for the first and second phases of flight. Both two guidance laws achieve higher orbit injection accuracies than traditional guidance laws.

The purpose of this paper is to present an algorithm for performing collision avoidance with robotic manipulators.

The method does not require any a priori knowledge of the motion of other objects in its environment. Moreover, it is computationally efficient enough to be implemented in real time. This is achieved by constructing limitations on the motion of a manipulator in terms of its allowable instantaneous velocity. Potential collisions and joint limits are formulated as linear inequality constraints. Selection of the optimal velocity is formulated as a convex optimization and is solved using an interior point method.

Experimental results with two industrial arms verify the effectiveness of the method and illustrate its ability to easily handle many simultaneous potential collisions.

The resulting algorithm allows arbitrary motions commanded to the robot to be modified on‐line in order to guarantee optimal real‐time collision avoidance behaviors.