The traditional A* algorithm has several problems in practical applications, such as many path turning points, redundant nodes, and long running time. it is sometimes impossible to plan the theoretical optimal route. To solve the above problem, this paper presents an optimized A* algorithm, the adaptive adjustment step algorithm and the three-time Bezier curve are used to solve the problems of many turning points, large turning angles, and long running time in the search path. Moreover, aiming at the path planning problem of mobile robots facing dynamic obstacle interference in complex environments, an algorithm that integrates the improved A* algorithm with the dynamic window method is proposed, which not only solves the shortcomings of the A* algorithm in which the dynamic obstacles cannot be avoided, but also prevents the mobile robot from falling into local optimization. The results show that the fusion algorithm of the improved A* algorithm and the dynamic window method with the traditional A* algorithm reduces the number of turns by 50% and the path length by 3.62% compared with the original algorithm. In the same environment, compared with the traditional algorithm, the hybrid algorithm in this paper reduces the average time consumption by 10.27%, the number of path inflection points by 57.14%, and the accuracy is higher than 33.33%, which is more effective in complex dynamic environments.

In recent years, dual-arm robots have been favored in various industries due to their excellent coordinated operability. One of the focused areas of study on dual-arm robots is obstacle avoidance, namely path planning. Among the existing path planning methods, the artificial potential field (APF) algorithm is widely applied in obstacle avoidance for its simplicity, practicability, and good real-time performance over other planning methods. However, APF is firstly proposed to solve the obstacle avoidance problem of mobile robot in plane, and thus has some limitations such as being prone to fall into local minimum, not being applicable when dynamic obstacles are encountered. Therefore, an obstacle avoidance strategy for a dual-arm robot based on speed field with improved artificial potential field algorithm is proposed. In our method, the APF algorithm is used to establish the attraction and repulsion functions of the robotic manipulator, and then the concepts of attraction and repulsion speed are introduced. The attraction and repulsion functions are converted into the attraction and repulsion speed functions, which mapped to the joint space. By using the Jacobian matrix and its inverse to establish the differential velocity function of joint motion, as well as comparing it with the set collision distance threshold between two robotic manipulators of robot, the collision avoidance can be solved. Meanwhile, after introducing a new repulsion function and adding virtual constraint points to eliminate existing limitations, APF is also improved. The correctness and effectiveness of the proposed method in the self-collision avoidance problem of a dual-arm robot are validated in MATLAB and Adams simulation environment.

In order to address the challenges of global path planning in complex and dynamic environments, where avoiding dynamic obstacles is difficult, and local paths are prone to getting stuck in local optima, this paper proposes an enhanced hybrid path planning approach based on improvements to the A* algorithm and the Artificial Potential Field (APF) algorithm. Firstly, addressing the issues of redundant path nodes and non-smooth paths generated by the A* algorithm, we introduce enhancements such as weighted heuristic function, removal of redundant path nodes, and the incorporation of a cubic quasi-uniform B-spline curve to improve search speed and generate smoother paths. Secondly, addressing the problems of unreachable target points and susceptibility to local optima associated with the APF algorithm, two key strategies are employed. On one hand, the repulsive potential field function is modified by incorporating the m -th power of the relative distance from the robot to the target point to ensure reachability. On the other hand, the Constrained Simulated Annealing with Augmented Potential Field (CSA-APF) algorithm is introduced, which integrates simulated annealing with angle and safety distance constraints. This approach facilitates escaping local optima and obtaining global optimal solutions. Finally, the proposed methods are compared through simulation experiments in different scenarios. The experimental results demonstrate that the proposed approach effectively achieves path planning in complex dynamic environments.

To meet the high-accuracy position/force control requirements of dual-arm robots for handling a target object, a control algorithm for dual-arm robots based on the modified sliding mode impedance controller MSMIC(tanh) is proposed. First, the combinative kinematics equation of the dual-arm robots and the unified dynamics model combining the manipulated object is established. Second, according to the impedance control motion model for the object, the desired joint angular accelerations of the manipulators are obtained, and the sliding mode controller based on the hyperbolic tangent function as the switch function is introduced to design the coordinated control strategy for dual-arm robots. The stability and convergence of the designed controller are proved according to the Lyapunov function theory. Finally, the operation tasks of the coordinated transport the target object for dual-arm robots are carried out in the simulated experiment environment. Simulation results show that the proposed control scheme can stably output the required internal force and achieve a high-precision trajectory tracking effect while reducing the periodic torque and joint chattering amplitude generated in the conventional sliding mode control algorithm.

Biomass phytic acid has potential flame retardant value as the main form of phosphorus in plant seeds. In this study, phytate-based flame retardants aluminum phytate (PA-Al) and iron phytate (PA-Fe) were synthesized and characterized. Subsequently, they were introduced into rigid polyurethane foam (RPUF) as flame retardants by one-step water-blown method. The results indicated that RPUF/PA-Fe30 exhibited the highest char residue of 22.1 wt%, significantly higher than 12.4 wt% of RPUF. Cone calorimetry analysis showed that the total heat release (THR) of RPUF/PA-Al30 decreased by 17.0% and total smoke release (TSR) decreased by 22.0% compared with pure RPUF, which were the lowest, demonstrating a low fire risk and good smoke suppression. Thermogravimetric analysis-Fourier transform infrared spectrometer (TG-FTIR) implied the release intensity of flammable gases (hydrocarbons, esters) and toxic gases (isocyanate, CO, aromatic compounds, HCN) of composites was significantly reduced after the addition of PA-Fe. The analysis of char residue indicated that the RPUF composites formed a dense char layer with a high degree of graphitization after the addition of PA-Al/PA-Fe, endowing RPUF composites with excellent mass & heat transmission inhibition effect and fire resistance in the combustion process.

Purpose: The authors propose a solder joint recognition method based on eigenspace technology.Design/methodology/approach: The original solder joint image is transformed into a small set of feature subspace called “eigensolder”, which is the eigenvector of the training set and can represent a solder joint well. Then, the eigensolder feature is extracted by projecting the new solder joint image into the subspace, and the Euclidean distance measure is used to classify the solder joint.Findings: The experimental results show that the proposed method is superior to the traditional classification method in solder joint recognition, and it can achieve 96.43 per cent recognition rate using only 15 eigenvalue images. It is suitable for the classification with small samples.Originality/value: Traditional classification method like neural network and statistical method cost long time. Here, Eigensolder method is used to extract feature. Eigensolder method is more efficient, as it uses the principal component analysis method to reduce the feature dimension of input image and only measure the distance to classify.