Effective path planning is crucial for autonomous mobile robots navigating complex environments. The “global–local” coupled path planning algorithm exhibits superior global planning capabilities and local adaptability. However, these algorithms often fail to fully realize their potential due to low efficiency and excessive constraints. To address these issues, this study introduces a simpler and more effective integration strategy. Specifically, this paper proposes using a bi-layer map and a feasible domain strategy to organically combine the D*Lite algorithm with the Dynamic Window Approach (DWA). The bi-layer map effectively reduces the number of nodes in global planning, enhancing the efficiency of the D*Lite algorithm. The feasible domain strategy decreases constraints, allowing the local algorithm DWA to utilize its local planning capabilities fully. Moreover, the cost functions of both the D*Lite algorithm and DWA have been refined, enabling the fused algorithm to cope with more complex environments. This paper conducts simulation experiments across various settings and compares our method with A_DWA, another “global–local” coupled approach, which combines A* and DWA. D_DWA significantly outperforms A_DWA in complex environments, despite a 7.43% increase in path length. It reduces the traversal of risk areas by 71.95%, accumulative risk by 80.34%, global planning time by 26.98%, and time cost by 35.61%. Additionally, D_DWA outperforms the A_Q algorithm, a coupled approach validated in real-world environments, which combines A* and Q-learning, achieving reductions of 1.34% in path length, 67.14% in traversal risk area, 78.70% in cumulative risk, 34.85% in global planning time, and 37.63% in total time cost. The results demonstrate the superiority of our proposed algorithm in complex scenarios.

Sandwich panels with carbon fiber fabric/epoxy resin face-sheet and aluminum foam core have a potential application value in the engineering field. To study the bending mechanical properties of the reinforced sandwich structure, three-point bending test was conducted by using WDW-T100 electronic universal tensile testing machine. The relation between load and displacement of the aluminum foam sandwich was obtained. Deformations and failure modes of the specimens were recorded. Scanning electron microscopy was used to observe the failure mechanism. Results showed that when aluminum foam was reinforced by carbon fiber fabric as face-sheet, its flexural load-carrying capacity and energy absorption ability improved significantly. Foam core density and number of carbon fiber plies had serious impacts on the peak load value and energy absorption value of the composite structure. It was suggested that aluminum foam core sandwich structure with low foam core density of 0.49 g/cm³and 5 plies of carbon fiber fabric had the highest energy absorption ability and medium load-carrying ability. Failure modes analysis showed that shear failure leaded to the final failure of sandwich panels with medium peak load and interface de-bonding leaded to the final failure of sandwich panels with high peak load.

In this paper, a topology optimization method combining Bi-directional Evolutionary Structural Optimization (BESO) and Fast Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed, which is called BESO-NSGA-II. To effectively fuse the two algorithms, a semi-random double-penalized crossover and mutation operator is developed. The proposed algorithm extends the traditional single-objective optimization to a double objective optimization problem, and investigates the influence of the stress norm parameters on the optimization results. To verify the effectiveness of the new method, several examples of 2D structural stiffness and stress optimization are given. At the same time, the optimization design of each model was repeated six times to demonstrate the ability of the new method to converge to a solution. Compared with BESO, BESO-NSGA-II can effectively realize the double objective optimization of stiffness and stress, and eliminate the stress concentration phenomenon on the premise of ensuring that the structure has greater stiffness.

Path planning in complex environments remains a challenging task for unmanned vehicles. In this paper, we propose a decoupled path-planning algorithm with the help of a deep reinforcement learning algorithm that separates the evaluation of paths from the planning algorithm to facilitate unmanned vehicles in real-time consideration of environmental factors. We use a 3D surface map to represent the path cost, where the elevation information represents the integrated cost. The peaks function simulates the path cost, which is processed and used as the algorithm’s input. Furthermore, we improved the double deep Q-learning algorithm (DDQL), called retrospective-double DDQL (R-DDQL), to improve the algorithm’s performance. R-DDQL utilizes global information and incorporates a retrospective mechanism that employs fuzzy logic to evaluate the quality of selected actions and identify better states for inclusion in the memory. Our simulation studies show that the proposed R-DDQL algorithm has better training speed and stability compared to the deep Q-learning algorithm and double deep Q-learning algorithm. We demonstrate the effectiveness of the R-DDQL algorithm under both static and dynamic tasks.