Considering the complex nature of branch pipes, which typically exhibit one-to-many characteristics, a transformation is implemented to facilitate one-to-one pipe routing. This approach primarily addresses the challenges related to laying sequence planning, branch point determination, and pipe direction. In terms of engineering practice, the optimal branch points are selected from a set of discrete points. Subsequently, the branch pipe laying sequence is determined based on the optimal distances between adjacent endpoints. Meanwhile, taking into account the constraints imposed by three-way connectors, the branch pipeline issue is resolved employing the ant lion algorithm. During the layout solving process, the ant lion algorithm exhibits certain limitations, such as local optima and slow convergence speed. To overcome these deficiencies, enhancements are made and rigorous testing is conducted. The improved ant lion algorithm is then employed to achieve automated layout optimization of branch pipelines, thereby enhancing layout efficiency. As for the branch pipeline direction problem, inspiration is drawn from Electronic Design Automation (EDA) wiring. By integrating orthogonal and non-orthogonal pipelines, a method is devised to achieve engineering aesthetics in general. Finally, the proposed method’s effectiveness is verified through a joint simulation utilizing Siemens NX/GRIP and Matlab software. This verification ensures the soundness and applicability of the proposed approach to practical scenarios.

A mild, radical cascade cyclization of N-arylacrylamides with diselenides for the preparation of oxindoles via iodine oxidation is disclosed that provides an environmentally friendly process for the construction of C–Se bonds. Twenty-five examples of N-arylacrylamide substrates were investigated, and excellent yields were achieved. The tandem cyclization of acrylamide with diphenyl disulfide was also applicable under the same conditions.

In order to solve the limitations of ant lion optimization algorithm such as local optimum slow convergence speed and so on this paper applies optimization strategy Logistic chaotic map and reverse learning to improve the selection of random ant lion update ant position and elite ant lion respectively. The improved ant lion optimization algorithm is compared and analyzed by selecting standard test functions, and the optimization accuracy and convergence speed of the algorithm are obviously improved. The improved ant lion optimization algorithm combined with a series of pipe routing engineering rules is applied to the pipe routing of aeroengine. The simulation of a pipe routing example is carried out by Siemens NX11.0 Grip and MATLAB, and the effectiveness of the proposed method is verified again.

In complex marine environments, path-following control of unmanned surface vessels (USVs) faces numerous challenges, including environmental disturbances, dynamic nonlinearities, and underactuated systems. To overcome the limitations of traditional line-of-sight/PID control in terms of robustness and adaptability, this study proposes a hybrid control architecture combining improved deep deterministic policy gradient (IDDPG) and model predictive control (MPC). The IDDPG algorithm, as the upper-level decision-making module, utilizes deep reinforcement learning to generate optimal heading angle increment commands by learning the environmental state. The MPC, as the lower-level execution module, optimizes control variables such as thrust and rudder angle through rolling optimization based on the USV’s three-degree-of-freedom nonlinear dynamics model. This study constructs a closed-loop “perception-decision-execution-learning” paradigm and employs gradient pruning and a customized reward function to ensure the stability of algorithm training and the optimality of control decisions. Lateral deviation and heading angle error are used as evaluation metrics to verify the control performance. Simulation results show that this method effectively solves the adaptability challenge of traditional control strategies in complex environments. Compared with the traditional ALOS-PID method, the average lateral deviation is reduced by 37% and the heading angle error is reduced by 21%, thus realizing high-precision path tracking control for unmanned surface vessels and providing a new method for autonomous surface vehicle navigation.

Multi-robot systems are increasingly applied in various domains, with efficient task allocation and path planning being crucial for successful collaboration. This paper presents a method combining the ant colony algorithm for task allocation and the A* algorithm for path planning. By leveraging the global search capability of the ant colony algorithm and the heuristic advantages of A*, efficient collaboration can be achieved. Key improvements are made to the A* algorithm, including the use of Manhattan distance as the heuristic function, which better reflects the real distance in grid environments. A 3×3 neighborhood search strategy reduces the average search nodes, enhances local optimal path identification, and increases efficiency. Additionally, theta* line of sight optimization eliminates redundant turning points, generating smoother, shorter paths. Simulation results show the improved algorithm produces a 6% shorter path with smoother transitions compared to the original, demonstrating its effectiveness in task allocation and obstacle avoidance for multi-robot systems.