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Objective By investigating the adaptability relationship between prefabricated track slab dimensions and track alignments, and proposing reasonable prefabricated slab dimensions based on corresponding constraints, a basis for the engineering design and standardization of prefabricated track slabs is provided. Method Prefabricated track slabs can achieve planar curve alignment through substituting straight segments for curved segments and adjusting fasteners laterally. Construction of transition curve and vertical curve alignment is achieved through adjusting fasteners vertically. The expressions of the allowable lateral and vertical adjustment amounts of the fasteners in relation to slab length are derived based on geometric relationships. Through geometric relationships, the expression of the relationship between prefabricated slab length and width is determined based on the allowable value of slab gaps. Result & Conclusion Research shows that the track slab length limit is closely related to the allowable lateral and vertical adjustment amounts of the fasteners, while the alignment is not a limiting factor for prefabricated slab width. It is recommended to set the allowable lateral adjustment of fasteners to 2 mm, and the allowable vertical adjustment to 15 mm. For the same line, it is suggested that the specifications of prefabricated slab lengths should not exceed two types. When track curve radius is R ≥ 950 m, slabs with a length of 5.9 m are recommended. For curve radii 300 m ≤ R < 950 m, slabs with a length of 3.5 m are recommended. When R

Cooperative search is a future development direction in the field of cruise missile multi-projectile control, and its research is helpful for cruise missile to adapt to the future battlefield intelligent operation requirements. To solve the problem of efficient target search of cruise missile cluster in the absence of prior environmental information, an improved pheromone fast collaborative search algorithm based on correlation location error is proposed, which selects elite individuals within the group to lead the search of cluster members. On the basis of this, this paper combines cross direction finding, position control, information fusion, autonomous grouping and other methods to build a specific implementation architecture suitable for cruise missile cluster. Through numerical simulation analysis and comparison, the architecture constructed in this paper can realize the location of cruise missile cluster to multiple unknown targets in the sea-skimming scenario, and the proposed enhanced pheromone search algorithm can improve the efficiency of collaborative search.

An improved active disturbance rejection control (ADRC) algorithm is proposed in this paper to enhance the heading control capabilities of unmanned surface vehicles (USVs) under wind and wave disturbances. The algorithm introduces two enhancements: parameter tuning and fitting, alongside the optimization of the nonlinear function in the ADRC algorithm. First, the parameter tuning employs an improved pigeon-inspired optimization (PIO) algorithm, which encompasses two strategies: the adaptive strategy and the wandering strategy. Parameter fitting ensures discretely optimized value transition into a continuous state, allowing dynamic parameter adjustments. Second, the optimization of the nonlinear function uses the D-value fitting method. Overall, the improved ADRC algorithm significantly enhances response speed to heading control commands for USVs, fortifying their resistance against wind and wave disturbances. Our proposed algorithm provides a new approach to achieve precise USV heading control. [ABSTRACT FROM AUTHOR]

ABSTRACT The target‐attacker‐defenders game (TADs) is an important topic in multi‐agent systems, concerning group cooperation and decision‐making. To effectively counter fast attackers, this study introduces a passive defensive approach, where a group of defenders position themselves around a formation point, rather than intercept the attacker actively. An evaluation model is developed to determine the optimal formation point, and a comprehensive analysis of the model is conducted, demonstrating that the formation point changes at a slower rate than the attacker, allowing the defenders to effectively track the attacker's movements. To enhance the global search capabilities, a backbone exchange mechanism is integrated into the original pigeon‐inspired optimization (PIO) algorithm. Finally, a series of comparative experiments are conducted, providing a comprehensive assessment of the defense strategy and the improved algorithm. The simulation results indicate the defenders can block the attacker with a slower speed following the proposed strategy, while the UAVs using the comparison method fail to follow the attacker, and the attacker escape the encirclement of defenders. The results prove the effectiveness of the proposed passive method in the article. [ABSTRACT FROM AUTHOR]

The UAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles (UAVs), which is pivotal in public safety applications, particularly in scenarios involving intrusion monitoring and interception. To address the challenges of data acquisition, real-world deployment, and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks, we propose an innovative swarm intelligence-based UAV pursuit-evasion control framework, namely "Boids Model-based DRL Approach for Pursuit and Escape" (Boids-PE), which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning (DRL). The Boids model, which simulates collective behavior through three fundamental rules, separation, alignment, and cohesion, is adopted in our work. By integrating Boids model with the Apollonian Circles algorithm, significant improvements are achieved in capturing UAVs against simple evasion strategies. To further enhance decision-making precision, we incorporate a DRL algorithm to facilitate more accurate strategic planning. We also leverage self-play training to continuously optimize the performance of pursuit UAVs. During experimental evaluation, we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios, customizing the state space, action space, and reward function models for each scenario. Extensive simulations, supported by the PyBullet physics engine, validate the effectiveness of our proposed method. The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks, providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions. [ABSTRACT FROM AUTHOR]