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As a specific task for unmanned tracked vehicles, leader-following imposes high-precision requirements on the vehicle's motion control, especially the steering control. However, due to characteristics such as the frequent changes in off-road terrain and steering resistance coefficients, controlling tracked vehicles poses significant challenges, making it difficult to achieve stable and precise leader-following. This paper decouples the leader-following control into speed and curvature control to address such issues. It utilizes model reference adaptive control to establish reference models for the speed and curvature subsystems and designs corresponding parameter adaptive control laws. This control method enables the actual vehicle speed and curvature to effectively track the response of the reference model, thereby addressing the impact of frequent changes in the steering resistance coefficient. Furthermore, this paper demonstrates significant improvements in leader-following performance through a series of simulations and experiments. Compared with the traditional PID control method, the results shows that the maximum following distance has been reduced by at least approximately 12% (ensuring the ability to keep up with the leader), the braking distance has effectively decreased by 22% (ensuring a safe distance in an emergency braking scenario and improving energy recovery), the curvature tracking accuracy has improved by at least 11% (improving steering performance), and the speed tracking accuracy has increased by at least 3.5% (improving following performance). [ABSTRACT FROM AUTHOR]

The balance between pertinence and universality of high value-added energetic materials has gained importance in recent years. Inspired by the efficient working mechanism of stem cells, an innovative energetic material concept, multifunctional modular energetic materials (MMEMs), with the ability of quantity production and multifunctional uses was first proposed. A machine learning-assisted high-throughput screening system was developed and applied to guide the discovery of MMEMs, and promising energetic compounds were rapidly targeted and synthesized. These compounds exhibited a satisfactory density, thermal properties, detonation performance and sensitivity. Furthermore, they can be easily synthesized to maintain the "energetic stem cell pool" and can undergo "differentiation" for functioning as various compounds such as crystal explosives, cast explosive carriers, energetic binders, and plasticizers through different processing forms/different formulae as needed. It is anticipated that the findings of this study will open up a new research direction of application-oriented energetic materials. [ABSTRACT FROM AUTHOR]

The prediction of crystal structures is of great significance in the community of energetic materials since the crystal packing pattern is closely linked to the energetic and safety features. Here, we developed a tailor-made polarizable force field (TMPFF) calibrated by the benchmarks obtained from ab initio methods and applied it with the USPEX evolutionary algorithm to predict crystal structures from energetic molecular structures. The results showed that the prediction results of three polymorphs of hexaazaisowurtzitane (CL-20) all agree well with experimental measurements, for both lattice parameters and packing patterns. This work provides a new solution to predict polymorphs of CHON-based energetic molecules with high accuracy and good efficiency and shall accelerate the cognition process of new energetic crystals by theoretical approaches prior to experimental synthesis. [ABSTRACT FROM AUTHOR]