Cross-domain cooperative task allocation is a complex and challenging issue in the field of multi-agent task allocation that requires urgent attention. This paper proposes a task allocation method based on the multi-strategy improved dung beetle optimization (MSIDBO) algorithm, aiming to solve the problem of fully distributed multi-agent cross-domain cooperative task allocation. This method integrates two key objective functions: target allocation and control allocation. We propose a target allocation model based on the optimal comprehensive efficiency, cluster load balancing, and economic benefit maximization, and a control allocation model leveraging the radar detection ability and control data link connectivity. To address the limitations of the original dung beetle optimization algorithm in solving such problems, four revolutionary strategies are introduced to improve its performance. The simulation results demonstrate that our proposed task allocation algorithm significantly improves the cross-domain collaboration efficiency and meets the real-time requirements for multi-agent task allocation on various scales. Specifically, our optimization performance was, on average, 32.5% higher compared to classical algorithms like the particle swarm optimization algorithm and the dung beetle optimization algorithm and its improved forms. Overall, our proposed scheme enhances system effectiveness and robustness while providing an innovative and practical solution for complex task allocation problems.

The rapid development of e-commerce has brought about a sharp increase in the demand for express packaging materials, which in turn has led to problems such as resource waste and environmental pollution. Under the target of “dual carbon”, the urgency and importance of effective classification and recycling of express packaging waste cannot be ignored. Based on the unified theory of technology acceptance and use (UTAUT) model and the characteristics of express packaging, this study selected performance expectancy, effort expectancy, social influence and perceived value as core variables, and gender, age and frequency as moderating variables, and established a structural equation through survey and analysis to study the factors influencing consumers’ willingness to recycle express packaging. The results of the study showed that performance expectancy, effort expectancy, social influence and perceived value had a significant positive effect on consumers’ willingness to classify. Classification willingness had a significant positive effect on consumers’ classification behavior. In addition to directly influencing classification intention, performance expectancy, effort expectancy and social influence also have mediating effects. In addition, gender played a moderating role in the effects of performance expectancy and social influence on classification intention. Based on the findings, this study proposes targeted measures to promote the green and healthy development of the express delivery industry.

The use of express packaging and its recycling produces large amounts of carbon dioxide. In order to achieve China’s “dual carbon” goal, this study adopted a literature research method to explore the idea of intelligent express packaging recycling cabinets. Based on the current design and use of intelligent express packaging recycling cabinets, new ideas for their improvement are proposed. This study also explored methods for improving people’s willingness to use intelligent express packaging recycling cabinets through experimental research and quantitative analysis. The results show that a reward mechanism has a significant effect on people’s willingness to use intelligent express packaging recycling cabinets. Of the two types of rewards, immediate rewards, compared to delayed rewards, can further increase people’s use of intelligent express packaging recycling cabinets. Gain and loss trade-offs play a mediating role between a reward mechanism and people’s willingness to use it, and consumers make that choice after weighing up the advantages and disadvantages. If consumers feel that it is worthwhile to protect the environment, in terms of the rewards they obtain compared to the time and effort they have to spend using intelligent express packaging recycling cabinets, and that the gain outweighs the loss, they will be inclined to use this system. Environmental responsibility plays a moderating role in mediating the trade-off between gains and losses. In the context of low environmental responsibility, cash rewards lead to greater gain and loss trade-offs compared to point rewards, while in the context of high environmental responsibility, there is no difference between cash rewards and point rewards. This study provides ideas for the design and promotion of the use of intelligent express packaging recycling cabinets, with the goal of effectively improving the recycling rate of express packaging waste.

The aim of this paper is to present a novel sliding mode control scheme for the supercavitating vehicle trajectory tracking problem that subjects to external disturbances and actuator saturation with two symmetric elevators and a cavitator as actuators by analytical methods and computer simulations. Firstly, the nonlinear and highly coupled dynamic and kinematic models of a supercavitating vehicle are presented in a comprehensive way by taking the cavity memory effect and time‐variant planing force into consideration. The PSO algorithm is employed to optimize the control parameters for achieving better and more practical tracking performance by minimizing the objective function. A second‐order extended state observer (ESO) is utilized to estimate the unknown external and state‐dependent disturbances and compensates for control inputs. In addition, an antiwindup compensator is adopted to cope with actuator saturation. Finally, the proposed control scheme is employed for complex trajectory tracking of a supercavitating vehicle under various conditions by conducting comparative numerical simulations. Rigorous theoretical analysis and simulation results indicate that the proposed control scheme can achieve satisfactory tracking performance and have a good capability of robustness.

This paper addresses the lateral motion control of a supercavitating vehicle and studies its ability to maneuver. According to the unique hydrodynamic characteristics of the supercavitating vehicle, highly coupled nonlinear 6-degree-of-freedom (DOF) dynamic and kinematic models are constructed considering time-delay effects. A control scheme utilizing radial basis function (RBF) neural-network-(NN)-based adaptive sliding with planing force avoidance is proposed to simultaneously control the longitudinal stability and lateral motion of the supercavitating vehicle in the presence of external ocean-induced disturbances. The online estimation of nonlinear disturbances is conducted in real time by the designed NN and compensated for the dynamic control laws. The adaptive laws of the NN weights and control parameters are introduced to improve the performance of the NN. The least squares method is utilized to solve the actuator control efforts with rolling restriction in real-time online. Rigorous theoretical proofs based on the Lyapunov theory prove the globally asymptotic stability of the proposed controller. Finally, numerical simulations were performed to obtain maximum maneuverability and verify the effectiveness and robustness of the proposed control scheme.

In the absence of a priori knowledge about target motion characteristics, the task of tracking complex maneuvering targets remains challenging. A multi-model polynomial-based complex target tracking method is presented to address this issue. Observation sequences of varying lengths are fitted by time polynomials of different orders, which are used to create a set of target motion models. Subsequently, the multi-model framework is employed to track maneuvering targets with uncertain motion characteristics. To verify the effectiveness of the suggested approach, three datasets were created with kinematic equation, the gazebo platform and real watercrafts. Based on the above three datasets, the proposed method is compared with classical multi-model methods and a deep learning method. Theoretical analysis and experimental results reveal that, in the lack of a priori information of target maneuvering features, the tracking error of the proposed method can be reduced by 12.5~30% compared with the traditional MM method. Moreover, the proposed method is able to overcome the problem of accuracy degradation caused by model misalignment and parameter tuning faced by the deep learning based methods.