To fully harness the burgeoning array of in-orbit satellite resources and augment the efficacy of dynamic surveillance of densely clustered terrestrial targets, this paper delineates the following methodologies. Initially, we leverage the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to aggregate the concentrated terrestrial targets, taking into account the field-of-view peculiarities of agile staring satellites. Subsequently, we architect a model for a synergistic multiple angle earth observation satellites (AEOSs) mission planning with the optimization objectives of observational revenue, minimal energy expenditure, and load balancing, factoring in constraints such as target visibility time window, AEOSs maneuverability, and satellite storage. To tackle this predicament, we propose an improved heuristic ant colony optimization (ACO) algorithm, utilizing the task interval, task priority, and the length of time a task can start observation as heuristic information. Furthermore, we incorporate the notion of the max–min ant system to regulate the magnitude of pheromone concentration, and we amalgamate global and local pheromone update strategies to expedite the convergence rate of the algorithm. We also introduce the Lévy flight improved pheromone evaporation coefficient to bolster the algorithm’s capacity to evade local optima. Ultimately, through a series of simulation experiments, we substantiate the significant performance improvements achieved by the improved heuristic ant colony algorithm compared to the standard ant colony algorithm. We furnish proof of its efficacy in resolving the planning of multiple AEOS staring observation missions.

Agile satellite imaging scheduling plays a vital role in improving emergency response, urban planning, national defense, and resource management. With the rise in the number of in-orbit satellites and observation windows, the need for diverse agile earth observation satellite (AEOS) scheduling has surged. However, current research seldom addresses multiple optimization objectives, which are crucial in many engineering practices. This paper tackles a multi-objective agile earth observation satellite scheduling problem (MOAEOSSP) that aims to optimize total observation task profit, satellite energy consumption, and load balancing. To address this intricate problem, we propose a multi-strategy fusion dung beetle optimization (SFMODBO) algorithm. This novel algorithm harnesses the position update characteristics of various dung beetle populations and integrates multiple high-adaptability strategies. Consequently, it strikes a better balance between global search capability and local exploitation accuracy, making it more effective at exploring the solution space and avoiding local optima. The SFMODBO algorithm enhances global search capabilities through diverse strategies, ensuring thorough coverage of the search space. Simultaneously, it significantly improves local optimization precision by fine-tuning solutions in promising regions. This dual approach enables more robust and efficient problem-solving. Simulation experiments confirm the effectiveness and efficiency of the SFMODBO algorithm. Results indicate that it significantly outperforms competitors across multiple metrics, achieving superior scheduling schemes. In addition to these enhanced metrics, the proposed algorithm also exhibits advantages in computation time and resource utilization. This not only demonstrates the algorithm’s robustness but also underscores its efficiency and speed in solving the MOAEOSSP.

This study investigates the trajectory-tracking control problem of a two-degree-of-freedom underwater manipulator operating in a complex disturbance environment. A dynamic model of the multi-link serial manipulator is first established. In this study, water resistance and additional mass forces acting on the manipulator are analyzed and calculated using differential analysis and the Morrison formula. To account for coupling between joints, the concept of equivalent gravity is introduced to precisely calculate the underwater manipulator’s buoyancy and gravity. As a result, a relatively accurate dynamic model of the underwater manipulator is established. To mitigate the influences of external disturbances and unmodeled parts on the manipulator, a finite-time extended state observer (FTESO) is designed to estimate system quantities that are difficult to measure directly. The robustness of the controller is enhanced using a feedforward compensation mechanism, and it is demonstrated that the observation error of the observer converges in finite time. Finally, a global fast terminal sliding mode controller (GFTSMC) is developed for trajectory tracking, integrated with the aforementioned observer, and designed to smooth and limit the controller’s output. The controller’s stability is proven using Lyapunov stability theory, and its effectiveness is verified through simulation-based comparison experiments.

Due to the increasing demand for remote sensing imaging products, the agile earth observation satellite scheduling problem (AEOSSP) has garnered significant attention. In response, this paper proposes a reinforcement learning-based dung beetle optimization (RLDBO) algorithm to address AEOSSP. The proposed method dynamically adjusts the proportions of four types of dung beetles (ball-rolling beetle, brood ball beetle, small dung beetle, and thief beetle), enabling adaptive optimization of the scheduling scheme to better handle the complexities and uncertainties of the search space. The Q-learning mechanism guides the adjustment of these proportions, effectively balancing global exploration and local exploitation at different stages of the search process. Experimental results demonstrate that the RLDBO algorithm effectively solves AEOSSP across multiple instances, and it outperforms other algorithms in various aspects, including optimization performance, convergence speed, and scheduling effectiveness. The experimental validation confirms that RLDBO significantly enhances the efficiency and effectiveness of agile earth observation satellite scheduling.

With advancements in inertial navigation measurement units (IMUs), the focus of inertial navigation accuracy has shifted from hardware limitations to algorithm performance. To effectively test algorithms using high-precision IMUs, trajectory generators are essential; however, existing generators often lack diverse motion patterns, making them inadequate for evaluating algorithms under complex and challenging conditions, particularly for unmanned aerial vehicle (UAV) applications. To address this, we designed a high-precision trajectory generator that enhances traditional models by incorporating coning motion, paddle motion, and coning motion with angular velocity precession to simulate high-dynamic environments. Additionally, a one-sample-plus-one-previous-per-update algorithm was developed to improve the accuracy of the IMU output inversion by generating precise gyroscope and accelerometer data for processing within inertial navigation systems. The results demonstrate that both the one-sample-plus-one-previous-per-update and three-sample algorithms significantly improve the navigation accuracy under high-dynamic motion compared to single-sample algorithms. This trajectory generator effectively validates the accuracy of inertial navigation algorithms in complex conditions, particularly for UAVs, and provides a solid foundation for testing higher-precision algorithms.The proposed methodology directly supports the development of advanced actuator control systems in UAVs by enabling rigorous validation of navigation algorithms under realistic high-dynamic scenarios, a critical requirement for next-generation autonomous platforms.