With the development of aerospace technologies, the mission planning of agile earth observation satellites has to consider several objectives simultaneously, such as profit, observation task number, image quality, resource balance, observation timeliness. In this paper, a five-objective mixed-integer optimization problem is formulated for agile satellite mission planning. Preference-based multiobjective evolutionary algorithms, i.e., TMOEA/ D-TCH, T-MOEA/D-PBI and T-NSGA-III are applied to solve the problem. Problem-specific coding and decoding approaches are proposed based on heuristic rules. Experiments have shown the advantage of integrating preferences in many-objective satellite mission planning. A comparative study is conducted with other state-of-the-art preference-based methods (T-NSGAII, T-RVEA, MOEA/D-c). Results have demonstrated that the proposed T-MOEA/D-TCH has the best performance with regard to IGD and elapsed runtime. An interactive framework is also proposed for the decision maker to adjust preferences during the search. We have exemplified that a more satisfactory solution could be gained through the interactive approach.

With the continuous progress of intelligent power system technology, in order to meet the needs of substation operation and maintenance, a target detection algorithm is applied to identify the status of equipment switches. YOLOv7, as the latest achievement of YOLO (You Only Look Once) series algorithms, has good speed and accuracy in target detection tasks. However, when the generalized network is applied in a specific scenario, its advantages are not obvious due to its high weight and poor portability. In this paper, an improved GF-YOLOv7 network model is proposed to apply in the recognition of the status of bounce locks in a substation. The MobileViT module is used to improve the feature extraction ability of the backbone network. Referring to the CBAM feature attention mechanism, the channel attention module and the spatial attention module are used to design a more lightweight feature fusion network. The experimental results in the test set show that the proposed network can significantly reduce the network weight and improve the detection accuracy on the basis of a small reduction in the detection speed, and the accuracy reaches 97.8%, which can meet the needs of the detection task of substation bounce locks.

The study of unmanned ground vehicles (UGVs) operating under unstructured roads is of great significance to intelligent transportation, agricultural development and military technology. In order to ensure reliable and stable operation of UGVs on unstructured terrain, it is necessary to identify the current road terrain and perform vehicle stability adjustment. Road terrain identification is a prerequisite for stability control. Most of the existing road terrain identification methods use a single vehicle sensor, which has the problem that complex algorithms need to be applied for data processing, which reduces the real-time performance. Moreover, the single sensor is weak in anti-interference and limited in recognizing the road. To address these problems, a method is proposed to collect vehicle motion data using on-board gyroscope sensors and velocity sensors. Back propagation (BP) neural network is used to identify the category of the road. For the problem that the conventional proportional-integral differential (PID) algorithm cannot be adapted to different road stability control, a multi-loop adaptive proportional-integral differential (PID) control system with the velocity loop as the outer loop and the torque (current) loop as the inner loop is proposed. In order to verify the feasibility and effectiveness of the method, experiments are conducted on a UGV using robot operating system (ROS), and the results verify the feasibility and superiority of the road identification and stability control method proposed in this paper. It provides a good theoretical basis and valuable technical guidance for the UGV operation and control on unstructured roads.

Unmanned surface vehicles (USVs) rely on multi-sensor perception, such as radar, LiDAR, GPS, and vision, to ensure safe and efficient navigation in complex maritime environments. Traditional ant colony optimization (ACO) for path planning, however, suffers from premature convergence, slow adaptation, and poor smoothness in cluttered waters, while the dynamic window approach (DWA) without global guidance can become trapped in local obstacle configurations. This paper presents a sensor-oriented hybrid method that couples an improved ACO for global route planning with an enhanced DWA for local, real-time obstacle avoidance. In the global stage, the ACO state–transition rule integrates path length, obstacle clearance, and trajectory smoothness heuristics, while a cosine-annealed schedule adaptively balances exploration and exploitation. Pheromone updating combines local and global mechanisms under bounded limits, with a stagnation detector to restore diversity. In the local stage, the DWA cost function is redesigned under USV kinematics to integrate velocity adaptability, trajectory smoothness, and goal-deviation, using obstacle data that would typically originate from onboard sensors. Simulation studies, where obstacle maps emulate sensor-detected environments, show that the proposed method achieves shorter paths, faster convergence, smoother trajectories, larger safety margins, and higher success rates against dynamic obstacles compared with standalone ACO or DWA. These results demonstrate the method’s potential for sensor-based, real-time USV navigation and collision avoidance in complex maritime scenarios.