Electric solar wind sail (E-sail) is an innovative propulsion system, which can generate low and continuous thrust without any propellant by reflecting ions from the solar wind. Because the propulsive acceleration of E-sail is low and continuous, its trajectory is usually characterized by long flight time. In order to explore more flight plans, rapid trajectory design is useful for preliminary mission analysis and design. This paper presents a method for rapid generation of minimum-time, three-dimensional trajectories for an E-sail using the finite Fourier series shapebased method. Unlike the more conventional electric thruster, the thrust vector of an E-sail is constrained. In order to consider the characteristics of the E-sail thrust, inequality constraints of thrust acceleration are investigated based on a recent thrust model. The numerical simulation results show that the proposed method is useful to quickly design the flight trajectory of the E-sail by considering the actual characteristics of the thrust vector

To improve the overall performance of mission planning for the unmanned aerial vehicles (UAVs), a multistage path prediction (MPP) for trajectory generation with rendezvous is addressed in this paper. The proposed real-time algorithm consists of four stages: path estimation, path planning, flyable trajectory generation, and trajectory modification for rendezvous. In every planning horizon, each UAV utilizes the local A* algorithm to estimate all probable paths and then the results serve as input for the task assignment system. A simple assignment algorithm is briefly introduced to validate the effectiveness of MPP. Based on the assignment, the polygonal paths are further obtained by using the global A* algorithm. Then these paths are smoothed to be flyable by using the cubic b-spline curve. In the last stage, the trajectories are modified for rendezvous of the UAVs to execute specific many-to-one tasks. Results of different stages are continuously revised and delivered to the task assignment syste...

Moving rapidly and agilely in high-pressure environment is a challenge for existing miniature underwater robots, which limits deeply the ocean exploitation. Here, we present a spherical underwater robot with diameter of 7 cm, and it has merits of miniature structure without bulky and antihydropressure devices, 3-DOFs agile motion, and good adaptability for 25 MPa environment simultaneously. Four designed piezoelectric double-jet actuators are embedded directly in the spherical shell without transmission mechanism, which enables the robot with relatively fast motion speed by compared with the published robots. Six motion modes are implemented by the cooperation of actuators, including rising, hovering, and sinking motions in the vertical direction and straight driving, turning, and in-situ turning motions in the horizontal plane. A robot prototype was fabricated, and its performance was tested. Experimental results indicated that the rising, straight driving, and in-situ turning speed reached up to 95.2 mm/s (1.36 BL/s), 99.6 mm/s (1.42 BL/s), and 5 rad/s, respectively. Besides, the robot could also rapidly turn at 1.3 rad/s with the turning radius of 22.1 mm. The speed deviation within 10% under different water pressures was obtained. The obstacle avoidance was successfully fulfilled by controlling the robot to pass through the narrow gap with width of only 1.5 body length of robot. There excellent characteristics demonstrate agile mobility and give the robot ability to move in the confined area of deep sea.

The privacy and security of industrial data are receiving increasing attention. Digital twins can establish realistic cyber information models for industrial equipment. However, it is challenging to transfer diagnostic knowledge from low-cost equipment, such as normal ball bearings, to high-cost equipment, such as high-speed aerospace roller bearings, during the diagnostic process. We have developed a paradigm for establishing digital twin diagnostic models across devices with source model and a few target samples. We consider the privacy and security of source data in the transfer learning process, as well as the problem of excessive domain drift. First, we use fault data from low-cost normal ball bearings to generate the corresponding fault diagnosis model. During the process of transferring diagnostic knowledge to aerospace roller bearings, we ensure that the source data remains inaccessible. To address the issue of excessive domain drift, we introduce a small number of labeled target samples to establish a supervised link between the model and the target domain. Information maximization is employed to enhance the extraction of knowledge from these labeled samples. Second, we compute similarity and dissimilarity matrices among target domain samples. Contrastive learning is utilized to strengthen the connections within similar target samples and to distinguish dissimilar ones. Subsequently, we leverage information maximization to make reasonable inferences within the target domain. However, since information maximization focuses only on the overall uniformity of the domain, it inherently weakens the support set’s guidance to the query set. To overcome this limitation, we propose a mixed uniformity loss function to further improve the reliability of uniformity predictions for unlabeled target samples. We validate our approach by transferring diagnostic knowledge from a deep groove ball bearing dataset to two specialized aerospace roller bearing datasets. Comparative experiments demonstrate that the proposed CDT method outperforms existing approaches.

This study focuses on the online thrust-loss state identification and trajectory optimization problem during the asteroid probe powered descent process where thrust-loss fault occurs. First, according to the probe's thruster configuration structure, a powered descent dynamic model of the probe and a trajectory optimization model with thrust-loss constraints are established. In addition, this paper proposes an online thrust-loss identification model based on the moving horizon estimation method. By combining the results of online thrust-loss identification, the trajectory replanning problem considering thrust-loss constraints is researched on the Bezier shape-based method, which can quickly generate feasible and relatively optimal trajectories. Finally, numerical simulations and analyses are performed to verify the effectiveness of the thrustloss identification model and the probe powered descent trajectory replanning strategy proposed in this paper. The proposed methods can ensure the soft-landing of the probe on a predetermined target, even if a thrust-loss fault occurs during the probe powered descent process