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Purpose The purpose of this paper is to verify the feasibility of lunar capture braking through three methods based on particle swarm optimization (PSO) and compare the advantages and disadvantages of the three strategies by analyzing the results of the simulation. Design/methodology/approach The paper proposes three methods to verify capture braking based on PSO. The constraints of the method are the final lunar orbit eccentricity and the height of the final orbit around the Moon. At the same time, fuel consumption is used as a performance indicator. Then, the PSO algorithm is used to optimize the track of the capture process and simulate the entire capture braking process. Findings The three proposed braking strategies under the framework of PSO algorithm are very effective for solving the problem of lunar capture braking. The simulation results show that the orbit in the opposite direction of the trajectory has the most serious attenuation at perilune, and it should consume the least amount of fuel in theoretical analysis. The methods based on the fixed thrust direction braking and thrust uniform rotation braking can better ensure the final perilune control accuracy and fuel consumption. As for practice, the fixed thrust direction braking method is better realized among the three strategies. Research limitations/implications The process of lunar capture is a complicated process, involving effective coordination between multiple subsystems. In this article, the main focus is on the correctness of the algorithm, and a simplified dynamic model is adopted. At the same time, because the capture time is short, the lunar curvature can be omitted. Furthermore, to better compare the pros and cons of different braking modes, some influence factors and perturbative forces are not considered, such as the Earth’s flatness, light pressure and system noise and errors. Practical implications This paper presents three braking strategies that can satisfy all the constraints well and optimize the fuel consumption to make the lunar capture more effective. The results of comparative analysis demonstrate that the three strategies have their own superiority, and the fixed thrust direction braking is beneficial to engineering realization and has certain engineering practicability, which can also provide reference for lunar exploration orbit design. Originality/value The proposed capture braking strategies based on PSO enable effective capture of the lunar module. During the lunar exploration, the capture braking phase determines whether the mission will be successful or not, and it is essential to control fuel consumption on the premise of accuracy. The three methods in this paper can be used to provide a study reference for the optimization of lunar capture braking.

This paper aims to address attitude stabilization control of an underactuated post-capture combination and achieve test validation based on the magnetism–buoyancy hybrid micro-gravity experiment system (MBHMES). Using a precise reduced model, a cascade control law is proposed to solve the issue theoretically. First, an advanced quaternion-based kinematic control law is proposed to stabilize the attitude of the non-symmetric post-capture combination. This control law can overcome the singularity brought about by the small initial attitude under the consideration of uncontrolled angular velocity. Then, considering the existence of multiple disturbances introduced by the fluid flow and electromagnetic force, an effective adaptive dynamic controller is proposed to meet the attitude requirement and reduce the negative effect of the multiple disturbances. For test demonstrations, a scenario of a base body capturing the target with robotics, to achieve attitude stabilization expectation, is described. Simulation results demonstrate the performance of the cascade control laws and test data show the effectiveness of the proposed method.

The problem of propellant-efficient station-keeping using a hybrid sail in the Earth–Moon system is investigated in this paper. To achieve high-precision station-keeping and minimize propellant consumption, the problem is addressed from perspectives of reference orbits design and control strategy design. A high-fidelity model of a hybrid sail, which consists of a solar electric propulsion (SEP) system and a solar sail covered by reflectivity control devices (RCDs), is exploited for reference orbits design in the Earth–Moon system using numerical methods. These hybrid-sail perturbed halo and Lyapunov orbits are parameterized by the sail’s reflectivity and are inherent unstable. An orbit-attitude control strategy is proposed for station-keeping which is composed of three parts: a nonlinear disturbance observer (NDO)-based optimal periodic orbital controller, SEP acceleration optimization, and a NDO-based robust backstepping attitude controller. In particular, RCDs are used in both orbital control and attitude control. Numerical results show that the proposed control strategy can guarantee high-precision station-keeping and effective reduction in propellant consumption.

In this paper, motion planning and coordination is investigated for a space robot composed of a floating pedestal and manipulator. In some cases, such as a manipulator grasping a higher quality target, the dynamic coupling can occur leading to under-actuation of the floating pedestal (that is, the required control force of the pedestal exceeds the thrust limit). As a result, the desired operation may not be achieved due to large control error. Therefore, we propose an innovative planning method, termed dynamic scaling planning method, to avoid pedestal under-actuation and guarantee accuracy of manipulator operations. Furthermore, to validate the proposed method, an experimental model of a space robot operating in a magnetic-liquid hybrid suspension microgravity simulation environment was developed. Results of the experimental simulations demonstrate that the proposed method can effectively avoid under-actuation of the pedestal. Moreover, the end-effector of the manipulator follows a desired path to successfully reach its target location.

With the increasing frequency of space activities, non-cooperative targets such as space debris have brought great hidden dangers to spacecraft operation. How to estimate their state has gradually become the main demand. This paper introduces a new method for estimating the angular velocity value as well as the axis of rotation of non-cooperative targets based on deep neural networks. The proposed algorithm has three parts: (1) use of the convolutional neural network (CNN) YOLO model, which is trained to identify the non-cooperative targets in the images taken by the camera, (2) extracting ORB features in the detected pixel regions of the non-cooperative target and using simultaneous localization and mapping (SLAM) to calculate the rotation matrix and translation matrix of the non-cooperative target relative to the camera, and (3) using the angular velocity value calculated by the Rodrigues equation to estimate the time of loop closure in SLAM. In order to calculate the axis of rotation, the plane fitting and spatial arc fitting are used. Combined with ground test, the experimental results indicate the error of the measured speed value of this algorithm is 0.0081rad/s, and the maximum error of the rotation axis is 5.12° which show the correctness of the algorithm and the potential of online application.

Accurate classification and identification of the detected terrain is the basis for the long-distance patrol mission of the planetary rover. But terrain measurement based on vision and radar is subject to conditions such as light changes and dust storms. In this paper, under the premise of not increasing the sensor load of the existing rover, a terrain classification and recognition method based on vibration is proposed. Firstly, the time-frequency domain transformation of vibration information is realized by fast Fourier transform (FFT), and the characteristic representation of vibration information is given. Secondly, a deep neural network based on multi-layer perception is designed to realize classification of different terrains. Finally, combined with the Jackal unmanned vehicle platform, the XQ unmanned vehicle platform, and the vibration sensor, the terrain classification comparison test based on five different terrains was completed. The results show that the proposed algorithm has higher classification accuracy, and different platforms and running speeds have certain influence on the terrain classification at the same time, which provides support for subsequent practical applications.

In network measurement, one-way delay is an important network performance indicators, during the measurement, to synchronize the time of different nodes has a great influence on the results of measurement, especially in the large-scale network measurement. This paper propose a time synchronization stability comparison method based on NTP after research the time synchronization technology. In the one-way delay measurement, in order to eliminate measurement errors caused by the frequency difference between nodes, this paper propose a one-way delay corrective algorithm based on forward and reverse delay measurement after research existing time corrective algorithm. Finally, the measurement platform deployed in Planetlab and measure one-way delay. The measurement select more stable nodes based on NTP stability comparison algorithm, and correct one-way delay measurement results through clock corrective algorithm mentioned above. Experiments show that the results of corrective measurement algorithm are more accurate.

The current network emulators commonly have bad versatility and their emulation fidelity is not high. Based on the principle of network emulation, this essay designs and implements a network path quality indicators emulation system, which can be transparently series deployed on a network path and change the performance of this path. The main tasks are: achieves a "Transparent Bridge" on Windows platform, using WinPcap packet capturer, gives the quality indicators emulation algorithms and models, studies the relationships between the indicators of network quality and the emulation algorithms has been improved and optimized based on this, designs, implements and verifies the emulation system. The emulation system has good cross-platform features, it achieves the frame-level emulation in the link layer, so it has a high fidelity.