Docking Phase Research Articles

초소형위성 지상 환경 도킹 시험

In this paper, we describe the results of the docking phase test in the ground environment of the rendezvous/docking technology verification satellite under development for the first time in Korea. rendezvous/docking technology is a high-level technology in space technology, which is also very important for accessing and performing tasks on relative objects in space orbit. In this paper, we describe the ground test results that the chaser finally docks the fixed target using an air bearing device. Based on the thrust control algorithm in the docking phase and the relative object recognition and relative distance estimation algorithm using visual-based sensors validated in this paper, we intend to use them for later expansion to rendezvous/docking algorithms in three-dimensional space for testing in space.

Membrane Interactions of the Peroxisomal Proteins PEX5 and PEX14.

Human PEX5 and PEX14 are essential components of the peroxisomal translocon, which mediates import of cargo enzymes into peroxisomes. PEX5 is a soluble receptor for cargo enzymes comprised of an N-terminal intrinsically disordered domain (NTD) and a C-terminal tetratricopeptide (TPR) domain, which recognizes peroxisomal targeting signal 1 (PTS1) peptide motif in cargo proteins. The PEX5 NTD harbors multiple WF peptide motifs (WxxxF/Y or related motifs) that are recognized by a small globular domain in the NTD of the membrane-associated protein PEX14. How the PEX5 or PEX14 NTDs bind to the peroxisomal membrane and how the interaction between the two proteins is modulated at the membrane is unknown. Here, we characterize the membrane interactions of the PEX5 NTD and PEX14 NTD in vitro by membrane mimicking bicelles and nanodiscs using NMR spectroscopy and isothermal titration calorimetry. The PEX14 NTD weakly interacts with membrane mimicking bicelles with a surface that partially overlaps with the WxxxF/Y binding site. The PEX5 NTD harbors multiple interaction sites with the membrane that involve a number of amphipathic α-helical regions, which include some of the WxxxF/Y-motifs. The partially formed α-helical conformation of these regions is stabilized in the presence of bicelles. Notably, ITC data show that the interaction between the PEX5 and PEX14 NTDs is largely unaffected by the presence of the membrane. The PEX5/PEX14 interaction exhibits similar free binding enthalpies, where reduced binding enthalpy in the presence of bicelles is compensated by a reduced entropy loss. This demonstrates that docking of PEX5 to PEX14 at the membrane does not reduce the overall binding affinity between the two proteins, providing insights into the initial phase of PEX5-PEX14 docking in the assembly of the peroxisome translocon.

High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm.

In order to detect gravitational waves and characterise their sources, three laser links were constructed with three identical satellites, such that interferometric measurements for scientific experiments can be carried out. The attitude of the spacecraft in the initial phase of laser link docking is provided by a star sensor (SSR) onboard the satellite. If the attitude measurement capacity of the SSR is improved, the efficiency of establishing laser linking can be elevated. An important technology for satellite attitude determination using SSRs is star identification. At present, a guide star catalogue (GSC) is the only basis for realising this. Hence, a method for improving the GSC, in terms of storage, completeness, and uniformity, is studied in this paper. First, the relationship between star numbers in the field of view (FOV) of a staring SSR, together with the noise equivalent angle (NEA) of the SSR—which determines the accuracy of the SSR—is discussed. Then, according to the relationship between the number of stars (NOS) in the FOV, the brightness of the stars, and the size of the FOV, two constraints are used to select stars in the SAO GSC. Finally, the performance of the GSCs generated by Decision Trees (DC), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), the Magnitude Filter Method (MFM), Gradient Boosting (GB), a Neural Network (NN), Random Forest (RF), and Stochastic Gradient Descent (SGD) is assessed. The results show that the GSC generated by the KNN method is better than those of other methods, in terms of storage, uniformity, and completeness. The KNN-generated GSC is suitable for high-accuracy spacecraft applications, such as gravitational detection satellites.

嫦娥五号月球轨道交会对接制导导航与控制系统

<p indent="0mm">The Chang’e-5 has carried out China’s first autonomous rendezvous and docking in lunar orbit. The guidance, navigation, and control system (GNC) plays an important role in this achievement. This paper introduces the composition of the GNC system and its working principles, in addition to rendezvous and docking (RVD) phase partition and the key technology involved. In addition, the first RVD flight result and its evaluation are presented.

A stochastic approximation method for probability prediction of docking success for aerial refueling

Aerial refueling is capable of increasing the endurance and flight range of aircraft. However, during the docking phase of aerial refueling, the docking risk increases as the receiver aircraft approaches the tanker aircraft. A high docking risk indicates docking failure or even flight collision. In order to guarantee docking reliability and safety, a stochastic approximation method is proposed to predict the docking success probability during the docking phase of aerial refueling. If the success probability is lower than the specified value, the receiver aircraft needs to increase its relative distance from the tanker aircraft to reduce the risk. In our method, a stochastic differential equation is used to model the relative motion between the receiver and tanker aircrafts, where the receiver aircraft flies along a predefined flight path with influence of additive wind perturbations. The correlation between the magnitude of wind perturbations and the distance between the receiver and tanker aircrafts is considered. Then, based on the established relative motion model, the probability that the receiver aircraft enters the target set during a given time interval is predicted by the Markov chain stochastic approximation method. The docking success probability is computed by propagating the transition probabilities of the Markov chain backwards starting from the target set during the time interval. Comparing with the widely used Monte Carlo method in previous studies, our method demonstrates substantial effectiveness and efficiency.

GPU-Accelerated Flexible Molecular Docking.

Virtual screening is a key enabler of computational drug discovery and requires accurate and efficient structure-based molecular docking. In this work, we develop algorithms and software building blocks for molecular docking that can take advantage of graphics processing units (GPUs). Specifically, we focus on MedusaDock, a flexible protein-small molecule docking approach and platform. We accelerate the performance of the coarse docking phase of MedusaDock, as this step constitutes nearly 70% of total running time in typical use-cases. We perform a comprehensive evaluation of the quality and performance with single-GPU and multi-GPU acceleration using a data set of 3875 protein-ligand complexes. The algorithmic ideas, data structure design choices, and performance optimization techniques shed light on GPU acceleration of other structure-based molecular docking software tools.

Automatic orbital docking with tumbling target using sliding mode control

The present study aimed to propose translational and rotational control of a chaser spacecraft in the close vicinity docking phase with a target subjected to external disturbances. For this purpose, two sliding mode controls (SMC) are developed to coordinate the relative position and attitude of two spacecraft. The chaser is guided to the tumbling target by the relative position control, approaching in the direction of the target docking port. At the same moment, the relative attitude control coordinates the chaser attitude so that it can be aligned with the target orientation. These control systems regulate the relative translational and rotational velocities to be zero when two spacecraft are docking. The robustness of the closed-loop system in the presence of external disturbances, measurement noises and uncertainties is guaranteed by analyzing and calculating the control gains via the Lyapunov function. The simulations in different scenarios indicated the effectiveness of the controller scheme and precise maneuver regarding the accuracy of docking conditions.

Reachability analysis on optimal trim state for aerial docking

Aerial refueling is an important capability to increase the endurance and flight range of aircraft, but it often suffers from a low success rate. The altitude and speed of the tanker aircraft in the docking phase play a great role in the docking success rate. According to this, the optimal trim state, namely the optimal speed and altitude of the tanker aircraft, is investigated through the reachability analysis method in this paper. The optimal problem is transformed to find the trim state corresponding to the maximum volume of the reachable set. First, a relative motion model of the receiver aircraft with respect to the drogue is proposed. Then, based on reachability analysis, an optimization problem is formulated and a solution procedure is given in detail. In the simulation, the volumes of reachable sets are plotted with respect to the given discrete speeds and altitudes, based on which the optimal trim state of the docking phase is determined. Finally, the determined optimal trim state is verified by using numerous docking control simulations and the degree of controllability from another aspect. The effectiveness of the proposed method is demonstrated.

Study on robust aerial docking mechanism with deep learning based drogue detection and docking

This paper proposes a simple and a robust bistable docking system with a deep learning based real-time drogue detection and tracking system for Unmanned Aircraft Systems (UAS) for mid-air autonomous aerial docking. Secure aerial docking mechanisms between the leader and follower aerial vehicles with effective drogue detection and tracking strategies are fundamental challenges during the air-to-air docking phase of autonomous aerial docking. To confront those issues, this paper not only presents the design of a bistable-based aerial docking mechanism, but also proposes effective deep learning based real-time drogue detection using a convolutional neural network (CNN) and real-time tracking algorithm using a point cloud algorithm. To ensure novelty and robustness for the aerial docking mechanism, a foldable bistable gripper-type mechanism is designed to increase the grasping performance with simplicity and adaptability. The proposed gripper acts as a drogue by itself to grasp a probe which is attached to the follower aerial vehicle. To employ an effective drogue detection method, the deep learning based real-time object detection algorithm, YOLOv3, is used to implement the drogue detection system. The proposed new probe-and-drogue type bistable docking system has the advantages of being simple and robust. The deep learning based real-time drogue detection method increases the detection rate. Moreover, the real-time tracking algorithm with a depth camera system does not require a GPS/INS system and many other sensors to follow the drogue movement in the air.

Robust hybrid supervisory control for spacecraft close proximity missions

We consider the problem of rendezvous, proximity operations, and docking of an autonomous spacecraft. The problem can be conveniently divided into three phases: (1) rendezvous phase; (2) docking phase; and (3) docked phase. On each phase the task to perform is different, and requires a different control algorithm. Angle and range measurements are available for the entire mission, but constraints and tasks to perform are different depending on the phase. Due to the different constraints, available measurements, and tasks to perform on each phase, we study this problem using a hybrid systems approach, in which the system has different modes of operation for which a suitable controller is to be designed. Following this approach, we characterize the family of individual controllers and the required properties they should induce to the closed-loop system to solve the problem within each phase of operation. Furthermore, we propose a supervisory algorithm that robustly coordinates the individual controllers so as to provide a solution to the problem. In addition, we present specific controller designs that appropriately solve the control problems for individual phases and validate them numerically.

STOCHASTIC MODEL PREDICTIVE CONTROL FOR SPACECRAFT RENDEZVOUS AND DOCKING VIA A DISTRIBUTIONALLY ROBUST OPTIMIZATION APPROACH

AbstractA stochastic model predictive control (SMPC) algorithm is developed to solve the problem of three-dimensional spacecraft rendezvous and docking with unbounded disturbance. In particular, we only assume that the mean and variance information of the disturbance is available. In other words, the probability density function of the disturbance distribution is not fully known. Obstacle avoidance is considered during the rendezvous phase. Line-of-sight cone, attitude control bandwidth, and thrust direction constraints are considered during the docking phase. A distributionally robust optimization based algorithm is then proposed by reformulating the SMPC problem into a convex optimization problem. Numerical examples show that the proposed method improves the existing model predictive control based strategy and the robust model predictive control based strategy in the presence of disturbance.

A Visual Navigation Framework for the Aerial Recovery of UAVs

This article proposes a visual navigation framework for the aerial recovery of unmanned aerial vehicles (UAVs). The framework is employed for pose estimation between the drogue and the probe to guide the recovery UAV to safely dock with the drogue. First, a deep learning-based detector and the proposed adaptive region of interest (AROI) tracker give the region of interest (ROI) of the drogue at high speed. Second, the centroids of the markers installed on the ring of the drogue can be extracted by image processing of the ROI, which can effectively reduce the computational overhead and the noise impact from other irrelevant areas. To improve the robustness of the framework, a marker compensation method is proposed to address situations of occlusion. After all the centroid coordinates in the image are obtained, stereo vision is employed to measure the drogue pose. Then, a Kalman filter (KF) is applied to accurately estimate the drogue pose. Finally, a ground semiphysical closed-loop experiment of the docking phase of aerial recovery is developed to verify the effectiveness of the proposed framework. The experimental results show that our framework has high accuracy, strong robustness, and good real-time performance.

Synthesis, in vitro evaluation, and computational simulations studies of 1,2,3-triazole analogues as DPP-4 inhibitors

Novel 1,2,3-triazole analogues (S7 ~ S10) were synthesized and evaluated for their inhibitory activity against hDPP-4. All the 1,2,3-triazole analogues exhibited moderate in vitro hDPP-4 inhibitory activities (265 ~ 780 nM). These results are somewhat less potent compared to those of known 1,2,3-triazole analogues (S1 ~ S6, 14 ~ 254 nM). S2 and S3 manifested excellent potency against hDPP-4 with IC50s of 28 and 14 nM, respectively. The role of the 1,2,3-triazole moiety in binding the molecule to the target was investigated using combined 10 1,2,3-triazole analogues (S1 ~ S10). Molecular dynamics (MD) simulations following the aforementioned docking phase were performed to elucidate potential binding modes of sitagliptin’s 1,2,3-triazole analogues in hDPP-4, with the use of a cocrystal structure of hDPP-4 with sitagliptin (PDB ID: 1X70). Docking and MD simulations of the complexes of hDPP-4 with sitagliptin, S2 and S3 suggest that Glu205, Glu206, Tyr662, and Tyr666 would be the key amino acid residues for the binding of the molecules with the receptor. Especially, S2 and S3 showed additional strong π-π interaction between Phe357 and 1,2,3-triazole. Same strong π-π interaction is also observed between Phe357 and the 1,2,4-triazole ring of sitagliptin. Furthermore, additional interactions with Tyr547, Cys551, and especially Arg358 would enhance the binding affinity of the compounds for the pocket of the enzyme. In overall, in vitro hDPP-4 inhibitory activities of synthetic 1,2,3-triazole analogues were well matched with results of computational simulations studies.

A Development of Docking Phase Analysis Tool for Nanosatellite

In order to avoid the high cost and high risk of demonstration mission of rendezvous-docking technology, missions using nanosatellites have recently been increasing. However, there are few successful mission cases due to many limitations of nanosatellites like small size, power limitation, and limited performances of sensor, thruster, and controller. To improve the probability of rendezvous-docking mission success using nanosatellite, a rendezvous-docking phase analysis tool for nanosatellites is developed. The tool serves to analyze the relative position and attitude control of the chaser satellite at the docking phase. In this tool, the Model Predictive Controller (MPC) is implemented as a controller, and Extended Kalman Filter (EKF) is adopted as a filter for noise filtering. To verify the performance and effectiveness of the developed tool for nanosatellites, simulation study was conducted. Consequently, we confirmed that this tool can be used for the analysis of relative position and attitude control for nanosatellites in the rendezvous-docking phase.

Autonomous Target Docking of Nonholonomic Mobile Robots Using Relative Pose Measurements

This article investigates the target docking problem for nonholonomic mobile robots using relative position and orientation measurements of the target. Considering the fact that target docking operations generally happen in unstructured environments where obstacles are left lying around, the whole docking process is divided into two phases, namely the approaching phase and the autonomous docking phase. In the approaching phase, the robot navigates in the operational environment until it reaches a switching region around the target, which triggers the autonomous docking phase. To account for the noise-corrupted and intermittent target observations, an extended Kalman filter-based relative pose estimation algorithm is designed to estimate the position and orientation of the target in the local reference frame of the robot. To achieve a perfect docking operation in the autonomous docking phase, which requires a zero-impact angle and a zero-lateral offset at the final engagement stage, a practical solution is proposed where motion planning and tracking control of the robot are incorporated into a unified scheme. Unlike most of the existing docking approaches in the literature which only account for static targets, our proposed strategy addresses the moving target docking problem as well. Real robot experiments have been performed to demonstrate the effectiveness of the proposed method.

Docking Controller for Autonomous Aerial Refueling With Adaptive Dynamic Surface Control

The docking controller for autonomous aerial refueling (AAR) is intractable considering the high precision requirement and the complex disturbances of multiple environment flows. To solve the problems in the docking phase of AAR, such as the uncertainties of the aerodynamic parameters of receiver aircraft and the disturbances acting on the receiver aircraft, an adaptive dynamic surface control (ADSC) scheme based on radical basis function neural network (RBF-NN) is presented in this paper. Firstly, a nonlinear model of longitudinal dynamics of the receiver aircraft relative to the tanker aircraft is established, which incorporates the tanker vortex term. Secondly, a nonlinear strict-feedback form is introduced to design an adaptive dynamic surface controller with RBF-NN. Thirdly, the upper bounds of the “total disturbances” are estimated with the adaptive law, and the uncertain aerodynamic parameters of receiver aircraft are estimated with RBF-NN. It is proved that the proposed controller can guarantee the uniform boundedness of all the signals in the closed-loop system using Lyapunov theory. Finally, simulation results demonstrate the effectiveness of the proposed controller for the docking control of AAR.

Docking mechanism design and dynamic analysis for the GEO tumbling satellite

Purpose According to the requirements of servicing and deorbiting the failure satellites, especially the tumbling ones on geosynchronous orbit, this paper aims to design a docking mechanism to capture these tumbling satellites in orbit, to analyze the dynamics of the docking system and to develop a new collision force-limited control method in various docking speeds. Design/methodology/approach The mechanism includes a cone-rod mechanism which captures the apogee engine with a full consideration of despinning and damping characteristics and a locking and releasing mechanism which rigidly connects the international standard interface ring (Marman rings, such as 937B, 1194 and 1194A mechanical interface). The docking mechanism was designed under-actuated, aimed to greatly reduce the difficulty of control and ensure the continuity, synchronization and force uniformity under the process of repeatedly capturing, despinning, locking and releasing the tumbling satellite. The dynamic model of docking mechanism was established, and the impact force was analyzed in the docking process. Furthermore, a collision detection and compliance control method is proposed by using the active force-limited Cartesian impedance control and passive damping mechanism design. Findings A variety of conditions were set for the docking kinematics and dynamics simulation. The simulation and low-speed docking experiment results showed that the force translation in the docking phase was stable, the mechanism design scheme was reasonable and feasible and the proposed force-limited Cartesian impedance control could detect the collision and keep the external force within the desired value. Originality/value The paper presents a universal docking mechanism and force-limited Cartesian impedance control approach to capture the tumbling non-cooperative satellite. The docking mechanism was designed under-actuated to greatly reduce the difficulty of control and ensure the continuity, synchronization and force uniformity. The dynamic model of docking mechanism was established. The impact force was controlled within desired value by using a combination of active force-limited control approach and passive damping mechanism.

In silico analysis of bacteriophage tail tubular proteins suggests a putative sugar binding site and a catalytic mechanism

Bacteriophage base tailplate proteins were recently discovered to have hydrolytic activity towards disaccharides. The putative assignment of sugar binding sites was based on known lectin structures and identified residues a.a. 40–120 as the potential binding region for disaccharides [1]. To help verify the prediction, an in silico analysis was performed on the structure of a base tailplate protein gp31 from Klebsiella pneumoniae bacteriophage KP32 (PDB: 5MU4) which shows activity towards maltose but not trehalose [1]. Based on the information, a full surface docking was performed for both sugars which identified 2 regions different than originally predicted. The first region clearly favored maltose during the docking phase while the second one allowed for the energetically-equivalent binding of trehalose. To verify the assignment, a molecular dynamics simulation was performed to assess the stability of the docked substrates. MD simulations suggested that the first site included residues D131, D133, and E134, and was also superior for maltose binding while clearly disfavoring trehalose. Analysis of the putative catalytic mechanism suggested residues D131, D133 and E134 as critical for substrate binding. The residue D133 did participate in a stable substrate binding and was positioned near the scissile bond, potentially making it a catalytic residue. Catalytic residues were most likely D131 and D133, one of the two options proposed by Pyra et al. [1]. A comparison with known hydrolase mechanisms suggested that the enzyme most likely retains configuration during hydrolysis of maltose. The findings are discussed for other bacteriophage proteins regarding their potential specificities and catalytic mechanisms.

Pose Determination for Malfunctioned Satellites Based on Depth Information

Autonomous on-orbit servicing is the future space activity which can be utilized to extend the satellite life. Relative pose estimation for a malfunctioned satellite is one of the key technologies to achieve robotic on-orbit servicing. In this paper, a relative pose determination method by using point cloud is presented for the final phase of the rendezvous and docking of malfunctioned satellites. The method consists of three parts: (1) planes are extracted from point cloud by utilizing the random sample consensus algorithm. (2) The eigenvector matrix and the diagonal eigenvalue matrix are calculated by decomposing the point cloud distribution matrix of the extracted plane. The eigenvalues are utilized to recognize rectangular planes, and the eigenvector matrix is the attitude rotation matrix from the sensor to the plane. The solution of multisolution problem is also presented. (3) An extended Kalman filter is designed to estimate the translational states, the rotational states, the location of mass center, and the moment-of-inertia ratios. Because the method only utilizes the local features without observing the whole satellite, it is suitable for the final phase of rendezvous and docking. The algorithm is validated by a series of mathematical simulations.

A Novel Method for Measuring Drogue-UAV Relative Pose in Autonomous Aerial Refueling Based on Monocular Vision

The key to the docking phase of autonomous aerial refueling missions is the relative pose measurement between the drogue and unmanned aerial vehicles. A novel measurement method for drogue-UAV (unmanned aerial vehicle) relative poses based on monocular vision is proposed in this paper. An adaptive arc-level structural feature extraction algorithm is applied to obtain the features that can be used for the measurements. In this algorithm, the projection of the end plane of the drogue in the image plane is accurately extracted based on an extraction quality metric. The projection contour and centroid of the internal black part of the drogue form the structural feature for the measurements. A robust pose estimation algorithm is presented to estimate the relative pose between the drogue and the UAV. The pose can be solved using a cone that is composed of the optical center of the camera and the structural feature. A Kalman filter is then applied to robustly estimate the pose of the drogue. A simulation and ground experiment are used to verify the robustness and effectiveness of the proposed method, which achieves good performance compared with other methods.