Search Results (29)

In the mission scenario of On-Orbit Assembly (OOA), servicing spacecraft are frequently tasked with towing large-scale, flexible truss structures to designated assembly sites. This process involves complex coupled dynamics between the spacecraft and the flexible payload, which are often unmodeled or unknown, posing significant challenges to control precision. Furthermore, the proximity of other assembled structures in the construction area necessitates strict collision avoidance. To address these challenges, this paper proposes a novel adaptive robust controller for spacecraft thruster-based orbital control that integrates Prescribed Performance Control (PPC) with a Radial Basis Function Neural Network (RBFNN). The PPC framework ensures that the position tracking errors remain within user-predefined, time-varying boundaries, providing an intrinsic mechanism for collision avoidance during the towing of large flexible structures. Concurrently, the RBFNN is employed to approximate the entire unknown nonlinear dynamics of the combined spacecraft-truss system online, effectively compensating for uncertainties arising from the flexibility of the truss and external disturbances. The performance of the proposed controller is validated through both numerical simulations and hardware experiments on a ground-based air-bearing satellite simulator. Simulation results demonstrate the controller’s superior tracking accuracy compared to a conventional PID controller, while strictly adhering to the prescribed error constraints. Experimental results further confirm its effectiveness, showing that the simulator can track a desired trajectory with high precision, with tracking errors converging to approximately 5 mm while consistently remaining within the predefined safety boundaries. The proposed approach provides a robust and safe control solution for complex proximity operations in on-orbit construction, eliminating the need for precise dynamic modeling of flexible payloads. Full article

An increased level of autonomy is attractive above all in the framework of proximity operations, and researchers are focusing more and more on artificial intelligence techniques to improve spacecraft’s capabilities in these scenarios. This work presents an autonomous AI-based guidance algorithm to plan the path of a chaser spacecraft for the map reconstruction of an artificial uncooperative target, coupled with Model Predictive Control for the tracking of the generated trajectory. Deep reinforcement learning is particularly interesting for enabling spacecraft’s autonomous guidance, since this problem can be formulated as a Partially Observable Markov Decision Process and because it leverages domain randomization well to cope with model uncertainty, thanks to the neural networks’ generalizing capabilities. The main drawback of this method is that it is difficult to verify its optimality mathematically and the constraints can be added only as part of the reward function, so it is not guaranteed that the solution satisfies them. To this end a convex Model Predictive Control formulation is employed to track the DRL-based trajectory, while simultaneously enforcing compliance with the constraints. Two neural network architectures are proposed and compared: a recurrent one and the more recent transformer. The trained reinforcement learning agent is then tested in an end-to-end AI-based pipeline with image generation in the loop, and the results are presented. The computational effort of the entire guidance and control strategy is also verified on a Raspberry Pi board. This work represents a viable solution to apply artificial intelligence methods for spacecraft’s autonomous motion, still retaining a higher level of explainability and safety than that given by more classical guidance and control approaches. Full article

Space object detection, as the foundation for ensuring the long-term safe and stable operation of spacecraft, is widely applied in a variety of close-proximity tasks such as non-cooperative target monitoring, space debris avoidance, and spacecraft mission planning. To strengthen the detection capabilities for non-cooperative spacecraft and space debris, a method based on You Only Look Once Version 11 (YOLO11) is proposed in this paper. On the one hand, to tackle the issues of noise and low contrast in images captured by spacecraft, bilateral filtering is applied to remove noise while preserving edge and texture details effectively, and image contrast is enhanced using the contrast-limited adaptive histogram equalization (CLAHE) technique. On the other hand, to address the challenge of small object detection in spacecraft, loss-guided online data augmentation is proposed, along with improvements to the YOLO11 network architecture, to boost detection capabilities for small objects. The experimental results show that the proposed method achieved $99.0\%$ mAP50 (mean Average Precision with an Intersection over Union threshold of 0.50) and $92.6\%$ mAP50-95 on the SPARK-2022 dataset, significantly outperforming the YOLO11 baseline, thereby validating the effectiveness of the proposed method. Full article

This paper addresses the challenge of robust pose estimation for spacecraft under rapid inter-frame motion, proposing a two-stage point cloud registration framework. The first stage computes coarse pose estimation by leveraging Fast Point Feature Histogram (FPFH) descriptors with random sample and consensus (RANSAC) for correspondence matching, effectively handling significant positional displacements. The second stage refines the solution through geometry-aware fine registration using raw point cloud data, enhancing precision through a multi-scale iterative ICP-like framework. To validate the approach, we simulate time-of-flight (ToF) sensor measurements by rendering NASA’s public 3D spacecraft models and obtain 3D point clouds by back-projecting the depth measurements to 3D space. Comprehensive experiments demonstrate superior performance over several state-of-the-art methods in both accuracy and robustness under rapid inter-frame motion scenarios. The dual-stage architecture proves effective in maintaining tracking continuity while mitigating error accumulation from fast relative motion, showing promise for autonomous spacecraft proximity operations. Full article

This paper proposes a pose estimation algorithm using INS and LiDAR for precise cooperative relative navigation between target and chaser spacecraft in a close docking mission scenario. Previous cooperative algorithms have proposed estimating position and pose transformations using typical matching methods or to pre-extract and utilize features from point cloud data. However, in the case of general proximity rendezvous docking, a straight-line approach scenario with very few changes in attitude is usually assumed, and, in this case, pose estimation using simple matching techniques or feature point extraction leads to inaccurate results. To solve this problem, this paper performed a principal component analysis (PCA) based on ICP to align the initial transformation matrix. To keep the distribution of point cloud data constant, the point cloud at the time of docking was applied to ICP to minimize the change in the distribution of point clouds over time. Finally, we designed an EKF filter that estimates the relative position, velocity, and attitude using the INS model and combines it with the relative pose estimated from the point cloud; the proposed method showed the results of estimating the relative pose more effectively than the previous method. The simulation and experiment showed more accurate estimation results than the ICP method in position and attitude, respectively. In particular, in the case of position, both the simulation and experiment showed 0.46 m and 0.32 m better estimation results in the z-axis. Also, attitude estimation showed 0.11° and 2.66° better results in roll and 0.01° and 0.34° better results in pitch. This shows that the proposed algorithm provided better pose estimation results in the docking scenario in a straight line. Full article

Satellite pose estimation (PE) is crucial for space missions and orbital maneuvering. High-accuracy satellite PE could reduce risks, enhance safety, and help achieve the objectives of close proximity and docking operations for autonomous systems by reducing the need for manual control in the future. This article presents a joint iterative satellite PE and particle swarm optimization (PE-PSO) method. The PE-PSO method uses the number of batches derived from satellite PE as the number of particles and keeps the number of epochs from the satellite PE process as the number of epochs for PSO. The objective function of PSO is the training function of the implemented network. The output obtained from the previous objective function is applied to update the new positions of the particles, which serve as the inputs of the current training function. The PE-PSO method is tested on synthetic Soyuz satellite image datasets acquired from the Unreal Rendered Spacecrafts On-Orbit Datasets (URSOs) under different preset hyperparameters. The proposed method significantly reduces the incurred loss, especially during the batch-processing operation of each epoch. The results illustrate the accuracy improvement attained by the PE-PSO method over epoch processing, but its time consumption is not distinct from that of the conventional method. In addition, PE-PSO achieves better performance by reducing the mean position estimation error by 13.1% and the mean orientation estimation error on the testing dataset by 29.1% based on the pretrained weights of Common Objects in Context (COCO). Additionally, PE-PSO improves the accuracy of the Soyuz_hard-based weight by 7.8% and 0.3% in terms of the mean position estimation error and mean orientation estimation error, respectively. Full article

Close-proximity operations play a crucial role in emerging mission concepts, such as Active Debris Removal or small celestial bodies exploration. When approaching a non-cooperative target, the increased risk of collisions and reduced reliance on ground intervention necessitate autonomous on-board relative pose (position and attitude) estimation. Although navigation strategies relying on monocular cameras which operate in the visible (VIS) spectrum have been extensively studied and tested in flight for navigation applications, their accuracy is heavily related to the target’s illumination conditions, thus limiting their applicability range. The novelty of the paper is the introduction of a thermal-infrared (TIR) camera to complement the VIS one to mitigate the aforementioned issues. The primary goal of this work is to evaluate the enhancement in navigation accuracy and robustness by performing VIS-TIR data fusion within an Extended Kalman Filter (EKF) and to assess the performance of such navigation strategy in challenging illumination scenarios. The proposed navigation architecture is tightly coupled, leveraging correspondences between a known uncooperative target and feature points extracted from multispectral images. Furthermore, handover from one camera to the other is introduced to enable seamlessly operations across both spectra while prioritizing the most significant measurement sources. The pipeline is tested on Tango spacecraft synthetically generated VIS and TIR images. A performance assessment is carried out through numerical simulations considering different illumination conditions. Our results demonstrate that a combined VIS-TIR navigation strategy effectively enhances operational robustness and flexibility compared to traditional VIS-only navigation chains. Full article

This paper proposes a pose control scheme of for proximity maneuvering for tracking and observing noncooperative targets with unknown acceleration, which is an important prerequisite for on-orbit operations in space. It mainly consists of a finite-time extended state observer and constraint processing procedures. Firstly, relative pose-coupled kinematics and dynamics models with unknown integrated disturbances are established based on dual quaternion representations. Then, a finite-time extended state observer is designed using the super-twisting algorithm to estimate the integrated disturbances. Both observation field of view and collision avoidance pose-constrained models are constructed to ensure that the service spacecraft continuously and safely observes the target during proximity maneuvering. And the constraint models are further incorporated into the design of artificial potential function with a unique minimum. After that, the proportional–derivative-like pose-constrained tracking control law is proposed based on the estimated disturbances and the gradient of the artificial potential function. Finally, the effectiveness of the control scheme is verified through numerical simulations. Full article

In this paper, a recently proposed $SE\left(3\right)$-constrained extended Kalman filter (EKF) is extended to formulate a strategy for relative orbit estimation in a space-based sensor network. The resulting consensus $SE\left(3\right)$-constrained EKF utilizes space-based sensor fusion and is applied to the problem of spacecraft proximity operations and formation flying. The proposed filter allows for the state (i.e., pose and velocities) estimation of the deputy satellite while accounting for measurement error statistics using the rotation matrix to represent attitude. Via a comparison with a conventional filter in the literature, it is shown that the use of the proposed consensus $SE\left(3\right)$-constrained EKF can improve the convergence performance of the existing filter for satellite formation flying. Moreover, the benefits of faster convergence and consensus speed by using communication networks with more connections are illustrated to show the significance of the proposed sensor fusion strategy in spacecraft proximity operations. Full article

This paper addresses the challenges of close proximity operations, such as rendezvous, docking, and fly-around maneuvers for micro/nano satellites, which require high control precision under the low power and limited computational capabilities of spacecraft. Firstly, a three-degree-of-freedom air float simulator platform is designed for ground-based experiments. Subsequently, model predictive controllers based on constraints aware of particle filtering (CAPF-NMPC) are developed for executing operations such as approach, fly-around, and docking maneuvers. The results validate the effectiveness of the experimental system, demonstrating position control accuracy less than 0.03 m and attitude control accuracy less than 3°, maintaining lower computational resource consumption. This study offers a practical solution for the onboard deployment of optimized control algorithms, highlighting significant value for further engineering applications. Full article

To address the limitations of inadequate real-time performance and robustness encountered in estimating the pose of non-cooperative spacecraft during on-orbit missions, a novel method of feature point distribution selection learning is proposed. This approach utilizes a non-coplanar key point selection network with uncertainty prediction, pioneering in its capability to accurately estimate the pose of non-cooperative spacecraft, thereby representing a significant advancement in the field. Initially, the feasibility of designing a non-coplanar key point selection network was analyzed based on the influence of sensor layout on the pose measurement. Subsequently, the key point selection network was designed and trained, leveraging images extracted from the spacecraft detection network. The network detected 11 pre-selected key points with distinctive features and was able to accurately predict their uncertainties and relative positional relationships. Upon selection of the key points exhibiting low uncertainty and non-coplanar relative positions, we utilized the EPnP algorithm to achieve accurate pose estimation of the target spacecraft. Our experimental evaluation on the SPEED dataset, which comes from the International Satellite Attitude Estimation Competition, validates the effectiveness of our key point selection network, significantly enhancing estimation accuracy and timeliness compared to other monocular spacecraft attitude estimation methods. This advancement provides robust technological support for spacecraft guidance, control, and proximity operations in orbital service missions. Full article

The application of space robotic manipulators and heightened autonomy for In-Orbit Servicing (IOS) represents a paramount pursuit for leading space agencies, given the substantial threat posed by space debris to operational satellites and forthcoming space endeavors. This work presents a guidance algorithm based on Deep Reinforcement Learning (DRL) to solve for space manipulator path planning during the motion-synchronization phase with the mission target. The goal is the trajectory generation and control of a spacecraft equipped with a 7-Degrees of Freedom (7-DoF) robotic manipulator, such that its end effector remains stationary with respect to the target point of capture. The Proximal Policy Optimization (PPO) DRL algorithm is used to optimize the manipulator’s guidance law, and the autonomous agent generates the desired joint rates of the robotic arm, which are then integrated and passed to a model-based feedback linearization controller. The agent is first trained to optimize its guidance policy and then tested extensively to validate the results against a simulated environment representing the motion synchronization scenario of an IOS mission. Full article

The accelerating deployment of spacecraft in orbit has generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-cooperative, possibly unknown, resident space objects. Safety concerns with manned missions and lag times with ground-based control necessitate complete autonomy. This requires robust characterization of the target’s geometry. In this article, we present an approach for mapping geometries of satellites on orbit based on 3D Gaussian splatting that can run on computing resources available on current spaceflight hardware. We demonstrate model training and 3D rendering performance on a hardware-in-the-loop satellite mock-up under several realistic lighting and motion conditions. Our model is shown to be capable of training on-board and rendering higher quality novel views of an unknown satellite nearly 2 orders of magnitude faster than previous NeRF-based algorithms. Such on-board capabilities are critical to enable downstream machine intelligence tasks necessary for autonomous guidance, navigation, and control tasks. Full article

The development of autonomous guidance control and navigation systems for spacecraft would greatly benefit applications such as debris removals or on-orbit servicing, where human intervention is not practical. Within this context, inspired by Autonomous Vision Approach Navigation and Target Identification (AVANTI) demonstration, this work presents new guidance algorithms for rendezvous and proximity operations missions. Analytical laws are adopted and preferred over numerical methods, and mean relative orbital elements are chosen as state variables. Application times, magnitudes and directions of impulsive controls are sought to minimize propellant consumption for the planar reconfiguration of the relative motion between a passive target spacecraft and an active chaser one. In addition, simple and effective algorithms to evaluate the benefit of combining in-plane and out-of-plane maneuvers are introduced to deal with 3D problems. The proposed new strategies focus on maneuvers with a dominant change in the relative mean longitude (rarely addressed in the literature), but they can also deal with transfers where other relative orbital elements exhibit the most significant variations. A comprehensive parametric analysis compares the proposed new strategies with those employed in AVANTI and with the global optimum, numerically found for each test case. Results are similar to the AVANTI solutions when variations of the relative eccentricity vector dominate. Instead, in scenarios requiring predominant changes in the relative mean longitude, the required $\Delta$V exhibits a 49.88% reduction (on average) when compared to the original methods. In all the test cases, the proposed solutions are within 3.5% of the global optimum in terms of $\Delta$V. The practical accuracy of the presented guidance algorithms is also tested with numerical integration of equations of motion with J2 perturbation. Full article

As interest in Moon exploration grows, and efforts to establish an orbiting outpost intensify, accurate modeling of spacecraft dynamics in cislunar space is becoming increasingly important. Contrary to satellites in Low Earth Orbit (LEO), where it takes around 5 ms to communicate back and forth with a ground station, it can take up to 2.4 s to communicate with satellites near the Moon. This delay in communication can make the difference between a successful docking and a catastrophic collision for a remotely controlled satellite. Moreover, due to the unstable nature of trajectories in cislunar space, it is necessary to design spacecraft that can autonomously make frequent maneuvers to stay on track with a reference orbit. The communication delay and unstable trajectories are exactly why autonomous navigation is critical for proximity operations and rendezvous and docking missions in cislunar space. Because spacecraft computational hardware is limited, reducing the computational complexity of navigational algorithms is both desirable and often necessary. By the introduction of a linear system approach to the deputy spacecraft motion, this research avoids the computational burden of integrating the deputy relative equations of motion. In this research, the relative CR3BP equations of motion are derived and linearized using a matrix exponential approximation. This research continues the development of the matrix exponential linearized relative circular restricted three-body problem (CR3BP) equations by applying the dynamics model to estimation and control applications. A simulation is performed to compare state estimation results obtained from using the linearized equations of motion utilizing a Kalman filter and for state estimation utilizing an unscented Kalman filter with the full nonlinear equations of motion. The linearized exponential model is shown to be sufficient for state estimation in the presence of noisy measurements for an example scenario. Additionally, a linear quadratic regulator (LQR) controller was added to optimally control a deputy spacecraft to rendezvous with a chief spacecraft in cislunar space. The contribution of this work is twofold: to provide a proof of concept that the matrix exponential solution for the linearized relative CR3BP equations can be used as the dynamics model for state estimation, as well as to simulate an optimal rendezvous maneuver in the presence of measurement noise. Full article