Maneuver Trajectory Research Articles

The three-dimensional maneuver control of spinning tether system under a new Lagrangian model

This study explores the modeling and control of the spinning tether system (STS) for maneuvering between arbitrary spinning planes. The spinning tether system garners significant attention for good centrifugal stability and transportation ability. However, conventional STS models face challenges such as singularities and coupling when studying three-dimensional spinning motion. To tackle the aforementioned challenges, a new singularity-adjustable model and a maneuvering control strategy are proposed. Initially, a variable coordinate system is defined, which allows for relocating singularities to unattainable positions during three-dimensional motions. Based on this new coordinate system, the singularity-adjustable Lagrangian model is established. Subsequently, based on the new model, a three-dimensional maneuvering scheme for spatial spinning motions is introduced to define and describe STS maneuvering trajectories in a de-coupled way. Finally, based on the reference maneuvering scheme, a saturated radial basis function network-based controller is designed to attenuate potential disturbances and errors, as electric thrusters used in this work are limited in actuating magnitude. Numerical results demonstrate that, with the new singularity-adjustable Lagrangian model, singularity and coupling phenomena are avoided during the three-dimensional maneuver, and the proposed controller ensures a stable STS three-dimensional maneuvering motion.

Unit coordination knowledge enhanced autonomous decision-making approach of heterogeneous UAV formation

Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.

Intelligent Bayesian regularization backpropagation neuro computing paradigm for state features estimation of underwater passive object

In underwater environments, the accurate estimation of state features for passive object is a critical aspect of various applications, including underwater robotics, surveillance, and environmental monitoring. This study presents an innovative neuro computing approach for instantaneous state features reckoning of passive marine object following dynamic Markov chains. This paper introduces the potential of intelligent Bayesian regularization backpropagation neuro computing (IBRBNC) for the precise estimation of state features of underwater passive object. The proposed paradigm combines the power of artificial neural network with Bayesian regularization technique to address the challenges associated with noisy and limited underwater sensor data. The IBRBNC paradigm leverages deep neural networks with a focus on backpropagation to model complex relationships in the underwater environment. Furthermore, Bayesian regularization is introduced to incorporate prior knowledge and mitigate overfitting, enhancing the model’s robustness and generalization capabilities. This dual approach results in a highly adaptive and intelligent system capable of accurately estimating the state features of passive object in real-time. To evaluate the efficacy of this intelligent computing approach, a controlled supervised maneuvering trajectory for underwater passive object is constructed. Real-time estimations of location, velocity, and turn rate for dynamic target are scrutinized across five distinct scenarios by varying the Gaussian observed noise’s standard deviation, aiming to minimize mean square errors (MSEs) between real and estimated values. The effectiveness of the proposed IBRBNC paradigm is demonstrated through extensive simulations and experimental trials. Results showcase its superiority over traditional nonlinear filtering methods like interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model unscented Kalman filter (IMMUKF), especially in the presence of noise, incomplete measurements and sparse data.

Comparison and Evaluation of Learning Capabilities of Deep Learning Methods for Predicting Ship Motions

The development of intelligent ship control systems in real-world conditions relies heavily on the accurate identification and prediction of ship seakeeping and maneuvering trajectories. In this study, we comprehensively evaluate a selection of deep learning methods to assess their learning capabilities in terms of idealizing ship motion behavior in realistic operational environments. To recover real conditions, we utilize historical Automatic Identification System (AIS) data and a time domain 6 Degree of Freedom (6- DoF) grounding dynamics model to generate ship motion sequences for a Ro-Ro passenger ship operating in the Gulf of Finland. Via a rigorous evaluation process, we validate the performance of these methods using extensive data streams. The analysis includes the identification and estimation of uncertainties between two ports. The paper demonstrates the proficiency of the selected deep learning methods in capturing ship maneuvering features, their potential use in the design of ship control and intelligent decision support systems.

Probabilistic Higher-Order Velocity Obstacle for Dynamic Collision Avoidance in Uncertain Environments

Autonomous systems like unmanned aircraft systems are touted as front-runners in terms of efficiency and agility in challenging operations like emergency response. The ability of autonomous agents in such systems to avoid collisions with dynamic obstacles while navigating through an obstacle-cluttered environment is critical for success in any mission. In addition to that, further challenges are posed by environments with uncertainties in obstacles’ motion, sensing, and occluded regions. To this end, a higher-order velocity obstacle–based novel motion planner is presented in this paper in a probabilistic setup for smooth, collision-free navigation of the agent with acceleration constraints in uncertain, unstructured environments with an element of anticipation for the future environment. The effectiveness of the developed algorithm for safe, collision-free, and smooth navigation of the agent is investigated in simulation studies in two different kinds of environments, one with known trajectories of obstacles and the other with unknown maneuvering trajectories of obstacles. Extensive simulation studies are performed in the presence of dynamic obstacles to elucidate the performance of the proposed algorithm on four crucial parameters—mission time, computational time, minimum obstacle distance, and an overall control effort under varied obstacle densities. With satisfactory performance in all these aspects, the developed algorithm possesses strong potential for real-time implementation.

Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment.

This article is concerned with the problem of planning optimal maneuver trajectories and guiding the mobile robot toward target positions in uncertain environments for exploration purposes. A hierarchical deep learning-based control framework is proposed which consists of an upper level motion planning layer and a lower level waypoint tracking layer. In the motion planning phase, a recurrent deep neural network (RDNN)-based algorithm is adopted to predict the optimal maneuver profiles for the mobile robot. This approach is built upon a recently proposed idea of using deep neural networks (DNNs) to approximate the optimal motion trajectories, which has been validated that a fast approximation performance can be achieved. To further enhance the network prediction performance, a recurrent network model capable of fully exploiting the inherent relationship between preoptimized system state and control pairs is advocated. In the lower level, a deep reinforcement learning (DRL)-based collision-free control algorithm is established to achieve the waypoint tracking task in an uncertain environment (e.g., the existence of unexpected obstacles). Since this approach allows the control policy to directly learn from human demonstration data, the time required by the training process can be significantly reduced. Moreover, a noisy prioritized experience replay (PER) algorithm is proposed to improve the exploring rate of control policy. The effectiveness of applying the proposed deep learning-based control is validated by executing a number of simulation and experimental case studies. The simulation result shows that the proposed DRL method outperforms the vanilla PER algorithm in terms of training speed. Experimental videos are also uploaded, and the corresponding results confirm that the proposed strategy is able to fulfill the autonomous exploration mission with improved motion planning performance, enhanced collision avoidance ability, and less training time.

Multiple Leap Maneuver Trajectory Design and Tracking Method Based on Prescribed Performance Control during the Gliding Phase of Vehicles

A novel standard trajectory design and tracking guidance used in the multiple active leap maneuver mode for hypersonic glide vehicles (HGVs) is proposed in this paper. First, the dynamic equation and multiconstraint model are first established in the flight path coordinate system. Second, the reference drag acceleration-normalized energy (D-e) profile of the multiple active leap maneuver mode is quickly determined by the Newton iterative algorithm with a single design parameter. The range to go error is corrected by the drag acceleration profile update algorithm, and the drag acceleration error of the gliding terminal is corrected by the aerodynamic parameter estimation algorithm. Then, the reference drag acceleration tracking guidance law is designed based on the prescribed performance control method. Finally, the CAV-L vehicle model is used for numerical simulation. The results show that the proposed method can satisfy the design requirements of drag acceleration under multiple active leap maneuver modes, and the reference drag acceleration can be tracked precisely. The adaptability and robustness of the proposed method are verified by the Monte Carlo simulations under various combined deviation conditions.

Arbitrary controlled re-orientation of a spinning body by evolving its tensor of inertia

Bodies with the nonspherical tensor of inertia (TOI) exhibit a variety of rotational motion patterns, including chaotic motion, stable periodic (quasi-periodic) rotation, unstable rotation around the direction close to the body's second principal axis, featuring a well-known tennis-racket (also known as Garriott-Dzhanibekov [1]) effect – series of seemingly spontaneous 180 degrees flips. These patterns are even more complex if the body's TOI is changing with time. Changing a body's TOI has been discussed recently as a tool to perform controllable Garriott-Dzhanibekov flips and similar maneuvers. In this work, the optimal control of the TOI of the body (spacecraft, or any other device that admits free rotation in three dimensions) is used as a means to perform desirable re-orientations of a body with respect to its angular velocity. Using the spherical TOI as the initial and final point of the maneuver, we optimize the parameters of the maneuver to achieve and stabilize the desired orientation of the body's principal axes with respect to spin angular velocity. It appears that such a procedure allows for finding arbitrarily complex maneuver trajectories of a spinning body. In particular, intermediate axis instability can be used to break the alignment of the body's principal axis and the axis of rotation. Such maneuvers do not require utilization of propellants and could be straightforwardly used for attitude control of a spin-stabilized spacecraft. The capabilities of such a method of angular maneuvering are demonstrated in numerical simulations.

Constrained multi-objective trans-media maneuver trajectory optimization for morphing unmanned aerial-underwater vehicles

In this study, the challenge of optimizing trans-media maneuver trajectories is addressed through the introduction of a Constrained Multi-Objective Grey Wolf Optimizer (CMOGWO). The objective function has been refined to assess solutions more accurately based on their feasibility and adherence to constraints. Three distinct Archives have been employed for categorizing and optimizing dominant, non-dominant, and feasible non-dominant solutions, each benefiting from specifically tailored optimization strategies. A hybrid search method, complemented by a watch-based strategy, is proposed for effectively guiding the population towards the Pareto front, ensuring the discovery of potential optimal solutions. Investigations have been conducted into the impact of initial altitude on trajectory optimization, with comparative simulations revealing the effectiveness of the proposed CMOGWO in managing unmanned aerial-underwater vehicle trajectories. The results demonstrate the potential of the CMOGWO in enhancing the reliability and feasibility of trajectory optimization in complex environmental conditions.

An Extended Polar Format Algorithm for Joint Envelope and Phase Error Correction in Widefield Staring SAR with Maneuvering Trajectory

Polar format algorithm (PFA) is a widely used high-resolution SAR imaging algorithm that can be implemented in advanced widefield staring synthetic aperture radar (WFS-SAR). However, existing algorithms have limited analysis in wavefront curvature error (WCE) and are challenging to apply to WFS-SAR with high-resolution and large-swath scenes. This paper proposes an extended polar format algorithm for joint envelope and phase error correction in WFS-SAR imaging with maneuvering trajectory. The impact of the WCE and residual acceleration error (RAE) are analyzed in detail by deriving the specific wavenumber domain signal based on the mapping relationship between the geometry space and wavenumber space. Subsequently, this paper improves the traditional WCE compensation function and introduces a new range cell migration (RCM) recalibration function for joint envelope and phase error correction. The 2D precisely focused SAR image is acquired based on the spatially variant inverse filtering in the final. Simulation experiments validate the effectiveness of the proposed method.

Numerical simulation of the maneuvering performance of ships in broken ice area

Maneuverability is a critical aspect of ship navigation performance, particularly during polar navigation, where the presence of ice significantly affects maneuverability compared to open water navigation. By coupling the non-smooth discrete element method (NDEM) with the Maneuvering Modeling Group (MMG) model, this paper proposes a numerical model to simulate the maneuvering motion of full-scale ships in broken ice regions. In the proposed model, the ship-ice interaction was solved through NDEM, while the 3-DOF MMG model was utilized to simulate the autonomous navigation movement of ships. Based on the relationship between ship turning radius and ship speed, the critical relationship between ice moment and ice resistance is derived in this paper, which can be used to verify the rationality of simulation results of maneuvering motion. In addition, the effects of ice concentration, size, thickness, and ship speed and rudder angle on the ship's maneuvering trajectory and steering flexibility are analyzed by simulating turning motion and Zig-Zag maneuvers. This provides an effective numerical model to simulate the maneuvering motion of a ship in a broken ice area with low-medium ice concentration.

Технологія керування безпілотним літальним апаратом за умов конфліктних ситуацій

Introduction. The motion control of traditional manned aircraft and unmanned aerial vehicles (UAVs) has many distinctions that directly affect the process of conflict resolution. The problem of resolving conflict situations involving different types of aircraft is relevant due to the rapid development of unmanned aircraft and the expansion of its application areas. Purpose. The aim of the paper is to develop a technology for controlling an unmanned aerial vehicle in conflict situations, which will allow optimizing the maneuver trajectory based on a number of criteria and ensure safe separation of aircraft in airspace. Results. The paper proposes a technology for UAV control in the conditions of a conflict: a number of approaches to resolving conflict situations between different types of aircraft are identified; the concept of conflict situations for UAVs and traditional manned aircrafts is presented, on the basis of which a technology is developed that combines different approaches to detecting a conflict, classifying the occurrence of a conflict, classifying an evasive maneuver, and ensuring the safe separation of the controlled UAV from different types of aircraft in airspace. Conclusions. Based on the analysis results of the current state of UAV control in the conditions of a conflict problem, it is determined that such tasks are already being solved, but it is advisable to analyze the possibility of further providing additional protection for the controlled UAV and optimizing its trajectory to ensure the least spatial and time losses during the flight. Conflict situations are a violation of the norms of maintaining a safe distance in space and lead to a collision of aircraft in the absence of appropriate actions to resolve such violations. Therefore, for the effective resolution of the conflict situations, the classifications of the degree of threat, classification of evasive maneuvers and ensuring minimal spatial and time losses by using the criterion of minimum area of the required maneuver are proposed.

The trajectory motion model based TPHD and TCPHD filters for maneuvering targets

In this article, we present the TMM-TPHD and TMM-TCPHD filters, which are the alternative trajectory probability hypothesis density (TPHD) and the alternative trajectory cardinality probability hypothesis density (TCPHD) filters for tracking maneuvering targets. To describe the trajectory maneuver, we define the motion model history sequence of a trajectory as the trajectory motion model (TMM) variable. By incorporating the TMM, we augment the general trajectory model to accommodate the maneuver. Based on this augmented model, we propose the TMM extensions of the standard TPHD and TCPHD filters, namely TMM-TPHD and TMM-TCPHD filters. These filters can directly estimate the number, trajectory state, and TMM of maneuvering targets. In addition to the derivation of the filtering formulas, we also develop linear Gaussian mixture (LGM) implementations for TMM-TPHD and TMM-TCPHD filters. We verify the tracking performance and parameter stability of the proposed filters through tracking simulations involving multiple maneuvering targets.

A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization

Air combat situation assessment is the basis of target assignment and maneuver decisions. The current air combat situation assessment models, whether nonparametric or parametric, ignore the continuity and timing of situation changes, making the situation assessment results lose tactical significance. Aimed at the shortcomings of current air combat situation assessment, a dynamic air combat situation assessment model based on situation knowledge extraction and weight optimization was proposed by combining a multiple regression model of hidden logic process, a weight optimization model based on grey prospect theory, a weight mapping model based on autoencoder and extreme learning machine (AE-ELM) and an air combat situation characteristic parameter prediction model based on dynamic weight online extreme learning machine (DWOSELM). Firstly, considering the timing and continuity of air combat situation change, a hidden logic process multiple regression model was introduced to realize the segmentation of air combat situation time series data and the extraction of air combat situation primitives. Secondly, the weight optimization method based on grey prospect theory was used to obtain the weight of the evaluation index under different air combat situations. On this basis, the dynamic mapping model between air combat situation characteristic parameters and the weight of index was constructed by using AE-ELM. Then, the dynamic weighted extreme learning machine was used to build the target maneuver trajectory prediction model, and the future position information of the target was predicted. On this basis, the future situation information between the enemy and us was obtained. Finally, the time weight calculation model based on normal cumulative distribution was used to determine the importance of the situation at each time. The situation information at multiple times in the air combat process was fused to obtain the comprehensive air combat situation assessment results at the current time. The simulation results show that the model can fully exploit the influence of historical information, effectively integrate the air combat situation information at multiple moments, and generate the air combat situation assessment results with practical tactical significance according to the individual differences of different pilots.

An air combat maneuver pattern extraction based on time series segmentation and clustering analysis

Target maneuver recognition is a prerequisite for air combat situation awareness, trajectory prediction, threat assessment and maneuver decision. To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data, and automatically and adaptively complete the task of extracting the target maneuver pattern, in this paper, an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder, G-G clustering algorithm and the selective ensemble clustering analysis algorithm. Firstly, the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension; Then, taking the time information into account, the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm, and a large number of maneuver primitives are extracted; Finally, the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm, which can prove that each class represents a maneuver action. The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3% of maneuver actions，indicating that the method is effective and satisfies the requirements for engineering accuracy. In addition, this method can provide data support for various target maneuvering recognition methods proposed in the literature, greatly reduce the workload and improve the recognition accuracy.

Dynamic-learning spatial-temporal Transformer network for vehicular trajectory prediction at urban intersections

Forecasting vehicles' future motion is crucial for real-world applications such as the navigation of autonomous vehicles and feasibility of safety systems based on the Internet of Vehicles (IoV). Vehicular trajectory prediction at urban intersections remains challenging due to the difficulty in modeling temporal dependencies and spatial interactions among traffic agents. This paper proposes a dynamic-learning spatial-temporal Transformer network (DSTTN) with domain adaptation training methods based on two modules for two-dimensional vehicular trajectory prediction at urban intersections. The first module is trajectory maneuver characterization (TMC) which captures latent driving maneuver features and divides trajectory data into different categories with different distributions, which can be regarded as different domains in transfer learning (TL). The second module is trajectory distribution matching (TDM) which adopts a novel spatial-temporal Transformer network with distribution matching loss to dynamically learn domain-invariant maneuvers and achieve accurate trajectory prediction. Experiments and ablation studies collected at two unsignalized urban intersections of the inD dataset first validate the interpretability, transferability, and high prediction accuracy of the proposed DSTTN. The results show that the TMC module has maneuver-representational ability. The proposed STTN achieves good and stable prediction accuracy compared with other baselines, including the state-of-the-art deep learning model, while DSTTN with the TMC and TDM modules further improves accuracy. Additional experiments have been conducted under 70 randomly-selected urban intersection scenarios of the Waymo dataset to validate the good prediction accuracy of the proposed method. The formulation of DSTTN offers new ideas for dividing trajectory data into different domains and general domain adaptation to vehicle trajectory prediction under urban road environments.

Impulsive guidance of optimal pursuit with conical imaging zone for the evader

This paper addresses the problem of orbital maneuver of the pursuer to position the evader within its imaging zone, which is a spherical cone centered at the pursuer's camera with its centerline parallel to the sunlight. Two pursuit strategies are proposed respectively for the free-floating and maneuvering evader based on geometry analysis of the maneuver trajectory. Specifically, the shortest path from the evader to the conical imaging zone of the pursuer is first calculated and used to analytically design a fixed-time near-fuel-optimal guidance law for the pursuer with a fixed time interval. The magnitude of each impulse is guaranteed to be within the allowable range. Compared with the Sequence Quadratic Programming (SQP) method, the superiority in robustness and computation efficiency of this method over SQP is illustrated. Furthermore, for the maneuvering evader whose maneuver strategy is unknown to the pursuer, the proximal policy optimization (PPO) algorithm in reinforcement learning is applied to train and guide the pursuer to approach the evader intelligently. To enhance the stability and efficiency of the training process, the shortest path from the conical imaging zone to the evader is used to define a unified reward function. Since the reward function synthesizes the relative distance and sunlight angle between the pursuer and evader, reward parameters can be reduced, which is more convenient and efficient for the design and implementation of the reinforcement learning algorithm.

Deep Learning-Based Trajectory Planning and Control for Autonomous Ground Vehicle Parking Maneuver

In this paper, a novel integrated real-time trajectory planning and tracking control framework capable of dealing with autonomous ground vehicle (AGV) parking maneuver problems is presented. In the motion planning component, a newly-proposed idea of utilizing deep neural networks (DNNs) for approximating optimal parking trajectories is further extended by taking advantages of a recurrent network structure. The main aim is to fully exploit the inherent relationships between different vehicle states in the training process. Furthermore, two transfer learning strategies are applied such that the developed motion planner can be adapted to suit various AGVs. In order to follow the planned maneuver trajectory, an adaptive learning tracking control algorithm is designed and served as the motion controller. By adapting the network parameters, the stability of the proposed control scheme, along with the convergence of tracking errors, can be theoretically guaranteed. In order to validate the effectiveness and emphasize key features of our proposal, a number of experimental studies and comparative analysis were executed. The obtained results reveal that the proposed strategy can enable the AGV to fulfill the parking mission with enhanced motion planning and control performance. <i>Note to Practitioners</i>&#x2014;This article was motivated by the problem of optimal automatic parking planning and tracking control for autonomous ground vehicles (AGVs) maneuvering in a restricted environment (e.g., constrained parking regions). A number of challenges may arise when dealing with this problem (e.g., the model uncertainties involved in the vehicle dynamics, system variable limits, and the presence of external disturbances). Existing approaches to address such a problem usually exploit the merit of optimization-based planning/control techniques such as model predictive control and dynamic programming in order for an optimal solution. However, two practical issues may require further considerations: 1). The nonlinear (re)optimization process tends to consume a large amount of computing power and it might not be affordable in real-time; 2). Existing motion planning and control algorithms might not be easily adapted to suit various types of AGVs. To overcome the aforementioned issues, we present an idea of utilizing the recurrent deep neural network (RDNN) for planning optimal parking maneuver trajectories and an adaptive learning NN-based (ALNN) control scheme for robust trajectory tracking. In addition, by introducing two transfer learning strategies, the proposed RDNN motion planner can be adapted to suit different AGVs. In our follow-up research, we will explore the possibility of extending the developed methodology for large-scale AGV parking systems collaboratively operating in a more complex cluttered environment.

Knowledge transfer strategy for enhancement of ship maneuvering model

The advancement of autonomous vehicles and concerns about ship navigation safety have resulted in a greater need for ship model quality. However, in situations where there is limited prior information or data available about the system, the development of accurate models can be challenging. To address this issue, we propose a knowledge transfer strategy that can migrate and adapt domain knowledge from a well-modeled benchmark ship to a target ship. The benchmark, or source ship, should resemble the target ship in the feature space and reveal similar trends in the prediction horizon. By incorporating informative trends into the data-driven transfer function, the representative model of the target ship can be considerably enhanced. In this study, the experiments are conducted on two full-scale vessels that characterize different dimensions and dynamic properties. A feature vector is introduced to evaluate the configuration similarity between vessels, and ship maneuverability is compared to authorize the security of predictive tendency. The derived target ship model is verified to accurately predict maneuvering trajectories in various scenarios, demonstrating that knowledge transfer from a source ship facilitates the target ship modeling process. This approach provide new insights into the development of models for systems with confined information.

Efficient on board storage and retrieval of fast maneuver profiles for space applications

Attitude guidance is a concept for implementing performance enhancing rotational maneuvers that uses a conventional closed-loop attitude control system to track an optimized maneuver trajectory. Minimum-time attitude guidance is presently being used for executing fast occultation avoidance maneuvers on NASA’s Lunar Reconnaissance Orbiter (LRO). A challenge in operationalizing the idea is related to the limited size of the spacecraft’s command buffer, which was not designed with maneuver tracking in mind. In this paper, we propose an interpolating pre-filter built on B-splines that can be used on board to process downsampled maneuver commands (to save buffer space) and provide attitude control inputs at the servo-rate. The approach is validated using a high-fidelity simulation of the LRO developed at NASA’s Goddard Space Flight Center.