arXiv subjects

Manoranjan Majji

Publications and source records attributed to Manoranjan Majji.

At least 19 recordsLinked to original sources

arXiv2026

Uncertainty Estimators for Robust Backup Control Barrier Functions

Designing safe controllers is crucial and notoriously challenging for input-constrained safety-critical control systems. Backup control barrier functions offer an approach for the construction of safe controllers online by considering the flow of the system under a backup controller. However, in the presence of model uncertainties, the flow cannot be accurately computed, making this method insufficient for safety assurance. To tackle this shortcoming, we integrate backup control barrier functions with uncertainty estimators and calculate the flow under a reconstruction of the model uncertainty while refining this estimate over time. We prove that the controllers resulting from the proposed Uncertainty Estimator Backup Control Barrier Function (UE-bCBF) approach guarantee safety, are robust to unknown disturbances, and satisfy input constraints.

eess.SY↗

arXiv2026

Output Feedback Backup Control Barrier Functions: Safety Guarantees Under Input Bounds and State Estimation Error

Guaranteeing the safety of controllers is vital for real-world applications, but is markedly difficult when the states are not perfectly known and when the control inputs are bounded. Backup control barrier functions (bCBFs) use predictions of the flow under a prescribed controller to achieve safety in the presence of bounded inputs and perfect state information. However, when only an estimate of the true state is known, this flow may not be precisely computed, as the initial condition is unknown. Furthermore, the true flow evolves using feedback from the estimated state, thus introducing coupling between known and unknown flows. To address these challenges, we propose a technique that leverages an uncertainty envelope centered around the estimated flow and show that ensuring the safety of this envelope guarantees that the true state satisfies the safety constraints. Additionally, we show that in the presence of state uncertainty, using the resulting Output Feedback Backup Control Barrier Functions (O-bCBFs), there always exists a feasible control input that can guarantee the safety of the true state, even in the presence of input constraints.

eess.SY↗

arXiv2026

Projective Transformations for Regularized Central-Force Dynamics: Hamiltonian Formulation

This work introduces a Hamiltonian approach to regularization and linearization of central-force particle dynamics through a new canonical extension of the so-called "projective decomposition". The regularization scheme is formulated within the framework of classic analytical Hamiltonian dynamics as a redundant-dimensional canonical/symplectic coordinate transformation, combined with an evolution parameter transformation, on extended phase space. By considering a generalized version of the standard projective decomposition, we obtain a family of such canonical transformations which differ at the momentum level. From this family of transformations, a preferred coordinate set is chosen that possesses a simple and intuitive connection to the particle's local reference frame. Using this transformation, closed-form solutions are readily obtained for inverse-square and inverse-cubic radial forces, or any superposition thereof. Governing equations are numerically validated for the classic two-body problem incorporating the J2 gravitational perturbation.

math.DS↗

arXiv2026

Orbit Elements from Kepler Solutions in Projective Coordinates

Closed-Form Kepler solutions in projective coordinates are used to define a corresponding set of eight orbit elements and obtain their governing equations for arbitrarily-perturbed two-body dynamics. The elements and their dynamics are singularity-free in all cases besides rectilinear motion (when angular momentum vanishes). The classic J2-perturbed two-body problem is developed and used for numerical verification.

astro-ph.EP↗

arXiv2025

On-Manifold Low-Thrust Rephasing of Quasi-Periodic Orbits

A bi-level optimal control framework is introduced to solve the low-thrust re-phasing problem on quasi-periodic invariant tori in multi-body environments where deviations away from the torus during maneuver are considered unsafe or irresponsible. It is shown for a large class of mechanical systems that conformity to the torus manifold during periods of non-zero control input is infeasible. The most feasible trajectories on the torus surface are generated through the minimization of fictitious control input in the torus space using phase space control variables mapped via the torus function. These reference trajectories are then transitioned to the phase space both through a minimum tracking error homotopy and minimum time patched solutions. Results are compared to torus agnostic low-thrust transfers using measures of fuel consumption, cumulative torus error, and coast time spent on the torus during maneuver. Modifications to the framework are made for the inclusion of quasi-periodically forced dynamical systems. Lastly, minimum time recovery trajectories with free final torus conditions expose the disparity between the proposed framework and torus agnostic approaches. Examples are drawn from the circular and elliptical restricted three-body problems.

nlin.CD↗

arXiv2025

Direct Pseudospectral Optimal Control by Orthogonal Polynomial Integral Collocation

This paper details a methodology to transcribe an optimal control problem into a nonlinear program for generation of the trajectories that optimize a given functional by approximating only the highest order derivatives of a given system's dynamics. The underlying method uses orthogonal polynomial integral collocation by which successive integrals are taken to approximate all lower order states. Hence, one set of polynomial coefficients can represent an entire coordinate's degree of freedom. Specifically, Chebyshev polynomials of the first and second kind and Legendre polynomials are used over their associated common interpolating grids derived from the bases' roots and extrema. Simple example problems compare different polynomial bases' performance to analytical solutions. The planar circular orbit raising problem is used to verify the method with solutions obtained by other pseudospectral methods in literature. Finally, a rocket landing flip maneuver problem is solved to demonstrate the ability to solve complex problems with multiple states and control variables with constraints. Simulations establish this method's performance, and reveal that the polynomial/node choice for a given problem notably affects the performance.

math.OC↗

arXiv2025

Linear and Regular Kepler-Manev Dynamics via Projective Transformations: A Geometric Perspective

This work presents a geometric formulation for transforming nonconservative mechanical Hamiltonian systems and introduces a new method for regularizing and linearizing central force dynamics -- in particular, Kepler and Manev dynamics -- through a projective transformation. The transformation is formulated as a configuration space diffeomorphism (rather than a submersion) that is lifted to a cotangent bundle (phase space) symplectomorphism and used to pullback the original mechanical Hamiltonian system, Riemannian kinetic energy metric, and other key geometric objects. Full linearization of both Kepler and Manev dynamics (in any finite dimension) is achieved by a subsequent conformal scaling of the projectively-transformed Hamiltonian vector field. Two such conformal scalings are given, both achieving linearization. Arbitrary conservative and nonconservative perturbations are included, with closed-form solutions readily obtained in the unperturbed Kepler or Manev cases.

math-ph↗

arXiv2025

A Rapid Trajectory Optimization and Control Framework for Resource-Constrained Applications

This paper presents a computationally efficient model predictive control formulation that uses an integral Chebyshev collocation method to enable rapid operations of autonomous agents. By posing the finite-horizon optimal control problem and recursive re-evaluation of the optimal trajectories, minimization of the L2 norms of the state and control errors are transcribed into a quadratic program. Control and state variable constraints are parameterized using Chebyshev polynomials and are accommodated in the optimal trajectory generation programs to incorporate the actuator limits and keep-out constraints. Differentiable collision detection of polytopes is leveraged for optimal collision avoidance. Results obtained from the collocation methods are benchmarked against the existing approaches on an edge computer to outline the performance improvements. Finally, collaborative control scenarios involving multi-agent space systems are considered to demonstrate the technical merits of the proposed work.

cs.RO↗

arXiv2025

Safe Multi-agent Satellite Servicing with Control Barrier Functions

The use of control barrier functions under uncertain pose information of multiple small servicing agents is analyzed for a satellite servicing application. The application consists of modular servicing agents deployed towards a tumbling space object from a mothership. Relative position and orientation of each agent is obtained via fusion of relative range and inertial measurement sensors. The control barrier functions are utilized to avoid collisions with other agents for the application of simultaneously relocating servicing agents on a tumbling body. A differential collision detection and avoidance framework using the polytopic hull of the tumbling space object is utilized to safely guide the agents away from the tumbling object.

eess.SY↗

arXiv2025

Covariance Analysis of Attitude and Angular Rate Estimation using Accelerometers

In this work a method for using accelerometers for the determination of angular velocity and acceleration is presented. Minimum sensor requirements and insights into how an array of accelerometers can be configured to maximize estimator performance are considered. The framework presented utilizes linear least squares to estimate functions that are quadratic in angular velocity. Simple methods for determining the sign of the spin axis and the linearized covariance approximation are presented and found to perform quite effectively when compared to results obtained by Monte Carlo.

eess.SY↗

arXiv2024

Disturbance-Robust Backup Control Barrier Functions: Safety Under Uncertain Dynamics

Obtaining a controlled invariant set is crucial for safety-critical control with control barrier functions (CBFs) but is non-trivial for complex nonlinear systems and constraints. Backup control barrier functions allow such sets to be constructed online in a computationally tractable manner by examining the evolution (or flow) of the system under a known backup control law. However, for systems with unmodeled disturbances, this flow cannot be directly computed, making the current methods inadequate for assuring safety in these scenarios. To address this gap, we leverage bounds on the nominal and disturbed flow to compute a forward invariant set online by ensuring safety of an expanding norm ball tube centered around the nominal system evolution. We prove that this set results in robust control constraints which guarantee safety of the disturbed system via our Disturbance-Robust Backup Control Barrier Function (DR-bCBF) solution. The efficacy of the proposed framework is demonstrated in simulation, applied to a double integrator problem and a rigid body spacecraft rotation problem with rate constraints.

eess.SY↗

arXiv2024

Magnetic Field Aided Vehicle Localization with Acceleration Correction

This paper presents a novel approach for vehicle localization by leveraging the ambient magnetic field within a given environment. Our approach involves introducing a global mathematical function for magnetic field mapping, combined with Euclidean distance-based matching technique for accurately estimating vehicle position in suburban settings. The mathematical function based map structure ensures efficiency and scalability of the magnetic field map, while the batch processing based localization provides continuity in pose estimation. Additionally, we establish a bias estimation pipeline for an onboard accelerometer by utilizing the updated poses obtained through magnetic field matching. Our work aims to showcase the potential utility of magnetic fields as supplementary aids to existing localization methods, particularly beneficial in scenarios where Global Positioning System (GPS) signal is restricted or where cost-effective navigation systems are required.

cs.RO↗

arXiv2024

Robust Proximity Operations using Probabilistic Markov Models

A Markov decision process-based state switching is devised, implemented, and analyzed for proximity operations of various autonomous vehicles. The framework contains a pose estimator along with a multi-state guidance algorithm. The unified pose estimator leverages the extended Kalman filter for the fusion of measurements from rate gyroscopes, monocular vision, and ultra-wideband radar sensors. It is also equipped with Mahalonobis distance-based outlier rejection and under-weighting of measurements for robust performance. The use of probabilistic Markov models to transition between various guidance modes is proposed to enable robust and efficient proximity operations. Finally, the framework is validated through an experimental analysis of the docking of two small satellites and the precision landing of an aerial vehicle.

cs.RO↗

arXiv2024

Pose estimation of CubeSats via sensor fusion and Error-State Extended Kalman Filter

A pose estimation technique based on error-state extended Kalman that fuses angular rates, accelerations, and relative range measurements is presented in this paper. An unconstrained dynamic model with kinematic coupling for a thrust-capable satellite is considered for the state propagation, and a pragmatic measurement model of the rate gyroscope, accelerometer, and an ultra-wideband radio are leveraged for the measurement update. The error-state extended Kalman filter framework is formulated for pose estimation, and its performance has been analyzed via several simulation scenarios. An application of the pose estimator for proximity operations and scaffolding formation of CubeSat deputies relative to their mother-ship is outlined. Finally, the performance of the error-state extended Kalman filter is demonstrated using experimental analysis consisting of a 3-DOF thrust cable satellite mock-up, rate gyroscope, accelerometer, and ultra-wideband radar modules.

eess.SY↗

arXiv2024

A Scalable Tabletop Satellite Automation Testbed:Design And Experiments

This paper presents a detailed system design and component selection for the Transforming Proximity Operations and Docking Service (TPODS) module, designed to gain custody of uncontrolled resident space objects (RSOs) via rendezvous and proximity operation (RPO). In addition to serving as a free-flying robotic manipulator to work with cooperative and uncooperative RSOs, the TPODS modules are engineered to have the ability to cooperate with one another to build scaffolding for more complex satellite servicing activities. The structural design of the prototype module is inspired by Tensegrity principles, minimizing the structural mass of the modules frame. The prototype TPODS module is fabricated using lightweight polycarbonate with an aluminum or carbon fiber frame. The inner shell that houses various electronic and pneumatic components is 3-D printed using ABS material. Four OpenMV H7 R1 cameras are used for the pose estimation of resident space objects (RSOs), including other TPODS modules. Compressed air supplied by an external source is used for the initial testing and can be replaced by module-mounted nitrogen pressure vessels for full on-board propulsion later. A Teensy 4.1 single-board computer is used as a central command unit that receives data from the four OpenMV cameras, and commands its thrusters based on the control logic.

cs.RO↗

arXiv2024

Estimation of inertial properties of a rigid structure maneuvered by satellite modules

The LASR Laboratory is investigating the use of free-flying spacecraft modules in several on-orbit, servicing and manufacturing (OSAM) activities. Previous work consists of the system development and testing of the aforementioned thrust-capable modules. This study makes advancements to devise, implement and validate an algorithm for the estimation of inertial parameters of a rigid structure, to be maneuvered with the help of Transforming Proximity Operations and Docking Service (TPODS) satellite modules. The primary contribution of this activity is observability analysis to infer a conducive input sequence for estimating the inertial parameters. For the experimental validation of proposed estimation algorithm, real-time pose measurements are logged through the VICON motion capture system and the recorded data is utilized to assess the performance of the estimation algorithm to predict mass and moment of inertia of an isolated TPODS module.

eess.SY↗

arXiv2024

Proximity operations of CubeSats via sensor fusion of ultra-wideband range measurements with rate gyroscopes, accelerometers and monocular vision

A robust pose estimation algorithm based on an extended Kalman filter using measurements from accelerometers, rate gyroscopes, monocular vision and ultra-wideband radar is presented. The sensor fusion and pose estimation algorithm incorporates Mahalonobis distance-based outlier rejection and under-weighting of measurements for robust filter performance in the case of sudden range measurements led by the absence of measurements due to range limitations of radar transceivers. The estimator is further validated through an experimental analysis using low-cost radar, IMU and camera sensors. The pose estimate is utilized to perform proximity operations and docking of Transforming Proximity Operations and Docking Service (TPODS) satellite modules with a fixed target.

eess.SY↗

arXiv2024

Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling Autonomy

Sampling autonomy for icy moon lander missions requires understanding of topographic and photometric properties of the sampling terrain. Unavailability of high resolution visual datasets (either bird-eye view or point-of-view from a lander) is an obstacle for selection, verification or development of perception systems. We attempt to alleviate this problem by: 1) proposing Graphical Utility for Icy moon Surface Simulations (GUISS) framework, for versatile stereo dataset generation that spans the spectrum of bulk photometric properties, and 2) focusing on a stereo-based visual perception system and evaluating both traditional and deep learning-based algorithms for depth estimation from stereo matching. The surface reflectance properties of icy moon terrains (Enceladus and Europa) are inferred from multispectral datasets of previous missions. With procedural terrain generation and physically valid illumination sources, our framework can fit a wide range of hypotheses with respect to visual representations of icy moon terrains. This is followed by a study over the performance of stereo matching algorithms under different visual hypotheses. Finally, we emphasize the standing challenges to be addressed for simulating perception data assets for icy moons such as Enceladus and Europa. Our code can be found here: https://github.com/nasa-jpl/guiss.

cs.CV↗