arXiv subjects

Madhur Tiwari

Publications and source records attributed to Madhur Tiwari.

16 recordsLinked to original sources

arXiv2026

Simulation Based Reward Function Validation for Multi-Agent On Orbit Inspection

A proposed method for the control of groups of inspection spacecraft is Multi-Agent Reinforcement Learning (MARL). While MARL has already been employed for this purpose in previous work, the reward functions used focus on reaching a finite set of predetermined inspection points around the target. In this work, we study and develop a generalized reward function for the MARL inspection task informed by the analysis of 3D reconstructions of inspected objects in orbit. Because the reward function is generalized such that any number of images at arbitrary locations may evaluated, we also allow trained agents to have complete control over when images are collected. With this approach, we gather insights into best practices for not only the specific MARL inspection task, but also gain key takeaways informative to the broader inspection task outside of a MARL context.

cs.MA↗

arXiv2026

From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects

On-orbit inspection imagery is crucial as it enables characterization of non-cooperative resident space objects, providing the geometry and structural condition essential for active debris removal and on-orbit servicing mission planning. However, most existing neural implicit surface reconstruction methods have been confined to synthetic or hardware-in-the-loop data with known camera poses and controlled illumination. In this work, we present a pipeline for neural implicit surface reconstruction of non-cooperative space objects from monocular inspection imagery. We demonstrate it on publicly released ISS inspection footage from the STS-119 mission and publicly released on-orbit inspection footage of an H-IIA rocket upper stage. We find that segmentation-based background removal is essential for successful camera pose estimation from real on-orbit footage, where background variation between frames caused direct processing to fail entirely. We further incorporate photometric correction of per-frame exposure variations and analyze its behavior across datasets, finding that performance in shadowed regions varies with the illumination characteristics of the input footage.

cs.CV↗

arXiv2025

Dynamic Scene 3D Reconstruction of an Uncooperative Resident Space Object

Characterization of uncooperative Resident Space Objects (RSO) play a crucial role in On-Orbit Servicing (OOS) and Active Debris Removal (ADR) missions to assess the geometry and motion properties. To address the challenges of reconstructing tumbling uncooperative targets, this study evaluates the performance of existing state-of-the-art 3D reconstruction algorithms for dynamic scenes, focusing on their ability to generate geometrically accurate models with high-fidelity. To support our evaluation, we developed a simulation environment using Isaac Sim to generate physics-accurate 2D image sequences of tumbling satellite under realistic orbital lighting conditions. Our preliminary results on static scenes using Neuralangelo demonstrate promising reconstruction quality. The generated 3D meshes closely match the original CAD models with minimal errors and artifacts when compared using Cloud Compare (CC). The reconstructed models were able to capture critical fine details for mission planning. This provides a baseline for our ongoing evaluation of dynamic scene reconstruction.

cs.CV↗

arXiv2025

Real-time Testing of Satellite Attitude Control With a Reaction Wheel Hardware-In-the-Loop Platform

We propose the Hardware-in-the-Loop (HIL) test of an adaptive satellite attitude control system with reaction wheel health estimation capabilities. Previous simulations and Software-in-the-Loop testing have prompted further experiments to explore the validity of the controller with real momentum exchange devices in the loop. This work is a step toward a comprehensive testing framework for validation of spacecraft attitude control algorithms. The proposed HIL testbed includes brushless DC motors and drivers that communicate using a CAN bus, an embedded computer that executes control and adaptation laws, and a satellite simulator that produces simulated sensor data, estimated attitude states, and responds to actions of the external actuators. We propose methods to artificially induce failures on the reaction wheels, and present related issues and lessons learned.

cs.RO↗

arXiv2025

Physics-Informed EvolveGCN: Satellite Prediction for Multi Agent Systems

In the rapidly evolving domain of autonomous systems, interaction among agents within a shared environment is both inevitable and essential for enhancing overall system capabilities. A key requirement in such multi-agent systems is the ability of each agent to reliably predict the future positions of its nearest neighbors. Traditionally, graphs and graph theory have served as effective tools for modeling inter agent communication and relationships. While this approach is widely used, the present work proposes a novel method that leverages dynamic graphs in a forward looking manner. Specifically, the employment of EvolveGCN, a dynamic graph convolutional network, to forecast the evolution of inter-agent relationships over time. To improve prediction accuracy and ensure physical plausibility, this research incorporates physics constrained loss functions based on the Clohessy-Wiltshire equations of motion. This integrated approach enhances the reliability of future state estimations in multi-agent scenarios.

cs.MA↗

arXiv2025

Adaptive Modified RISE Control for Quadrotors: Enhancing Trajectory Tracking Through Uncertainty Compensation

This paper presents an adaptive modified Robust Inverse of Signum Error (AM-RISE) control method, which achieves reliable trajectory tracking control for a quadrotor unmanned aerial vehicle. The proposed method systematically accounts for gyroscopic effects, rotor dynamics, parametric uncertainties, and external disturbances, ensuring robust performance across varying trajectory speeds. Through novel mathematical manipulation in the error system development, the quadrotor dynamics are expressed in a control-oriented form, which explicitly incorporates the uncertainty in the gyroscopic term and control actuation term. An adaptive modified RISE law is then designed to stabilize both the position and attitude loops of the quadrotor system. A rigorous Lyapunov-based analysis is utilized to prove asymptotic trajectory tracking, where the region of convergence can be made arbitrarily large through judicious control gain selection. Moreover, the stability analysis formally addresses gyroscopic effects and actuator uncertainty. To illustrate the performance of the control law, comparative numerical simulation results are provided, which demonstrate the improved closed-loop performance achieved under varying levels of parametric uncertainty, disturbance magnitudes and trajectory speeds.

eess.SY↗

arXiv2025

Deep Learning Based Dynamics Identification and Linearization of Orbital Problems using Koopman Theory

The study of the Two-Body and Circular Restricted Three-Body Problems in the field of aerospace engineering and sciences is deeply important because they help describe the motion of both celestial and artificial satellites. With the growing demand for satellites and satellite formation flying, fast and efficient control of these systems is becoming ever more important. Global linearization of these systems allows engineers to employ methods of control in order to achieve these desired results. We propose a data-driven framework for simultaneous system identification and global linearization of the Circular, Elliptical and Perturbed Two-Body Problem as well as the Circular Restricted Three-Body Problem around the L1 Lagrange point via deep learning-based Koopman Theory, i.e., a framework that can identify the underlying dynamics and globally linearize it into a linear time-invariant (LTI) system. The linear Koopman operator is discovered through purely data-driven training of a Deep Neural Network with a custom architecture. This paper displays the ability of the Koopman operator to generalize to various other Two-Body systems without the need for retraining. We also demonstrate the capability of the same architecture to be utilized to accurately learn a Koopman operator that approximates the Circular Restricted Three-Body Problem.

math-ph↗

arXiv2025

Adaptive Controller For Simultaneous Spacecraft Attitude Tracking And Reaction Wheel Fault Detection

The attitude control of a spacecraft is integral to achieving mission success. However, failures in actuators such as reaction wheels are detrimental and can often lead to an early end of mission. We propose a Lyapunov-based adaptive controller that can estimate and compensate for reaction wheels degradation simultaneously. The controller incorporates an adaptive update control law with a gradient-based term and an integral concurrent learning term that collects input-output data for online estimation of uncertain parameters. The proposed controller guarantees attitude tracking and its performance is tested through numerical simulations.

math.OC↗

arXiv2025

End-to-End Imitation Learning for Optimal Asteroid Proximity Operations

Controlling spacecraft near asteroids in deep space comes with many challenges. The delays involved necessitate heavy usage of limited onboard computation resources while fuel efficiency remains a priority to support the long loiter times needed for gathering data. Additionally, the difficulty of state determination due to the lack of traditional reference systems requires a guidance, navigation, and control (GNC) pipeline that ideally is both computationally and fuel-efficient, and that incorporates a robust state determination system. In this paper, we propose an end-to-end algorithm utilizing neural networks to generate near-optimal control commands from raw sensor data, as well as a hybrid model predictive control (MPC) guided imitation learning controller delivering improvements in computational efficiency over a traditional MPC controller.

cs.RO↗

arXiv2024

Dwellers in the Deep: Biological Consequences of Dark Oxygen

The striking recent putative detection of "dark oxygen" (dark O$_2$) sources on the abyssal ocean floor in the Pacific at $\sim 4$ km depth raises the intriguing scenario that complex (i.e., animal-like) life could exist in underwater environments sans oxygenic photosynthesis. In this work, we thus explore the possible (astro)biological implications of this discovery. From the available data, we roughly estimate the concentration of dissolved O$_2$ and the corresponding O$_2$ partial pressure, as well as the flux of O$_2$ production, associated with dark oxygen sources. Based on these values, we infer that organisms limited by internal diffusion may reach maximal sizes of $\sim 0.1-1$ mm in habitats with dark O$_2$, while those with circulatory systems might achieve sizes of $\sim 0.1-10$ cm. Optimistically, the estimated dark oxygen flux can potentially support biomass densities up to $\sim 3-30$ g m$^{-2}$, perhaps surpassing typical reported densities at similar depths in global deep-sea surveys. Finally, we outline how oceanic settings with dark O$_2$ may facilitate the origin(s) of life via the emergence of electrotrophy. Our findings indicate that complex life fueled by dark oxygen is plausibly capable of inhabiting submarine environments devoid of photosynthesis on Earth, conceivably extending likewise to extraterrestrial locations such as icy worlds with subsurface oceans (e.g., Enceladus and Europa), which are likely common throughout the Universe.

physics.bio-ph↗

arXiv2024

Leveraging KANs For Enhanced Deep Koopman Operator Discovery

Multi-layer perceptrons (MLP's) have been extensively utilized in discovering Deep Koopman operators for linearizing nonlinear dynamics. With the emergence of Kolmogorov-Arnold Networks (KANs) as a more efficient and accurate alternative to the MLP Neural Network, we propose a comparison of the performance of each network type in the context of learning Koopman operators with control. In this work, we propose a KANs-based deep Koopman framework with applications to an orbital Two-Body Problem (2BP) and the pendulum for data-driven discovery of linear system dynamics. KANs were found to be superior in nearly all aspects of training; learning 31 times faster, being 15 times more parameter efficiency, and predicting 1.25 times more accurately as compared to the MLP Deep Neural Networks (DNNs) in the case of the 2BP. Thus, KANs shows potential for being an efficient tool in the development of Deep Koopman Theory.

cs.LG↗

arXiv2024

SpY: A Context-Based Approach to Spacecraft Component Detection

This paper focuses on autonomously characterizing components such as solar panels, body panels, antennas, and thrusters of an unknown resident space object (RSO) using camera feed to aid autonomous on-orbit servicing (OOS) and active debris removal. Significant research has been conducted in this area using convolutional neural networks (CNNs). While CNNs are powerful at learning patterns and performing object detection, they struggle with missed detections and misclassifications in environments different from the training data, making them unreliable for safety in high-stakes missions like OOS. Additionally, failures exhibited by CNNs are often easily rectifiable by humans using commonsense reasoning and contextual knowledge. Embedding such reasoning in an object detector could improve detection accuracy. To validate this hypothesis, this paper presents an end-to-end object detector called SpaceYOLOv2 (SpY), which leverages the generalizability of CNNs while incorporating contextual knowledge using traditional computer vision techniques. SpY consists of two main components: a shape detector and the SpaceYOLO classifier (SYC). The shape detector uses CNNs to detect primitive shapes of RSOs and SYC associates these shapes with contextual knowledge, such as color and texture, to classify them as spacecraft components or "unknown" if the detected shape is uncertain. SpY's modular architecture allows customizable usage of contextual knowledge to improve detection performance, or SYC as a secondary fail-safe classifier with an existing spacecraft component detector. Performance evaluations on hardware-in-the-loop images of a mock-up spacecraft demonstrate that SpY is accurate and an ensemble of SpY with YOLOv5 trained for satellite component detection improved the performance by 23.4% in recall, demonstrating enhanced safety for vision-based navigation tasks.

cs.CV↗

arXiv2023

Computationally Efficient Data-Driven Discovery and Linear Representation of Nonlinear Systems For Control

This work focuses on developing a data-driven framework using Koopman operator theory for system identification and linearization of nonlinear systems for control. Our proposed method presents a deep learning framework with recursive learning. The resulting linear system is controlled using a linear quadratic control. An illustrative example using a pendulum system is presented with simulations on noisy data. We show that our proposed method is trained more efficiently and is more accurate than an autoencoder baseline.

eess.SY↗

arXiv2023

SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detection

The rapid proliferation of non-cooperative spacecraft and space debris in orbit has precipitated a surging demand for on-orbit servicing and space debris removal at a scale that only autonomous missions can address, but the prerequisite autonomous navigation and flightpath planning to safely capture an unknown, non-cooperative, tumbling space object is an open problem. This requires algorithms for real-time, automated spacecraft feature recognition to pinpoint the locations of collision hazards (e.g. solar panels or antennas) and safe docking features (e.g. satellite bodies or thrusters) so safe, effective flightpaths can be planned. Prior work in this area reveals the performance of computer vision models are highly dependent on the training dataset and its coverage of scenarios visually similar to the real scenarios that occur in deployment. Hence, the algorithm may have degraded performance under certain lighting conditions even when the rendezvous maneuver conditions of the chaser to the target spacecraft are the same. This work delves into how humans perform these tasks through a survey of how aerospace engineering students experienced with spacecraft shapes and components recognize features of the three spacecraft: Landsat, Envisat, Anik, and the orbiter Mir. The survey reveals that the most common patterns in the human detection process were to consider the shape and texture of the features: antennas, solar panels, thrusters, and satellite bodies. This work introduces a novel algorithm SpaceYOLO, which fuses a state-of-the-art object detector YOLOv5 with a separate neural network based on these human-inspired decision processes exploiting shape and texture. Performance in autonomous spacecraft detection of SpaceYOLO is compared to ordinary YOLOv5 in hardware-in-the-loop experiments under different lighting and chaser maneuver conditions at the ORION Laboratory at Florida Tech.

cs.CV↗

arXiv2023

Autonomous Satellite Docking via Adaptive Optimal Output Regulation: A Reinforcement Learning Approach

This paper describes an online off-policy data-driven reinforcement learning based-algorithm to regulate and control the relative position of a deputy satellite in an autonomous satellite docking problem. The optimal control policy is learned under the framework of output regulation problem and adaptive dynamic programming (ADP) by considering the continuous-time linearized model of the satellite. The linearized model of relative motion is used to describe the motion between satellites, and the satellite docking problem is formulated as a linear optimal output regulation problem, in which the feedback-forward optimal controller is used to track a class of references and rejecting a class of disturbances while maintaining the overall system's closed-loop stability. The optimal control problem is presented using a data-driven reinforcement learning based method to regulate the relative position and velocity of the deputy to safely dock with the chief. Using the adaptive optimal output regulation framework, the learned optimal feedback-feedforward gains guarantee optimal transient and steady state performances without any prior knowledge of the dynamics of the studied system. {The states/input information of the underlying dynamical system are instead used to compute the approximated optimal feedback-feedforward control gain matrices.} Reference tracking and disturbance rejection are achieved in an optimal sense without using any modelling information of the physics of the satellites. {Simulation results are presented and demonstrate the efficacy of the proposed method.

eess.SY↗

arXiv2022

Autonomous Satellite Detection and Tracking using Optical Flow

In this paper, an autonomous method of satellite detection and tracking in images is implemented using optical flow. Optical flow is used to estimate the image velocities of detected objects in a series of space images. Given that most objects in an image will be stars, the overall image velocity from star motion is used to estimate the image's frame-to-frame motion. Objects seen to be moving with velocity profiles distinct from the overall image velocity are then classified as potential resident space objects. The detection algorithm is exercised using both simulated star images and ground-based imagery of satellites. Finally, this algorithm will be tested and compared using a commercial and an open-source software approach to provide the reader with two different options based on their need.

astro-ph.IM↗