Estimating a target's 6-DoF motion in underwater proximity operations is difficult because the chaser lacks target-side proprioception and the available relative observations are sparse, noisy, and often partial (e.g., Ultra-Short Baseline (USBL) positions). Without a motion prior, factor-graph maximum a posteriori estimation is underconstrained: consecutive target states are weakly linked and orientation can drift. We propose a generalized constant-twist motion prior defined on the tangent space of Lie groups that enforces temporally consistent trajectories across all degrees of freedom; in SE(3) it couples translation and rotation in the body frame. We present a ternary factor and derive its closed-form Jacobians based on standard Lie group operations, enabling drop-in use for trajectories on arbitrary Lie groups. We evaluate two deployment modes: (A) an SE(3)-only representation that regularizes orientation even when only position is measured, and (B) a mode with boundary factors that switches the target representation between SE(3) and 3D position while applying the same generalized constant-twist prior across representation changes. Validation on a real-world dynamic docking scenario dataset shows consistent ego-target trajectory estimation through USBL-only and optical relative measurement segments with an improved relative tracking accuracy compared to the noisy measurements to the target. Because the construction relies on standard Lie group primitives, it is portable across state manifolds and sensing modalities.

-- Autonomous tracking of flying aerial objects has important civilian and defense applications, ranging from search and rescue to counter-unmanned aerial systems (counter-UAS). Ground based tracking requires setting up infrastructure, could be range limited, and may not be feasible in remote areas, crowded cities or in dense vegetation areas. Vision based active tracking of aerial objects from another airborne vehicle, e.g., a chaser unmanned aerial vehicle (UA V), promises to fill this important gap, along with serving aerial coordination use cases. Vision-based active tracking by a UA V entails solving two coupled problems: 1) compute-efficient and accurate (target) object detection and target state estimation; and 2) maneuver decisions to ensure that the target remains in the field of view in the future time-steps and favorably positioned for continued detection. As a solution to the first problem, this paper presents a novel integration of standard deep learning based architectures with Kernelized Correlation Filter (KCF) to achieve compute-efficient object detection without compromising accuracy, unlike standalone learning or filtering approaches. The proposed perception framework is validated using a lab-scale setup. For the second problem, to obviate the linearity assumptions and background variations limiting effectiveness of the traditional controllers, we present the use of reinforcement learning to train a neuro-controller for fast computation of velocity maneuvers. New state space, action space and reward formulations are developed for this purpose, and training is performed in simulation using AirSim. The trained model is also tested in AirSim with respect to complex target maneuvers, and is found to outperform a baseline PID control in terms of tracking up-time and average distance maintained (from the target) during tracking. Vision based air-to-air tracking of an aerial object is an important and challenging problem with potential applications spanning collision avoidance, multi-aircraft coordination, and counter unmanned aerial system (counter-UAS) scenarios [1], [2]. In such applications, the ability to track the flying object from another autonomous aircraft or uncrewed aerial vehicle (UA V) - the chaser - would expand operational capabilities, in terms of area, speed and agility of tracking and enable quickness of response that might otherwise be difficult with ground based tracking [3].

Accurate and robust relative pose estimation is crucial for enabling challenging Active Debris Removal (ADR) missions targeting tumbling derelict satellites such as ESA's ENVISA T. This work presents a complete pipeline integrating advanced computer vision techniques with adaptive nonlinear filtering to address this challenge. A Convolutional Neural Network (CNN), enhanced with image preprocess-ing, detects structural markers (corners) from chaser imagery, whose 2D coordinates are converted to 3D measurements using camera modeling. These measurements are fused within an Unscented Kalman Filter (UKF) framework, selected for its ability to handle nonlinear relative dynamics, to estimate the full relative pose. Key contributions include the integrated system architecture and a dual adaptive strategy within the UKF: dynamic tuning of the measurement noise covariance compensates for varying CNN measurement uncertainty, while adaptive tuning of the process noise covariance, utilizing measurement residual analysis, accounts for unmodeled dynamics or maneuvers online. This dual adaptation enhances robustness against both measurement imperfections and dynamic model uncertainties. The performance of the proposed adaptive integrated system is evaluated through high-fidelity simulations using a realistic ENVISA T model, comparing estimates against ground truth under various conditions, including measurement outages. This comprehensive approach offers an enhanced solution for robust onboard relative navigation, significantly advancing the capabilities required for safe proximity operations during ADR missions. INTRODUCTION The capability to estimate the relative pose of uncooperative targets, such as derelict satellites, is critical for enabling future ADR, on-orbit servicing, and space situational awareness missions.

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We investigate a scenario where a chaser spacecraft or satellite equipped with a monocular camera navigates in close proximity to a target spacecraft. The satellite's primary objective is to construct a representation of the operational environment and localize itself within it, utilizing the available image data. We frame the joint task of state trajectory and map estimation as an instance of smoothing-based simultaneous localization and mapping (SLAM), where the underlying structure of the problem is represented as a factor graph. Rather than considering estimation and planning as separate tasks, we propose to control the camera observations to actively reduce the uncertainty of the estimation variables, the spacecraft state, and the map landmarks. This is accomplished by adopting an information-theoretic metric to reason about the impact of candidate actions on the evolution of the belief state. Numerical simulations indicate that the proposed method successfully captures the interplay between planning and estimation, hence yielding reduced uncertainty and higher accuracy when compared to commonly adopted passive sensing strategies.

Autonomous Rendezvous and Docking (RVD) have been extensively studied in recent years, addressing the stringent requirements of spacecraft dynamics variations and the limitations of GNC systems. This paper presents an innovative approach employing Artificial Neural Networks (ANN) trained through Reinforcement Learning (RL) for autonomous spacecraft guidance and control during the final phase of the rendezvous maneuver. The proposed strategy is easily implementable onboard and offers fast adaptability and robustness to disturbances by learning control policies from experience rather than relying on predefined models. Extensive Monte Carlo simulations within a relevant environment are conducted in 6DoF settings to validate our approach, along with hardware tests that demonstrate deployment feasibility. Our findings highlight the efficacy of RL in assuring the adaptability and efficiency of spacecraft RVD, offering insights into future mission expectations.

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 keepout 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.

In the past ten years, deep learning (DL) has profoundly influenced the development of computer vision algorithms, enhancing their performance and robustness in various applications like image classification, segmentation, and object tracking. This momentum has carried into spacecraft pose estimation, where DL-based methods have begun to surpass traditional feature-engineering techniques as reported in the literature [1-3], corner and marker detection algorithms such as Shi-Tomasi, Hough Transform methods [4, 5]. CNNs have the edge over feature-based methods primarily due to their enhanced robustness against poor lighting conditions and their streamlined computational demands. However, when it comes to space imagery, the scenario changes due to the distinct challenges such as high contrast, low signal-to-noise ratio, and inferior sensor resolution, which can diminish accuracy.

We present a fast trajectory optimization algorithm for the soft capture of uncooperative tumbling space objects. Our algorithm generates safe, dynamically feasible, and minimum-fuel trajectories for a six-degree-of-freedom servicing spacecraft to achieve soft capture (near-zero relative velocity at contact) between predefined locations on the servicer spacecraft and target body. We solve a convex problem by enforcing a convex relaxation of the field-of-view constraint, followed by a sequential convex program correcting the trajectory for collision avoidance. The optimization problems can be solved with a standard second-order cone programming solver, making the algorithm both fast and practical for implementation in flight software. We demonstrate the performance and robustness of our algorithm in simulation over a range of object tumble rates up to 10{\deg}/s.

Due to the challenges regarding the limits of their endurance and autonomous capabilities, underwater docking for autonomous underwater vehicles (AUVs) has become a topic of interest for many academic and commercial applications. Herein, we take on the problem of state estimation during an autonomous underwater docking mission. Docking operations typically involve only two actors, a chaser and a target. We leverage the similarities to proximity operations (prox-ops) from spacecraft robotic missions to frame the diverse docking scenarios with a set of phases the chaser undergoes on the way to its target. We use factor graphs to generalize the underlying estimation problem for arbitrary underwater prox-ops. To showcase our framework, we use this factor graph approach to model an underwater homing scenario with an active target as a Simultaneous Localization and Mapping problem. Using basic AUV navigation sensors, relative Ultra-short Baseline measurements, and the assumption of constant dynamics for the target, we derive factors that constrain the chaser's state and the position and trajectory of the target. We detail our front- and back-end software implementation using open-source software and libraries, and verify its performance with both simulated and field experiments. Obtained results show an overall increase in performance against the unprocessed measurements, regardless of the presence of an adversarial target whose dynamics void the modeled assumptions. However, challenges with unmodeled noise parameters and stringent target motion assumptions shed light on limitations that must be addressed to enhance the accuracy and consistency of the proposed approach.