An Autonomous, End-to-End, Convex-Based Framework for Close-Range Rendezvous Trajectory Design and Guidance with Hardware Testbed Validation

• URL: http://arxiv.org/abs/2602.12421v1
• Date: Thu, 12 Feb 2026 21:23:21 GMT
• Title: An Autonomous, End-to-End, Convex-Based Framework for Close-Range Rendezvous Trajectory Design and Guidance with Hardware Testbed Validation
• Abstract summary: This paper presents CORTEX, an autonomous, perception-enabled, real-time trajectory design and guidance framework for close-range rendezvous.<n> CORTEX is validated in high-fidelity software simulation and hardware-in-the-loop experiments.
• Score: 3.551661404820989
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Autonomous satellite servicing missions must execute close-range rendezvous under stringent safety and operational constraints while remaining computationally tractable for onboard use and robust to uncertainty in sensing, actuation, and dynamics. This paper presents CORTEX (Convex Optimization for Rendezvous Trajectory Execution), an autonomous, perception-enabled, real-time trajectory design and guidance framework for close-range rendezvous. CORTEX integrates a deep-learning perception pipeline with convex-optimisation-based trajectory design and guidance, including reference regeneration and abort-to-safe-orbit logic to recover from large deviations caused by sensor faults and engine failures. CORTEX is validated in high-fidelity software simulation and hardware-in-the-loop experiments. The software pipeline (Basilisk) models high-fidelity relative dynamics, realistic thruster execution, perception, and attitude control. Hardware testing uses (i) an optical navigation testbed to assess perception-to-estimation performance and (ii) a planar air-bearing testbed to evaluate the end-to-end guidance loop under representative actuation and subsystem effects. A Monte-Carlo campaign in simulation includes initial-state uncertainty, thrust-magnitude errors, and missed-thrust events; under the strongest case investigated, CORTEX achieves terminal docking errors of $36.85 \pm 44.46$ mm in relative position and $1.25 \pm 2.26$ mm/s in relative velocity. On the planar air-bearing testbed, 18 cases are executed (10 nominal; 8 off-nominal requiring recomputation and/or abort due to simulated engine failure and sensor malfunctions), yielding terminal errors of $8.09 \pm 5.29$ mm in position and $2.23 \pm 1.72$ mm/s in velocity.

Related papers

• Nonlinear Model Predictive Control via Sequential Convex Programming for Drone-to-Drone Docking [0.0]
  This work formulates the docking task as a finite-horizon optimal control problem based on a reduced-order nonlinear model augmented with disturbance states.<n>The resulting problem is solved using sequential convex programming within a receding-horizon framework to generate dynamically feasible docking trajectories.
  arXiv  Detail & Related papers  (2026-08-11T06:27:45Z)
• Layered Outer-Loop Control for Disturbance-Robust Multi-Waypoint UAV Arrival [0.0]
  Disturbance-robust UAV position control is easy to demonstrate in benign simulations but hard to make fast in approach, well behaved near the target, and credible beyond a single benchmark.<n>This letter presents a layered terminal-control architecture for multi-waypoint UAV position control with a staged evaluation across PyBullet, PX4/Gazebo, and hardware.
  arXiv  Detail & Related papers  (2026-06-24T18:58:57Z)
• Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators [9.91824382053006]
  Real-time trajectory guidance for space manipulators is challenging due to the nonlinear coupling between spacecraft bus motion, manipulator dynamics, cone, robustness, and safety constraints.<n>This paper introduces a sequential learning-based warmally-cost-model SCP-chunk warm-starts.<n>Results show that SCP-chunk warm-starts can improve both the efficiency and trajectory guidance of SCP-based terminal programming for space manipulation.
  arXiv  Detail & Related papers  (2026-06-15T21:54:43Z)
• Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations [122.22391796628408]
  We present Bench2Drive-Robust, to our knowledge the first device-centric robustness benchmark for closed-loop end-to-end autonomous driving.<n>We systematically evaluate deployment-oriented perturbations arising from three major sources: camera-stream failures, ego-state estimation errors, and compute-induced control delay.<n>Our results show that these deployment-related perturbations can substantially degrade closed-loop driving performance.
  arXiv  Detail & Related papers  (2026-05-18T08:45:24Z)
• Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing [73.88859384645264]
  Out-Of-Distribution (OOD) generalization to various racetracks utilizes Machine Learning (ML) to encode the mathematical relation between sensor data and vehicle actuation for end-to-end control.<n>This paper presents a DRL method that parameterizes nonlinear vehicle dynamics from the spectral distribution of depth measurements with a non-geometric, physics-informed reward, to infer vehicle time-optimal and overtaking racing controls with an Artificial Neural Network (ANN)<n>The policy outperforms human demonstrations by 12% in OOD tracks on proportionally scaled hardware, by maximizing the friction circle with tire dynamics that resemble an empirical Pacejka tire model.
  arXiv  Detail & Related papers  (2026-04-10T17:12:07Z)
• SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction [46.645872209565574]
  We present a specialized large-scale foundation model for virtual cell perturbation prediction.<n>First, we build a BioNeMo-based training and inference framework that substantially improves data throughput.<n>Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture.<n>Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics.
  arXiv  Detail & Related papers  (2026-03-18T05:51:08Z)
• Intelligent Control of Differential Drive Robots Subject to Unmodeled Dynamics with EKF-based State Estimation [28.37226750522601]
  This work introduces a unified control and state estimation framework that combines a Lyapunov-based nonlinear controller and Adaptive Networks (ANN)<n>The proposed controller leverages the universal approximation property of neural networks to model unknown nonlinearities in real time.<n>To ensure robust state estimation, the EKF fuses inertial measurement unit (IMU) and odometry from monocular, 2D-LiDAR and wheel encoders.
  arXiv  Detail & Related papers  (2026-03-16T07:44:28Z)
• Perception-Aware Time-Optimal Planning for Quadrotor Waypoint Flight [25.084985306310827]
  This paper introduces a unified time-optimal trajectory optimization framework for vision-based quadrotors.<n>It incorporates perception constraints alongside full nonlinear dynamics, rotor actuation limits, aerodynamic effects, camera field-of-view constraints, and convex geometric gate representations.<n>Experiments demonstrate real-world flight speeds up to 9.8 m/s with 0.07 m average tracking error, and closed-loop success rates improved from 55% to 100%.
  arXiv  Detail & Related papers  (2026-03-04T17:22:16Z)
• Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing [74.83272587893508]
  We present a framework for autonomous raceline optimization that combines NURBS-based trajectory representation, CMA-ES global trajectory optimization, and controller-guided spatial feedback.<n>In simulation, our approach achieves a 17.38% lap time reduction compared to a controller parametrized with maximum static acceleration.<n>On real hardware, tested with different tire compounds ranging from high to low friction, we obtain a 7.60% lap time improvement without explicitly parametrizing friction.
  arXiv  Detail & Related papers  (2026-02-17T15:10:44Z)
• SpecFuse: A Spectral-Temporal Fusion Predictive Control Framework for UAV Landing on Oscillating Marine Platforms [6.921143850808118]
  We propose a novel spectral fusion framework that integrates frequency-domain wave time-temporal wave decomposition.<n>It refines predictions real time via IMU data without relying on complex calibration.<n>It achieves a 3.2 cm prediction error, 4.46 cm landing deviation, 98.7% / 87.5% success rates (2,000 / real-world), and 82 ms latency on embedded hardware, outperforming state-of-the-art methods by 44%-48% in accuracy.
  arXiv  Detail & Related papers  (2026-02-17T15:03:05Z)
• ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation [72.78362530982109]
  ARTIS, Agentic Risk-Aware Test-Time Scaling via Iterative Simulation, is a framework that decouples exploration from commitment.<n>We show that naive LLM-based simulators struggle to capture rare but high-impact failure modes.<n>We introduce a risk-aware tool simulator that emphasizes fidelity on failure-inducing actions.
  arXiv  Detail & Related papers  (2026-02-02T06:33:22Z)
• High-Level Multi-Robot Trajectory Planning And Spurious Behavior Detection [41.75258434978792]
  This paper addresses the challenge of identifying spurious executions of plans specified as a Linear Temporal Logic (LTL) formula.<n>We introduce a structured data generation framework based on the Nets-within-Nets (NWN) paradigm.<n>We propose a Transformer-based anomaly detection pipeline that classifies robot trajectories as normal or anomalous.
  arXiv  Detail & Related papers  (2025-10-20T07:47:51Z)
• Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systems [10.101847906979435]
  Unmodeled aerodynamic drag forces from carried payloads can lead to catastrophic outcomes. We argue that adapting the trajectory generation component keeping the controller fixed can improve trajectory tracking. Our experiments in both simulation and on the Crazyflie hardware platform show that changing the planner reduces tracking error by as much as 83%.
  arXiv  Detail & Related papers  (2024-01-10T07:00:07Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)
• Constrained Reinforcement Learning using Distributional Representation for Trustworthy Quadrotor UAV Tracking Control [2.325021848829375]
  We propose a novel trajectory tracker integrating a Distributional Reinforcement Learning disturbance estimator for unknown aerodynamic effects. The proposed estimator Constrained Distributional Reinforced disturbance estimator' (ConsDRED) accurately identifies uncertainties between true and estimated values of aerodynamic effects. We demonstrate our system improves accumulative tracking errors by at least 70% compared with the recent art.
  arXiv  Detail & Related papers  (2023-02-22T23:15:56Z)
• Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds [100.61456258283245]
  This paper presents a multi-layer motion planning and control architecture for autonomous racing. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
  arXiv  Detail & Related papers  (2022-07-22T15:16:54Z)
• Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems [79.67879934935661]
  We study the problem of system identification and adaptive control of unknown ARX systems. We provide finite-time learning guarantees for the ARX systems under both open-loop and closed-loop data collection.
  arXiv  Detail & Related papers  (2021-08-26T18:00:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.