Validation of Space Robotics in Underwater Environments via Disturbance Robustness Equivalency

• URL: http://arxiv.org/abs/2603.00628v1
• Date: Sat, 28 Feb 2026 12:42:24 GMT
• Title: Validation of Space Robotics in Underwater Environments via Disturbance Robustness Equivalency
• Abstract summary: We present an experimental validation framework for space robotics that leverages underwater environments to approximate microgravity dynamics.<n>We formulate the motion planning problem such that the original space mission and the validation mission achieve the same disturbance degree robustness.<n>Our evaluation features a dual-experiment setup: an underwater robot operating under near-neutral buoyancy conditions to validate the planning and control strategy of either an experimental spacecraft platform or a CubeSat in a high-fidelity space dynamics simulator.
• Score: 8.271517121359345
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present an experimental validation framework for space robotics that leverages underwater environments to approximate microgravity dynamics. While neutral buoyancy conditions make underwater robotics an excellent platform for space robotics validation, there are still dynamical and environmental differences that need to be overcome. Given a high-level space mission specification, expressed in terms of a Signal Temporal Logic specification, we overcome these differences via the notion of maximal disturbance robustness of the mission. We formulate the motion planning problem such that the original space mission and the validation mission achieve the same disturbance robustness degree. The validation platform then executes its mission plan using a near-identical control strategy to the space mission where the closed-loop controller considers the spacecraft dynamics. Evaluating our validation framework relies on estimating disturbances during execution and comparing them to the disturbance robustness degree, providing practical evidence of operation in the space environment. Our evaluation features a dual-experiment setup: an underwater robot operating under near-neutral buoyancy conditions to validate the planning and control strategy of either an experimental planar spacecraft platform or a CubeSat in a high-fidelity space dynamics simulator.

Related papers

• Design and Attitude Control of an Underwater Quadruped Robot [12.2300314007703]
  Legged robots are versatile on land, but their use in underwater environments remains limited.<n>This paper presents the design, modeling, and experimental validation of a reproducible underwater quadruped robot.
  arXiv  Detail & Related papers  (2026-09-06T17:19:31Z)
• Demonstration of Space Robot Teleoperation over a Lossy and Delayed Network using ATMOS [21.55000241689123]
  We develop and experimentally validate a control strategy that combines state prediction and trajectory tracking control to perform a docking maneuver.<n>The results highlight the capability of ATMOS to support rapid, reliable, and cost-effective testing of spacecraft teleoperation concepts.
  arXiv  Detail & Related papers  (2026-08-14T07:20:40Z)
• Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning [56.240199425429445]
  Multi-robot motion planning requires each robot to generate collision-free trajectories from local observations.<n>Most existing planners generate trajectories from a static snapshot of the local observation.<n>This paper introduces Simulation-Informed Diffusion (SID), a decentralized framework built on constraint-aware diffusion models.
  arXiv  Detail & Related papers  (2026-05-26T21:17:53Z)
• Learning Smooth and Robust Space Robotic Manipulation of Dynamic Target via Inter-frame Correlation [12.529803316934176]
  In microgravity environments, objects are typically free-floating, lacking the support and frictional constraints found on Earth.<n>This letter proposes a data-driven space robotic manipulation approach that integrates historical temporal information and inter-frame correlation mechanisms.
  arXiv  Detail & Related papers  (2026-03-29T06:27:30Z)
• Marinarium: a New Arena to Bring Maritime Robotics Closer to Shore [13.103990961903408]
  The Marinarium is a stand-alone underwater research facility designed to provide a realistic testbed for maritime and space- analog robotic experimentation.<n>The Marinarium combines a fully instrumented underwater and aerial operational volume, extendable via a retractable roof for real-weather conditions, a digital twin in the SMaRCSim simulator and tight integration with a space robotics laboratory.
  arXiv  Detail & Related papers  (2026-02-26T14:40:20Z)
• Digital Twin Supervised Reinforcement Learning Framework for Autonomous Underwater Navigation [0.0]
  This article investigates issues through the case of the BlueROV2, an open platform widely used for scientific experimentation.<n>We propose a deep reinforcement learning approach based on the Proximal Policy Optimization (PPO) algorithm.<n>Results show that the PPO policy consistently outperforms DWA in highly cluttered environments.
  arXiv  Detail & Related papers  (2025-12-11T18:52:42Z)
• ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
  ASTREA is the first agentic system executed on flight-heritage hardware for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
  arXiv  Detail & Related papers  (2025-09-16T08:52:13Z)
• Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning [43.35947581429867]
  Vision-centric hierarchical embodied models have demonstrated strong potential for long-horizon robotic control.<n>Existing methods lack spatial awareness capabilities, limiting their effectiveness in bridging visual plans to actionable control in complex environments.<n>We propose Spatial Policy (SP), a unified spatial-aware visuomotor robotic manipulation framework via explicit spatial modeling and reasoning.
  arXiv  Detail & Related papers  (2025-08-21T10:24:18Z)
• Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach [0.0]
  This work proposes an implementation of autonomous CA decision-making capabilities on spacecraft based on Reinforcement Learning techniques. The proposed framework considers imperfect monitoring information about the status of the debris in orbit and allows the AI system to effectively learn policies to perform accurate Collision Avoidance Maneuvers (CAMs) The objective is to successfully delegate the decision-making process for autonomously implementing a CAM to the spacecraft without human intervention.
  arXiv  Detail & Related papers  (2023-10-29T10:15:33Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detection [0.0]
  Real-time, automated spacecraft feature recognition is needed to pinpoint the locations of collision hazards. New algorithm SpaceYOLO fuses a state-of-the-art object detector YOLOv5 with a separate neural network based on human-inspired decision processes. Performance in autonomous spacecraft detection of SpaceYOLO is compared to ordinary YOLOv5 in hardware-in-the-loop experiments.
  arXiv  Detail & Related papers  (2023-02-02T02:11:39Z)
• Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
  A fully-autonomous aerial robot for high-speed object grasping has been proposed. As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface. Our approach has been validated in a challenging international competition and has shown outstanding results.
  arXiv  Detail & Related papers  (2021-12-10T11:49:51Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)

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.