Real-Time Spatiotemporal Tubes for Dynamic Unsafe Sets

• URL: http://arxiv.org/abs/2512.06151v1
• Date: Fri, 05 Dec 2025 21:00:43 GMT
• Title: Real-Time Spatiotemporal Tubes for Dynamic Unsafe Sets
• Abstract summary: We present a real-time control framework for nonlinear pure-stay systems with unknown dynamics.<n>We provide formal guarantees for obstacle avoidance and on-time completion.<n>The framework is demonstrated through simulations and hardware experiments on a mobile robot and an aerial vehicle.
• Score: 2.7222301668137483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a real-time control framework for nonlinear pure-feedback systems with unknown dynamics to satisfy reach-avoid-stay tasks within a prescribed time in dynamic environments. To achieve this, we introduce a real-time spatiotemporal tube (STT) framework. An STT is defined as a time-varying ball in the state space whose center and radius adapt online using only real-time sensory input. A closed-form, approximation-free control law is then derived to constrain the system output within the STT, ensuring safety and task satisfaction. We provide formal guarantees for obstacle avoidance and on-time task completion. The effectiveness and scalability of the framework are demonstrated through simulations and hardware experiments on a mobile robot and an aerial vehicle, navigating in cluttered dynamic environments.

Related papers

• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• Spatiotemporal Agility: Time-Constrained Reinforcement Learning for Vision-Guided Dynamic Quadrupedal Interception [3.372164928871513]
  Legged robots require robust agility to perceive and interact with complex and dynamic environments.<n>Most existing quadruped locomotion works rely on velocity-tracking policy, which struggle to reach precise targets within strict temporal constraints.<n>This paper proposes an integrated framework that combines a vision module for landing point and time prediction with a direct position and time conditioned RL locomotion policy.
  arXiv  Detail & Related papers  (2026-08-07T07:42:19Z)
• Input-Constrained Spatiotemporal Tubes for Safe Navigation of Unknown Euler-Lagrange Systems in Dynamic Environments [2.7222301668137483]
  This paper presents a real-time control framework for unknowngrange systems that guarantees finite-time reach-avoidtemporal navigation (FT-RAS)<n>We extend our framework by input constraints into the controller design and derive offlineverifiable-verifiable conditions that relate control authority to the tube design and available uncertainty.<n>The proposed approach is validated through simulations on a mobile robot, a quadrotor, and a spacecraft, together with hardware experiments on a mobile robot demonstrating safe navigation while satisfying actuator constraints.
  arXiv  Detail & Related papers  (2026-07-09T07:39:39Z)
• Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments [14.321190258774358]
  We present a real-time control architecture that integrates a 3D LIDAR perception-based composite control barrier function.<n>We validate the system through multiple field experiments in underground environments.
  arXiv  Detail & Related papers  (2026-05-15T09:42:13Z)
• Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control [50.28263951510334]
  We propose a hybrid hierarchical control framework for reactive dexterous grasping.<n>The proposed framework explicitly decouples high-level spatial intent from low-level joint execution.<n>We extensively validate the proposed framework through a rigorous simulation-to-reality pipeline.
  arXiv  Detail & Related papers  (2026-05-05T04:49:38Z)
• Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers [64.35403680946182]
  Device-free fall detection utilizing WiFi Channel State Information (CSI) has emerged as a promising solution for elderly health monitoring.<n>Existing deep learning approaches suffer from severe performance degradation when deployed in unseen environments.<n>We propose a robust, domain-general framework featuring a novel Attention-Enhanced CNN-Transformer hybrid architecture.
  arXiv  Detail & Related papers  (2026-04-23T03:28:12Z)
• ONRAP: Occupancy-driven Noise-Resilient Autonomous Path Planning [4.58403099322105]
  We propose a practical, implementation-friendly planner that operates on occupancy grids.<n>It generates ego-centric, kinematically feasible paths that safely navigate through static and dynamic obstacles.<n>We validate our approach in simulation with severe localization and perception noises.
  arXiv  Detail & Related papers  (2026-02-14T03:27:46Z)
• Spatiotemporal Tubes for Probabilistic Temporal Reach-Avoid-Stay Task in Uncertain Dynamic Environment [2.7222301668137483]
  We develop a real-time tube synthesis procedure that accounts for time-varying uncertain obstacles.<n>We derive a closed-form, approximation-free control law that confines the system trajectory within the tube.<n>Our method offers a formal guarantee for probabilistic avoidance and finite-time task completion.
  arXiv  Detail & Related papers  (2025-12-25T04:06:35Z)
• Learning Spatiotemporal Tubes for Temporal Reach-Avoid-Stay Tasks using Physics-Informed Neural Networks [2.7222301668137483]
  This paper presents a Spatiotemporal Tube (STT)-based control framework for general control-affine nonlinear pure-feedback systems.<n>The framework is validated through two case studies involving a mobile robot and an aerial vehicle navigating through cluttered environments.
  arXiv  Detail & Related papers  (2025-12-09T05:08:52Z)
• Spatiotemporal Tubes for Differential Drive Robots with Model Uncertainty [2.4919077201316067]
  This paper presents a Spatiotemporal Tube (STT)-based control framework for differential-drive mobile robots with dynamic uncertainties and external disturbances.<n>The proposed framework is validated through simulation studies on a differential-drive robot and benchmarked against state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-12-05T07:43:23Z)
• Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
  Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
  arXiv  Detail & Related papers  (2025-10-28T10:05:53Z)
• Forecasting Continuous Non-Conservative Dynamical Systems in SO(3) [51.510040541600176]
  We propose a novel approach to modeling the rotation of moving objects in computer vision.<n>Our approach is agnostic to energy and momentum conservation while being robust to input noise.<n>By learning to approximate object dynamics from noisy states during training, our model attains robust extrapolation capabilities in simulation and various real-world settings.
  arXiv  Detail & Related papers  (2025-08-11T09:03:10Z)
• NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
  We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
  arXiv  Detail & Related papers  (2025-06-23T14:28:30Z)
• Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy [88.8665000676562]
  Prior methods often simplify the problem to low-speed or 2D settings, limiting their applicability to real-world 3D tasks.<n>To mitigate data scarcity, we introduce a novel simulation framework and benchmark grounded in reduced-order dynamics.<n>We propose Dynamics Informed Diffusion Policy (DIDP), a framework that integrates imitation pretraining with physics-informed test-time adaptation.
  arXiv  Detail & Related papers  (2025-05-23T03:28:25Z)
• Neural Configuration-Space Barriers for Manipulation Planning and Control [14.758869638207251]
  Planning and control for high-dimensional robot manipulators in cluttered, dynamic environments require both computational efficiency and robust safety guarantees.<n>We propose a unified framework for motion planning and control that formulates safety constraints as CDF barriers.<n>A CDF barrier approximates the local free configuration space, substantially reducing the number of collision-checking operations during motion planning.
  arXiv  Detail & Related papers  (2025-03-06T20:00:56Z)
• Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments [49.30744329170107]
  We propose a novel approach for optimal online motion planning with minimal information about dynamic obstacles.<n>The proposed methodology combines Monte Carlo Tree Search (MCTS), for online optimal planning via model simulations, with Velocity Obstacles (VO), for obstacle avoidance.<n>We show the superiority of our methodology with respect to state-of-the-art planners, including Non-linear Model Predictive Control (NMPC), in terms of improved collision rate, computational and task performance.
  arXiv  Detail & Related papers  (2025-01-16T16:45:08Z)

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.