Input-Constrained Spatiotemporal Tubes for Safe Navigation of Unknown Euler-Lagrange Systems in Dynamic Environments

• URL: http://arxiv.org/abs/2607.08189v1
• Date: Thu, 09 Jul 2026 07:39:39 GMT
• Title: Input-Constrained Spatiotemporal Tubes for Safe Navigation of Unknown Euler-Lagrange Systems in Dynamic Environments
• Abstract summary: 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.
• Score: 2.7222301668137483
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Safe navigation in dynamic environments is challenging when system dynamics are unknown and actuator inputs are limited. Existing methods either rely on accurate models, require online optimization, or do not explicitly account for input constraints. This paper presents a real-time control framework for unknown Euler-Lagrange systems that guarantees finite-time reach-avoid-stay (FT-RAS) specifications while respecting actuator limits. We extend the spatiotemporal tube (STT) framework by incorporating input constraints into the controller design and derive offline-verifiable feasibility conditions that relate the available control authority to the tube design and uncertainty bounds. The resulting framework is approximation-free and computationally efficient, making it suitable for real-time implementation. 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.

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)
• SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors [48.60897189520386]
  We introduce the JEPA-style model for real-time quadrotor control.<n>We combine a latent dynamics model with a novel physics-inspired prober that maps frozen latents to interpretable state.<n>To reduce the dependence on expensive and unsafe real-world data collection, we develop a structured pipeline for automated dataset generation.
  arXiv  Detail & Related papers  (2026-06-22T15:00:59Z)
• Safe Polytope-in-Polytope Motion Planning and Control with Control Barrier Functions [45.68209588239261]
  This work proposes a safe local motion planning and control method that guarantees that a polytopic robot footprint stays inside a convex free-space region.<n>The proposed free-space formulation does not need any obstacle detection or segmentation.<n>The approach is validated in simulation with an autonomous surface vehicle and on hardware with a non-holonomic mobile robot.
  arXiv  Detail & Related papers  (2026-06-08T16:44:28Z)
• 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)
• 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)
• Real-Time Spatiotemporal Tubes for Dynamic Unsafe Sets [2.7222301668137483]
  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.
  arXiv  Detail & Related papers  (2025-12-05T21:00:43Z)
• 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)
• Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
  We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
  arXiv  Detail & Related papers  (2025-04-30T13:47:25Z)
• Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments [0.0]
  This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers.
  arXiv  Detail & Related papers  (2024-10-03T17:50:19Z)

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.