Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments
- URL: http://arxiv.org/abs/2605.15782v1
- Date: Fri, 15 May 2026 09:42:13 GMT
- Title: Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments
- Abstract summary: 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.
- Score: 14.321190258774358
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this work, we address the problem of ensuring real-time safety in autonomous robot navigation, in spatially constrained dynamic environments, by utilizing only onboard sensors. We present a real-time control architecture that integrates a 3D LIDAR perception-based composite control barrier function(CBF)-based safety filter directly into the autonomy pipeline. The proposed perception-driven framework enforces collision avoidance constraints dynamically from onboard point cloud data, thus allowing a large number of constraints to be handled at the control frequency, while remaining minimally invasive to nominal task execution. The safety region is defined as an ellipsoid in the body-frame, consistent with the geometry of the platform, which induces time-varying constraints in the world frame as the robot rotates; this effect is handled through a dedicated formulation of time-varying (CBF) for each LIDAR point. We validate the system through multiple field experiments in underground environments by utilizing a quadruped platform performing a visual inspection task, demonstrating reliable operation in the presence of dynamic obstacles, unsafe high-level references, abrupt localization anomalies, and while traversing through narrow corridors.
Related papers
- RoboNav-Arm: Agentic AI-Driven Navigation and Obstacle Avoidance for Robotic Manipulator in Cluttered Environments [1.1638921255130017]
We propose a framework for safe task execution with effective obstacle avoidance.<n>The proposed approach is evaluated in Gazebo Classic, demonstrating robustness in dynamic scenarios.
arXiv Detail & Related papers (2026-06-25T07:29:11Z) - 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) - 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) - Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots [1.7044368816068716]
This paper presents a unified control framework for robust trajectory tracking and moving obstacle avoidance.<n>The proposed architecture guarantees tracking in the presence of external disturbances while strictly enforcing collision avoidance constraints.<n>The efficacy and versatility of the approach are validated through numerical simulations and extensive real-world experiments.
arXiv Detail & Related papers (2026-04-27T14:20:10Z) - Dynamic Control Barrier Function Regulation with Vision-Language Models for Safe, Adaptive, and Realtime Visual Navigation [5.268770735910558]
We present AlphaAdj, a vision-to-control navigation framework that uses egocentric RGB input to adapt the conservativeness of a CBF safety filter in real time.<n>Results show that AlphaAdj maintains collision-free navigation while improving efficiency (in terms of path length and time to goal) by up to 18.5% relative to fixed settings.
arXiv Detail & Related papers (2026-03-22T09:35:18Z) - SPOT: Spatio-Temporal Obstacle-free Trajectory Planning for UAVs in an Unknown Dynamic Environment [1.9181913148426697]
We develop a 4-dimensional planner, integrated with vision-based Safe Flight Corridor (SFC) generation.<n>We validate our method extensively in both simulation and real-world hardware experiments and benchmark it against state-of-the-art approaches.
arXiv Detail & Related papers (2026-02-01T12:24:12Z) - 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) - 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) - 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) - 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) - Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for
Online Collision Avoidance [95.86944752753564]
We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties.
Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates.
The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
arXiv Detail & Related papers (2020-07-28T07:34:30Z)
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.