Instantaneous Planning, Control and Safety for Navigation in Unknown Underwater Spaces
- URL: http://arxiv.org/abs/2604.05310v1
- Date: Tue, 07 Apr 2026 01:32:29 GMT
- Title: Instantaneous Planning, Control and Safety for Navigation in Unknown Underwater Spaces
- Abstract summary: Navigating autonomous underwater vehicles (AUVs) in unknown environments is significantly challenging.<n>These factors pose challenges in accurate global localization, reliable communication, and obstacle avoidance.<n>We propose an integrated planning and control framework that leverages real time sensor data to dynamically induce closed loop AUV trajectories.
- Score: 0.21108097398435333
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Navigating autonomous underwater vehicles (AUVs) in unknown environments is significantly challenging due to poor visibility, weak signal transmission, and dynamic water currents. These factors pose challenges in accurate global localization, reliable communication, and obstacle avoidance. Local sensing provides critical real time environmental data to enable online decision making. However, the inherent noise in underwater sensor measurements introduces uncertainty, complicating planning and control. To address these challenges, we propose an integrated planning and control framework that leverages real time sensor data to dynamically induce closed loop AUV trajectories, ensuring robust obstacle avoidance and enhanced maneuverability in tight spaces. By planning motion based on pre designed feedback controllers, the approach reduces the computational complexity needed for carrying out online optimizations and enhances operational safety in complex underwater spaces. The proposed method is validated through ROS Gazebo simulations on the RexRov AUV, demonstrating its efficacy. Its performance is evaluated by comparison against PID based tracking methods, and quantifying localization errors in dead reckoning as the AUV transitions into the target communication range.
Related papers
- DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction [54.94692733670454]
DESCENT is a transformer-based architecture designed to handle heterogeneous dynamics and strict topological constraints.<n>Our approach features a Potential Reachable Set (PRS) context sampling mechanism that adaptively collects airfield environment context.<n>Our evaluations on the Amelia-10 benchmark demonstrate significant performance improvements over state-of-the-art baselines.
arXiv Detail & Related papers (2026-08-26T16:49:50Z) - A Survey on Sensor-based Planning and Control for Unmanned Underwater Vehicles [2.6706790642027194]
This survey examines recent sensor-based planning and control methods for Unmanned Underwater Vehicles (UUVs)<n>The focus is on reactive local planning layers that adapt to real-time sensor inputs such as SONAR and Inertial Measurement Units (IMU)<n>The survey categorizes the existing literature into decoupled and coupled architectures for sensor-based planning and control.
arXiv Detail & Related papers (2026-04-06T00:40:37Z) - Safe Path Planning and Observation Quality Enhancement Strategy for Unmanned Aerial Vehicles in Water Quality Monitoring Tasks [7.258114996625529]
spectral remote sensing technology is widely used in water quality monitoring.<n>In dynamic environments, varying illumination conditions, such as shadows and specular reflection, can cause severe spectral distortion.<n>This paper proposes an active path planning method for dynamic light and shadow disturbance avoidance.
arXiv Detail & Related papers (2025-12-24T14:26:20Z) - Fixed-time control with prescribed performance for path following of underwater gliders [1.3149034455953847]
This paper presents a fixed-time prescribed performance control scheme for the 3D path following of underwater gliders.<n>The primary contribution is the integration of a finite-time performance function within a fixed-time control framework.<n>A second key contribution is the development of a fixed-time sliding mode disturbance observer.
arXiv Detail & Related papers (2025-12-23T20:11:25Z) - 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) - Secure Low-altitude Maritime Communications via Intelligent Jamming [53.42658269206017]
Low-altitude wireless networks (LAWNs) have emerged as a viable solution for maritime communications.<n>The open and clear UAV communication channels make maritime LAWNs vulnerable to eavesdropping attacks.<n>We propose a low-altitude maritime communication system that employs intelligent jamming to counter dynamic eavesdroppers.
arXiv Detail & Related papers (2025-11-10T03:16:19Z) - 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) - Depth-Constrained ASV Navigation with Deep RL and Limited Sensing [43.785833390490446]
We propose a reinforcement learning framework for ASV navigation under depth constraints.<n>To enhance environmental awareness, we integrate GP regression into the RL framework.<n>We demonstrate effective sim-to-real transfer, ensuring that trained policies generalize well to real-world aquatic conditions.
arXiv Detail & Related papers (2025-04-25T10:56:56Z) - Low-altitude UAV Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning [72.23178920029957]
This paper presents a satellite-maritime communication system assisted by low-altitude unmanned aerial vehicle (UAV) friendly-jamming.<n>We formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP)<n>In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process.<n>We then propose a transformer-enhanced soft actor-critic (TransSAC) algorithm, which is a generative artificial intelligence-enabled deep reinforcement learning approach.
arXiv Detail & Related papers (2025-01-26T10:13:51Z) - OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
Trajectory prediction is fundamental in computer vision and autonomous driving.
Existing approaches in this field often assume precise and complete observational data.
We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
arXiv Detail & Related papers (2024-04-02T18:30:29Z) - 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)
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.