SWIFT-Nav: Stability-Aware Waypoint-Level TD3 with Fuzzy Arbitration for UAV Navigation in Cluttered Environments
- URL: http://arxiv.org/abs/2512.16027v1
- Date: Wed, 17 Dec 2025 23:19:06 GMT
- Title: SWIFT-Nav: Stability-Aware Waypoint-Level TD3 with Fuzzy Arbitration for UAV Navigation in Cluttered Environments
- Abstract summary: We propose a TD3-based navigation framework that achieves fast, stable convergence to obstacle-aware paths.<n>We show that combining TD3 with replay prioritisation, exploration, and fuzzy-safety rules yields a robust and deployable solution for UAV navigation in cluttered scenes.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Efficient and reliable UAV navigation in cluttered and dynamic environments remains challenging. We propose SWIFT-Nav: Stability-aware Waypoint-level Integration of Fuzzy arbitration and TD3 for Navigation, a TD3-based navigation framework that achieves fast, stable convergence to obstacle-aware paths. The system couples a sensor-driven perception front end with a TD3 waypoint policy: the perception module converts LiDAR ranges into a confidence-weighted safety map and goal cues, while the TD3 policy is trained with Prioritised Experience Replay to focus on high-error transitions and a decaying epsilon-greedy exploration schedule that gradually shifts from exploration to exploitation. A lightweight fuzzy-logic layer computes a safety score from radial measurements and near obstacles, gates mode switching and clamps unsafe actions; in parallel, task-aligned reward shaping combining goal progress, clearance, and switch-economy terms provides dense, well-scaled feedback that accelerates learning. Implemented in Webots with proximity-based collision checking, our approach consistently outperforms baselines in trajectory smoothness and generalization to unseen layouts, while preserving real-time responsiveness. These results show that combining TD3 with replay prioritisation, calibrated exploration, and fuzzy-safety rules yields a robust and deployable solution for UAV navigation in cluttered scenes.
Related papers
- Embodied-Navigator: Point, Think, Memorize, and Align for Efficient Navigation [68.02899606570223]
We propose TAMP-Nav, a unified framework for efficient embodied navigation.<n>First, we introduce a Pixel-to-3D Action Formulation (Point) that reformulates navigation into 2D visual coordinates.<n>Second, we propose an integrated Selective Reasoning and Anchor-Trajectory Memory mechanism (Think and Memorize)
arXiv Detail & Related papers (2026-08-18T08:37:08Z) - DynFly: Dynamic-Aware Continuous Trajectory Generation for UAV Vision-Language Navigation in Urban Environments [55.73595346639892]
We propose DynFly to bridge high-level navigation reasoning and executable UAV motion.<n>DynFly represents expert trajectories in B-spline control-point space and employs a Spline-DiT generator to learn conditional trajectory generation.<n>Experiments on the OpenUAV UAV-VLN benchmark show that DynFly improves both navigation performance and trajectory quality.
arXiv Detail & Related papers (2026-06-30T13:33:48Z) - Beyond Waypoints: A Trajectory-Centric Waypointing Paradigm for Vision-Language Navigation [79.12557132788139]
Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural-language instructions while navigating in real-world-like environments.<n>Most VLN-CE approach-es adopt a three-stage framework: a waypoint predictor proposes waypoints, and a navigator selects the best waypoint.<n>We introduce a novel paradigm called Trajectory Waypoint, which grounds each candidate waypoint in an executable trajectory.
arXiv Detail & Related papers (2026-06-05T13:11:39Z) - LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting [8.537296437078387]
We present LiftNav, a hybrid navigation framework built on GSFusion's TSDF+GS dual map.<n>This design enables flexible semantic navigation without dense 3D embeddings.<n>Compared against a state-of-the-art radiance field baseline we show a 100% feasibility rate and shorter trajectories.
arXiv Detail & Related papers (2026-05-29T14:44:59Z) - SaferPath: Hierarchical Visual Navigation with Learned Guidance and Safety-Constrained Control [9.692780954657229]
Visual navigation is a core capability for mobile robots, yet end-to-end learning-based methods often struggle with generalization and safety in unseen, cluttered, or narrow environments.<n>We propose SaferPath, a hierarchical visual navigation framework that leverages learned guidance from existing end-to-end models and refines it through a safety-constrained optimization-control module.
arXiv Detail & Related papers (2026-03-02T14:17:37Z) - Deep Learning-based Robust Autonomous Navigation of Aerial Robots in Dense Forests [7.726773291477927]
This work presents an improved deep learning-based navigation framework that integrates semantically enhanced depth encoding with neural motion-primitive evaluation for robust flight in cluttered forests.<n>The system is deployed on a custom quadrotor in three boreal forest environments, achieving fully autonomous completion in all flights in moderate and dense clutter, and 12 out of 15 flights in highly dense underbrush.
arXiv Detail & Related papers (2025-12-19T13:19:33Z) - VLA-AN: An Efficient and Onboard Vision-Language-Action Framework for Aerial Navigation in Complex Environments [12.689250855332569]
VLA-AN is a framework dedicated to autonomous drone navigation in complex environments.<n>It addresses four major limitations of existing large aerial navigation models.<n>It achieves a maximum single-task success rate of 98.1%.
arXiv Detail & Related papers (2025-12-17T10:02:55Z) - Hybrid DQN-TD3 Reinforcement Learning for Autonomous Navigation in Dynamic Environments [1.241204035960416]
This paper presents a hierarchical path-planning and control framework that combines a high-level Deep Q-Network (DQN) for discrete sub-goal selection with a low-level Twin Delayed Deep Deterministic Policy Gradient (TD3) controller for continuous actuation.<n>We design a practical reward shaping scheme (direction, distance, obstacle avoidance, action smoothness, collision penalty, time penalty, and progress) together with a LiDAR-based safety gate that prevents unsafe motions.
arXiv Detail & Related papers (2025-10-30T16:12:01Z) - HUNT: High-Speed UAV Navigation and Tracking in Unstructured Environments via Instantaneous Relative Frames [50.83645076723809]
HUNT (High-speed UAV Navigation and Tracking) is a real-time framework that unifies, acquisition, and tracking within a single relative formulation.<n>Trials in dense forests, container compounds, and search-and-rescue operations with vehicles and mannequins demonstrate robust autonomy where global methods fail.
arXiv Detail & Related papers (2025-09-23T18:07:10Z) - DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation [73.80968452950854]
Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural language instructions through free-form 3D spaces.<n>Existing VLN-CE approaches typically use a two-stage waypoint planning framework.<n>We propose DAgger Diffusion Navigation (DifNav) as an end-to-end optimized VLN-CE policy.
arXiv Detail & Related papers (2025-08-13T02:51:43Z) - 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) - ETPNav: Evolving Topological Planning for Vision-Language Navigation in
Continuous Environments [56.194988818341976]
Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments.
We propose ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments.
ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets.
arXiv Detail & Related papers (2023-04-06T13:07:17Z)
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.