Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation

• URL: http://arxiv.org/abs/2607.01794v1
• Date: Thu, 02 Jul 2026 07:12:24 GMT
• Title: Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation
• Abstract summary: We propose a safety-constrained perception-control integrated framework for UAV navigation.<n>A lightweight network encodes sparse observations into collision-risk-aware features using asymmetric and depthwise separable convolutions.<n> Experiments with varying obstacle densities and flight speeds demonstrate higher success rates, improved safety, and better efficiency.
• Score: 16.850454229093135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of autonomous aerial systems, Unmanned Aerial Vehicles (UAVs) are increasingly deployed in applications such as inspection, environmental monitoring, and rescue, creating growing demand for reliable autonomous navigation. However, autonomous UAV navigation in dense environments remains challenging under sparse perception and dynamic constraints. Most reinforcement learning (RL) methods lack explicit safety mechanisms, leading to unsafe exploration, unstable training, and risky behaviors, especially during high-speed flight. Even in safe RL approaches, safety is often enforced by projecting policy outputs onto a safe action set, which may introduce instability. Meanwhile, many learning-based methods rely on dense inputs or large networks, increasing computational burden and limiting lightweight onboard deployment. Facing the above challenges, we propose a safety-constrained perception-control integrated framework for UAV navigation. A lightweight network encodes sparse observations into collision-risk-aware features using asymmetric and depthwise separable convolutions. We formulate the task as a constrained Markov decision process within a hierarchical control architecture and solve it using a Lagrangian-based safe PPO algorithm. Curriculum learning further improves training stability. Experiments with varying obstacle densities and flight speeds demonstrate higher success rates, improved safety, and better efficiency than existing reinforcement learning baselines.

Related papers

• Learning Agile Navigation in Crowded Environments for Quadruped Robots [19.29089241882416]
  VOP-Nav is a novel navigation system for quadruped robots.<n>It combines the geometric safety of Velocity Obstacles with the agile adaptability of end-to-end learning.<n>VOP-Nav achieves higher success rates than all baselines while balancing speed and collision avoidance.
  arXiv  Detail & Related papers  (2026-07-16T14:16:19Z)
• High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning [7.796497371549364]
  We propose an end-to-end RL framework augmented with model-based safety mechanisms.<n>During training, we design a physics-informed reward structure that provides global navigational guidance.<n>During deployment, we integrate a real-time safety filter that projects the policy outputs onto a provably safe set to enforce strict collision-avoidance constraints.
  arXiv  Detail & Related papers  (2026-02-09T13:47:02Z)
• Leveraging the Power of Ensemble Learning for Secure Low Altitude Economy [64.39232788946173]
  Low Altitude Economy (LAE) holds immense promise for enhancing societal well-being and driving economic growth.<n>This paper investigates ensemble learning for secure LAE, covering research focuses, solutions, and a case study.
  arXiv  Detail & Related papers  (2026-02-07T23:15:58Z)
• 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)
• 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)
• Monocular Obstacle Avoidance Based on Inverse PPO for Fixed-wing UAVs [29.207513994002202]
  Fixed-wing Unmanned Aerial Vehicles (UAVs) are one of the most commonly used platforms for the Low-altitude Economy (LAE) and Urban Air Mobility (UAM)<n>Classical obstacle avoidance systems, which rely on prior maps or sophisticated sensors, face limitations in unknown low-altitude environments and small UAV platforms.<n>This paper proposes a lightweight deep reinforcement learning (DRL) based UAV collision avoidance system.
  arXiv  Detail & Related papers  (2024-11-27T03:03:37Z)
• ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning [48.536695794883826]
  We present ActSafe, a novel model-based RL algorithm for safe and efficient exploration.<n>We show that ActSafe guarantees safety during learning while also obtaining a near-optimal policy in finite time.<n>In addition, we propose a practical variant of ActSafe that builds on latest model-based RL advancements.
  arXiv  Detail & Related papers  (2024-10-12T10:46:02Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning [9.891207216312937]
  Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility. We propose a deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) to guide autonomous UAS to their destinations. Results show that the proposed model can provide accurate and robust guidance and resolve conflict with a success rate of over 99%.
  arXiv  Detail & Related papers  (2021-11-13T04:44:53Z)
• Learning to be Safe: Deep RL with a Safety Critic [72.00568333130391]
  A natural first approach toward safe RL is to manually specify constraints on the policy's behavior. We propose to learn how to be safe in one set of tasks and environments, and then use that learned intuition to constrain future behaviors.
  arXiv  Detail & Related papers  (2020-10-27T20:53:20Z)
• Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems [81.7983463275447]
  Learning-based control algorithms require data collection with abundant supervision for training. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained optimal control with dynamics learning and feedback control.
  arXiv  Detail & Related papers  (2020-05-09T05:57:43Z)

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.