mlsanityData to 14 Oct 2026

Dynamic-TD3: A Novel Algorithm for UAV Path Planning with Dynamic Obstacle Trajectory Prediction

arXiv 2605.00059 ↗First posted 30 Apr 2026cs.RO cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Deep reinforcement learning (DRL) finds extensive application in autonomous drone navigation within complex, high-risk environments. However, its practical deployment faces a safety-exploration dilemma: soft penalty mechanisms encourage risky trial-and-error, while most constraint-based methods suffer degraded performance under sensor noise and intent uncertainty. We propose Dynamic-TD3, a physically enhanced framework that enforces strict safety constraints while maintaining maneuverability by modeling navigation as a Constrained Markov Decision Process (CMDP). This framework integrates an Adaptive Trajectory Relational Evolution Mechanism (ATREM) to capture long-range intentions and employs a Physically Aware Gated Kalman Filter (PAG-KF) to mitigate non-stationary observation noise. The resulting state representation drives a dual-criterion policy that balances mission efficiency against hard safety constraints via Lagrangian relaxation. In experiments with aggressive dynamic threats, this approach demonstrates superior collision avoidance performance, reduced energy consumption, and smoother flight trajectories.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 23 references that are in the map. First posted on arXiv 30 Apr 2026.

Builds on

12 of 23 references.

References · 12

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 297
  5. 2017Control of a Quadrotor with Reinforcement Learningcited by 92
  6. 2018Learning Navigation Behaviors End-to-End with AutoRLcited by 70
  7. 2021Goal-Driven Autonomous Exploration Through Deep Reinforcement Learningcited by 35
  8. 2019Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flightcited by 28
  9. 2024Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulationcited by 21
  10. 2023Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigationcited by 10
  11. 2023State-wise Constrained Policy Optimizationcited by 9
  12. 2025KARL: Kalman-Filter Assisted Reinforcement Learner for Dynamic Object Tracking and Graspingcited by 1

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  2. 2026Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation
  3. 2025AgilePilot: DRL-Based Drone Agent for Real-Time Motion Planning in Dynamic Environments by Leveraging Object Detection
  4. 2024NavRL: Learning Safe Flight in Dynamic Environments
  5. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  6. 2026High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning
  7. 2024Robust Policy Learning for Multi-UAV Collision Avoidance with Causal Feature Selection ↗
  8. 2025LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation