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

TL;DR

Dynamic-TD3 is a new DRL-based UAV path planning algorithm that enforces safety constraints, predicts obstacle trajectories, and improves collision avoidance in dynamic environments.

Contribution

It introduces a physically enhanced constrained DRL framework with novel mechanisms for long-range intention modeling and noise mitigation.

Findings

Superior collision avoidance in dynamic threat scenarios

Reduced energy consumption during flight

Smoother trajectories compared to existing methods

Abstract

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.