Dynamic unmanned aerial vehicle path planning for rescue missions using trajectory-predictive and attention-enhanced deep recurrent SARSA

Abstract

Autonomous unmanned aerial vehicle path planning in rescue missions must cope with dynamically moving targets and obstacles, where decision-making based solely on instantaneous observations often becomes myopic and fails to anticipate future motion behaviors. To address this issue, this paper proposes a trajectory-predictive and attention-enhanced deep recurrent State-Action-Reward-State-Action (SARSA) algorithm (PA-DR-SARSA). This algorithm incorporates future motion information into on-policy reinforcement learning through a Gated Recurrent Unit–based trajectory prediction module that learns temporal motion patterns from historical observation sequences. Short-horizon trajectory forecasts are fused with current observations and attention-enhanced features to construct a prediction-enhanced decision representation for SARSA action-value evaluation. Furthermore, an attention mechanism is introduced to adaptively weight predictive and instantaneous features, enabling the agent to prioritize decision-relevant motion information. Moreover, a risk-aware reward shaping strategy leverages these predicted trajectories to guide proactive action evaluation under dynamic uncertainty. Simulation results in dynamic grid-based rescue environments demonstrate that, compared with representative planning and reinforcement learning baselines, the proposed algorithm reduces average path length by up to 13.2% and turning points by up to 39.3%, while improving dynamic obstacle avoidance and task success rates by up to 17.2% and 23.7%, respectively, without compromising on-policy learning stability.