Battery endurance limits commercial quadcopter UAVs to 15–25 minutes per charge — a fundamental propulsive constraint that passive energy harvesting approaches, which recover at most 0. 002% of hover power from structural vibration, cannot meaningfully address at the propulsion level. Existing deep reinforcement learning (DRL) comparative studies for autonomous UAV navigation evaluate algorithms on task-success rate alone, ignoring energy expenditure. This paper proposes an energy-aware multi-objective reward function with a per-step energy penalty (wₑ = −0. 20) and a battery-scaled goal bonus (+200· (1+0. 5·b/100) ), creating a 43% reward differential between energy-efficient and energy-wasteful arrivals. Three algorithms — Deep Q-Network (DQN), Proximal Policy Optimisation (PPO with GAE), and Soft Actor-Critic (SAC with reparameterisation trick and twin critics) — are implemented in pure NumPy and compared across five random seeds over 200, 000 training steps. SAC achieves Pareto-optimality: 82. 2±2. 7% success with 24. 2±1. 8% battery use; PPO: 71. 7±3. 1% / 29. 2±1. 8%; DQN: 57. 8±2. 6% / 36. 1±2. 2%; A*+PID: 43. 5±5. 2% / 48. 9±4. 7% (with full obstacle knowledge). SAC’s 24. 7 pp battery advantage over A*+PID translates to a real-world propulsive energy saving of approximately 49–69 W·h over a nominal 15–20 minute DJI F450 mission. ANOVA yields F = 93. 96 (p < 0. 001) ; all pairwise comparisons are significant after Bonferroni correction; Cohen’s d ≥ 3. 6. Ablation confirms each reward component contributes independently. SAC maintains above 68. 7% success under combined sensor noise and wind disturbance without retraining. All code is available in Appendix A.
OMAR et al. (Tue,) studied this question.