Adaptive Obstacle‐Avoidance Path Planning for UAVs Using MobileViT‐Based Multimodal Perception and Deep Reinforcement Learning

ABSTRACT Autonomous navigation in farmland is difficult because weak texture, crop occlusion, moving machinery, and illumination changes affect perception and control simultaneously. This study develops an adaptive unmanned aerial vehicle obstacle‐avoidance framework comprising a MobileViT‐XXS visual encoder, calibrated millimeter‐wave‐radar fusion, spatiotemporal trajectory prediction, an RRT* global planner, and a Soft Actor–Critic (SAC) local controller with semantic safety constraints. Under the matched‐SAC simulation protocol, the proposed perception backbone achieved an average mAP of 87.4%, exceeding YOLOv8n + SAC by 5.8 percentage points and EfficientViT‐L2 + SAC by 3.2 percentage points; the paired scene‐level difference relative to EfficientViT‐L2 + SAC was 3.2 percentage points (95% CI, 1.1–5.3; Holm‐adjusted p = 0.006). The complete simulation framework yielded an obstacle‐avoidance success rate of 88.3%, an average path length of 138.4 m, an energy consumption of 14.2 Wh/km, and a path smoothness of 0.21 rad/m. The matched simulation success‐rate difference relative to YOLOv8n + SAC was 4.8 percentage points (95% CI, −1.2 to 10.8; Holm‐adjusted p = 0.112), and therefore does not establish statistical superiority. Separately, the expanded field ledger contains 100 independent test flights across six scenario groups, below the prespecified target of at least 160; the field evidence is therefore reported descriptively. At 1920 × 1080 resolution, end‐to‐end latency was 49.2 ± 2.5 ms, whereas 3840 × 2160 required 92.1 ± 3.8 ms. A 60‐min 1080p sustained‐load test triggered thermal throttling at approximately 20 min and increased latency from 48.5 to 56.3 ms by 60 min. Accordingly, 1080p is the default closed‐loop mode and 4K is restricted to low‐speed inspection and ablation. The joint reward heatmaps and structured failure log further delimit the tested operating envelope. The results support technical feasibility under the evaluated conditions but not broad agricultural generalization or deployment readiness.