动态调整探索奖励，让无人机更快精准定位目标。
DynCur-Geo: Dynamic Curiosity Reward Shaping for Multimodal Active Geo-Localization
- 根据距离目标远近动态调节探索奖励强度。
- 在复杂场景下定位成功率提升17.3%，搜索路径更短。
- 适合需要快速定位的无人机搜救与应急巡检任务。
主动地理定位使低空无人机从有限的空中观测中搜索指定目标，支持搜救和应急检查等时效性应用。然而，多模态目标线索、视域受限及反馈稀疏导致探索与目标收敛难以平衡。现有基于好奇心的方法在整个搜索过程中固定内在奖励权重，可能导致接近目标后仍持续奖励新奇性，引发绕行。本文提出DynCur-Geo，一种动态好奇心框架，根据剩余目标距离调整预测误差的内在奖励。距离感知门控促进早期探索，并在靠近目标时引导策略转向目标导向行为；基于势能的奖励塑造提供密集进度指引。在多模态、跨场景、灾害影响及长距离设置下的实验表明，该方法在多个基准上均实现一致性能提升。
原文摘要 · Abstract (English)
Active geo-localization enables low-altitude UAVs to search for specified targets from limited local aerial observations, supporting time-sensitive applications such as search and rescue and emergency inspection. However, multimodal target cues, restricted views, and sparse feedback make it difficult to balance exploration with target convergence. Existing curiosity-driven methods assign a fixed intrinsic-reward weight throughout search, which can continue rewarding novelty after the agent nears the target and induce detours. We propose DynCur-Geo, a dynamic curiosity framework that adjusts prediction-error intrinsic reward according to remaining target distance. A distance-aware gate encourages early exploration and shifts the policy toward goal-directed behavior near the target, while potential-based reward shaping supplies dense progress guidance. Experiments across multimodal, cross-scene, disaster-affected, and long-range settings show consistent gains over active geo-localization baselines.
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