改訂可能なターゲット記憶による継続的空中探索エージェント
SkyAgent: Revisable Target Memory for Continuous Aerial Search
空中探索で誤った候補が残り続ける問題(FCP)を解決するため、証拠に基づき候補を暫定・確定・棄却の状態で更新するRevisable Target MemoryをSkyAgentに導入し、探索成功率と信頼性を向上させた。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
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著者: Kaibo Yang, Xingyu Wu, Enze Zhu, Yajie Chai, Xuyang Li, Jiahui Cui, Xiaoxuan Liu, Lei Wang, Zhan Chen
分類: cs.RO
原文アブストラクト
Continuous aerial search builds target hypotheses from sparse, viewpoint-dependent observations. When later evidence contradicts a stored hypothesis, however, the candidate can remain available to the planner and continue triggering approach or verification actions. We call this trajectory-level failure false candidate persistence (FCP). We equip SkyAgent with Revisable Target Memory (RTM), which associates evidence with persistent candidates, discounts correlated views, and revises each candidate through Tentative, Confirmed, and Rejected states. The key interface binds every generated waypoint to its source candidate: rejection therefore invalidates stale goals, while conservative reactivation protects temporarily occluded targets. Across four splits from the CityAVOS, UAV-ON, and OpenUAV benchmarks, our SkyAgent equipped with RTM outperforms all competitors, improving SR by 2.90--6.31 points while substantially reducing Behavior-FCP and post-contradiction travel with a low false-rejection rate. Across 35 matched real-world DJI scenarios per method, RTM consistently improves task success and behavioral reliability, further validating its state-to-action correction under physical deployment conditions.