为野外医疗设施的无人转运设计分层风险地图，提升安全性和通行效率。
Layered Risk Mapping for Autonomous Patient Transport in Expeditionary Medical Facilities
- 融合地形、障碍物和语义可通行性等四类风险，构建概率代价图
- 碰撞率从73%降至32%以下，障碍物通过率翻倍
- 适用于复杂动态环境，适合医疗机器人导航场景
在野外医疗设施中，常规患者转运会加剧个人防护装备消耗、人员分流和感染风险，在突发高峰情况下难以为继。尽管自主轮椅可缓解这一负担，但高度非结构化且动态变化环境中的患者运输具有极高的安全要求，带来复杂的导航挑战。为此，我们提出一种分层风险映射框架，通过噪声或（Noisy-OR）融合模型，将四类异构环境危害（地形坡度、静态与动态障碍物、语义可通行性）整合为统一的概率代价表面。在配对蒙特卡洛评估中，基于风险的融合使碰撞率从超过73%降至32%以下，障碍物通过率提升一倍以上，优于无风险感知基线。此外，在所有测试的危害密度下，Noisy-OR实现最高的障碍物通过率和最低的条件峰值风险。我们在商用电动轮椅上对三种典型任务场景进行了验证，涵盖室内外部署，结果表明该架构成功满足了此前未被解决的操作环境下的规划需求。
原文摘要 · Abstract (English)
In expeditionary medical facilities, routine patient transport imposes a compounding burden of personal protective equipment consumption, staff diversion, and elevated infection risk that becomes unsustainable under surge conditions. While autonomous wheelchairs could absorb this operational load, the safety-critical nature of patient transit within these highly unstructured and dynamic environments poses complex navigational challenges. To address this, we present a layered risk mapping framework that fuses four heterogeneous environmental hazards (terrain slope, static and dynamic obstacles, and semantic traversability) into a unified probabilistic cost surface via a Noisy-OR fusion model. In a paired Monte-Carlo evaluation, risk-informed fusion reduces collision rates from over 73% to under 32% and more than doubles obstacle clearance relative to a risk-unaware baseline. Additionaly, Noisy-OR achieves the highest clearance to obstacles and the lowest conditional peak risk across all tested hazard densities. We further validate the framework on a commercial powered wheelchair across three representative mission profiles in indoor and outdoor deployments, demonstrating that this architecture successfully meets the planning requirements of this previously unaddressed operational regime.
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