用于远征医疗设施中自主患者运输的分层风险映射
Layered Risk Mapping for Autonomous Patient Transport in Expeditionary Medical Facilities
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- Institute for Experiential Robotics, Northeastern University（体验式机器人研究所，东北大学）
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中文总结 AI 辅助
针对远征医疗设施中自主患者运输面临的复杂导航挑战，提出分层风险映射框架，融合多种环境危害，经配对蒙特卡洛评估及实际验证，有效降低碰撞率、提高障碍物清除率，满足相关运营模式规划要求。
中文摘要 AI 辅助
在远征医疗设施中，常规患者运输带来了个人防护装备消耗、人员转移和感染风险增加等多重负担，在高峰情况下难以为继。虽然自动轮椅可分担此运营负荷，但在高度非结构化和动态环境中患者运输的安全关键特性带来了复杂导航挑战。为此，我们提出了一个分层风险映射框架，通过噪声或融合模型将四种异构环境危害（地形坡度、静态和动态障碍物以及语义可通行性）融合到统一的概率成本表面。在配对蒙特卡洛评估中，风险知情融合将碰撞率从超过73%降至32%以下，相对于无风险基线，障碍物清除率增加了一倍多。此外，在所有测试的危害密度下，噪声或实现了最高的障碍物清除率和最低条件峰值风险。我们还在商业电动轮椅上针对室内和室外部署的三个代表性任务配置文件验证了该框架，表明此架构成功满足了这一以前未解决的运营模式的规划要求。
英文摘要
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.
