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ResQuad: Toward a semi-autonomous wilderness search and rescue unmanned aerial system

机译:Resquad:走向半自动荒野搜索和救援无人机系统

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In a search and rescue scenario, an expeditious response is critical since the radius of the search area increases by roughly 3 km per hour while the life expectancy of the lost individual decreases. To find the object of the search as quickly as possible, the incident commander creates an overlay probability map identifying high priority search areas and develops a search plan that allocates search assets to each area to maximize the probability of success. Conventional aerial searches have the ability to cover a large area quickly, but are costly and require highly trained personnel to operate. In this work we present results of a proof of concept Search and Rescue Unmanned Aerial System, ResQuad, that capitalizes on the advances in drone industry while allowing for reduction in cost and retaining the advantages of aerial perspectives. Given the probability map from the search commander and the system constraints, a near optimal path is determined by the Path Planner in our system ensuring the coverage of high probability areas. While the Unmanned Aerial Vehicle (UAV) follows the path, an on-board computer with a camera processes and collates the data using an anomaly detection algorithm, saving the results to its memory for the base to verify in real-time.
机译:在搜索和救援场景中,迅速的反应是至关重要的,因为搜索区域的半径增加每小时大约3公里,而失去的个人的预期寿命减少。为了尽快找到搜索的对象,该事件指挥官创建识别高优先级搜索区域的叠加概率图,并开发一个搜索计划,将搜索资产分配给每个区域以最大化成功的概率。传统的航拍搜索有能力快速覆盖大面积,但成本高,需要高度培训的人员进行操作。在这项工作中,我们展示了概念搜索和救援无人空中系统的证据的结果,这些内容资本资本化了无人机行业的进步,同时允许降低成本并保留空中观点的优势。鉴于来自搜索指挥官和系统约束的概率图,通过我们的系统中的路径规划确定了近最佳路径,确保了高概率区域的覆盖范围。虽然无人驾驶飞行器(UAV)跟踪路径时,具有相机进程的板载计算机,并使用异常检测算法对数据进行整理,将结果保存到其内存中的基础以实时验证。

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