Closed-Loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
Closed-Loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
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DOI：
10.23919/icif.2018.8455634
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Luke Burks;Ian Loefgren;Luke Barbier;Jeremy Muesing;Jamison McGinley;Sousheel Vunnam;N. Ahmed
中科院分区：
文献类型：
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作者：
Luke Burks;Ian Loefgren;Luke Barbier;Jeremy Muesing;Jamison McGinley;Sousheel Vunnam;N. Ahmed
在搜索应用中，自主无人驾驶车辆必须能够有效地重新捕获和定位移动的目标，这些目标可以在大空间中长时间保持在视野之外。因此，必须积极利用所有可用的信息源-包括人类提供的不精确但容易获得的语义观察。为了实现这一目标，这项工作开发并验证了一种新的协作式人机传感解决方案，用于动态目标搜索。我们的方法使用连续部分可观察马尔可夫决策过程（CPOMDP）规划生成车辆轨迹，最佳地利用车载传感器的不完美检测数据，以及可以从人类传感器专门请求的语义自然语言观察。关键的创新是一个可扩展的分层高斯混合模型制定有效地解决CPOMDPs与语义观察在连续的动态状态空间。该方法被证明和验证与一个真实的人-机器人团队从事动态室内目标搜索和捕获场景上的自定义测试平台。
In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged - including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.