Integrity Risk-Based Model Predictive Control for Mobile Robots
Integrity Risk-Based Model Predictive Control for Mobile Robots
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DOI：
10.1109/icra.2019.8793521
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
O. A. Hafez;Guillermo Duenas Arana;M. Spenko
中科院分区：
文献类型：
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作者：
O. A. Hafez;Guillermo Duenas Arana;M. Spenko
提出了一种以导航完整性风险为约束的模型预测控制器（MPC）。导航完整性风险解释了定位传感器和算法中存在的故障，随着在生命和关键任务情况下运行的机器人数量预计在不久的将来会急剧增加（例如，自动驾驶汽车的潜在涌入），这是一个越来越重要的考虑因素。具体来说，这项工作使用了一种局部最近邻完整性风险评估方法，该方法将数据关联错误作为约束，以保证在后退的视界上的本地化安全。此外，状态和控制输入约束也在本工作中被强制执行。提出的MPC设计使用真实世界的地图环境进行了测试，表明机器人能够在城市环境中运行时保持预定义的最低本地化安全水平。
This paper presents a Model Predictive Controller (MPC) that uses navigation integrity risk as a constraint. Navigation integrity risk accounts for the presence of faults in localization sensors and algorithms, an increasingly important consideration as the number of robots operating in life and mission-critical situations is expected to increase dramatically in near future (e.g. a potential influx of self-driving cars). Specifically, the work uses a local nearest neighbor integrity risk evaluation methodology that accounts for data association faults as a constraint in order to guarantee localization safety over a receding horizon. Moreover, state and control-input constraints have also been enforced in this work. The proposed MPC design is tested using real-world mapped environments, showing that a robot is capable of maintaining a predefined minimum level of localization safety while operating in an urban environment.