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IMPERFECT SENSING BASED ANALYSIS OF AGENTS DEPLOYED IN ENVIRONMENTS FOR TRAVERSAL TASK EXECUTION

机译:基于不完全感知的对部署在遍历任务执行环境中的代理的分析

摘要

A fully tested autonomous system works predictably under ideal or assumed environment. However, behavior of the system is not fully defined when component(s) malfunction or fail (e.g., sensor failures) and the like which leads to inefficient task execution. Present disclosure provides system and method for imperfect sensing-based analysis of agents deployed in environments for traversal task execution, specifically, in unknown environments. The system estimates performance metric (e.g., task execution time) and safety metrics (e.g., number of collisions encountered while executing the task) related to task of traversal of the vehicle/agent from its current position to a target location. The system also incorporates sensitivity of each sensor(s), for given task, when they malfunction. The sensing malfunction can be both independent and/or co-related with malfunction of other sensors. Such a model helps to identify the most critical component in the agent(s), thereby increasing reliability of the system and meet safety standards.
机译:经过充分测试的自治系统在理想或假设的环境下可预测地工作。然而,当组件故障或失效(例如,传感器失效)等导致任务执行效率低下时,系统的行为没有完全定义。本发明提供了一种系统和方法,用于对部署在环境中的代理进行基于不完全感知的分析,以执行遍历任务,特别是在未知环境中。系统估计与车辆/代理从其当前位置到目标位置的遍历任务相关的性能指标(例如,任务执行时间)和安全指标(例如,执行任务时遇到的碰撞次数)。对于给定任务,当每个传感器发生故障时,该系统还包括每个传感器的灵敏度。传感故障可以是独立的,也可以与其他传感器的故障共同相关。这种模型有助于识别代理中最关键的组件,从而提高系统的可靠性并满足安全标准。

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