A methodology to quantify risk of failure for dynamic robots
A methodology to quantify risk of failure for dynamic robots
复制标题
量化动态机器人故障风险的方法
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Albert Wang
中科院分区：
文献类型：
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作者：
Albert Wang
人类拥有一种与生俱来的危险感，这种危险感指导着非凡的动态机动的执行。他们也可以利用这种感觉来产生创造性的恢复策略，最终达到安全停止。机器人还没有这种能力，根本上是因为没有明确的指标来表示量化的失败风险。拥有这样的度量标准将允许机器人探索其动态能力，直到其物理限制。本文试图通过引入一种方法来量化动态机器人的故障风险来解决这个问题。它采用了一个基于采样的网络，该网络使用可行性理论的原则构建，其重点是避免失败，而不是对特定运动的调节。简化，具体针对复杂的混合动力系统进行了探讨，以扩展使用的可行性理论的实际应用腿式机器人。这种方法的结果是可行状态网络，一个网络显示的非失败和失败的状态，和风险地图，量化的失败风险。这些概念证明了平面跳跃机器人模型。论文导师：Sangyuan Kim职称：副教授
Humans possess an innate sense of danger that guides the execution of extraordinary dynamic maneuvers. They can also use this sense to generate creative recovery strategies to eventually come to a safe stop. This capability is not yet available to robots, fundamentally because there is no clear metric that represents the quantified risk of failing. Possessing such a metric would allow robots to explore their dynamic capability up to their physical limitations. This thesis attempts to address this problem by introducing a methodology to quantify the risk of failure for dynamic robots. It employs a sampling-based network constructed using the principles of viability theory, which focuses on the avoidance of failure instead of the regulation to specific movements. Simplifications that specifically target complex hybrid systems are explored to extend the usage of viability theory for practical application to legged robots. The results of this methodology are the Viable State Network, a network showing the non-failing and failing states, and the Risk Map, the quantified risk of failure. These concepts are demonstrated for a planar hopping robot model. Thesis Supervisor: Sangbae Kim Title: Associate Professor