Mobile robots are ubiquitous. Such vehicles benefit from well-designed and calibrated control algorithms ensuring their task execution under precise uncertainty bounds. Yet, in tasks involving humans in the loop, such as elderly or mobility impaired, the problem takes a new dimension. In such cases, the system needs not only to compensate for uncertainty and volatility in its operation but at the same time to anticipate and offer responses that go beyond robust. Such robots operate in cluttered, complex environments, akin to human residences, and need to face during their operation sensor and, even, actuator faults, and still operate. This is where our thesis comes into the foreground. We propose a new control design framework based on the principles of antifragility. Such a design is meant to offer a high uncertainty anticipation given previous exposure to failures and faults, and exploit this anticipation capacity to provide performance beyond robust. In the current instantiation of antifragile control applied to mobile robot trajectory tracking, we provide controller design steps, the analysis of performance under parametrizable uncertainty and faults, as well as an extended comparative evaluation against state-of-the-art controllers. We believe in the potential antifragile control has in achieving closed-loop performance in the face of uncertainty and volatility by using its exposures to uncertainty to increase its capacity to anticipate and compensate for such events.
翻译:移动机器人无处不在。 这些飞行器受益于精心设计和校准的控制算法,以确保在精确的不确定性范围内执行任务。 然而,在涉及循环中的人的任务中,例如老年人或行动能力受损等,问题具有新的层面。在这种情况下,系统不仅需要补偿其运行的不确定性和波动性,而且同时需要预测和提供超出强力的应对能力。这些机器人在混乱、复杂的环境中运作,与人类住所相近,在操作过程中需要面对感应器,甚至操作器缺陷,并且仍在运行中。这是我们的观点进入前台的地方。我们基于抗易变原则提出了新的控制设计框架。这种设计旨在提供高度的不确定性预测,因为以前曾遭受过失败和失误,而同时利用这种预测能力提供超强的性能。在目前用于移动机器人轨迹跟踪的抗亚弱力控制中,我们提供了控制器设计步骤,在可辨定的不确定性和缺陷下分析性能,以及扩大的对比性能设计框架框架框架框架框架框架。我们利用不确定性的比较性评估,以便实现稳定风险风险的预测。