Opportunistic robot control for interactive multiobjective optimization under human performance limitations
Opportunistic robot control for interactive multiobjective optimization under human performance limitations
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DOI：
10.1016/j.automatica.2020.109263
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Pio Ong;J. Cortés
中科院分区：
文献类型：
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作者：
Pio Ong;J. Cortés
本文提出了机会状态触发策略，用于解决涉及人机交互的凸多目标优化问题。机器人知道定义问题的多个目标函数，但需要人工输入来找到最理想的帕累托解。为了避免超载的人与查询，我们认为她作为一个有限的资源，机器人，并设计事件触发控制器，机会主义地规定他们之间的信息交换。我们考虑各种模型的人的表现，从一个理想的查询即时响应，后来考虑的响应时间和交互频率的约束。对于每个模型，我们正式建立所需的优化器的渐近收敛，并排除芝诺行为的存在。
This paper proposes opportunistic state-triggered strategies for solving convex multiobjective optimization problems that involve human–robot interaction. The robot is aware of the multiple objective functions defining the problem, but requires human input to find the most desirable Pareto solution. In order to avoid overloading the human with queries, we view her as a limited resource to the robot, and design event-triggered controllers that opportunistically prescribe the information exchanges among them. We consider various models of human performance, starting with an ideal one where queries are responded instantaneously, and later considering constraints on the response time and the interaction frequency. For each model, we formally establish the asymptotic convergence to the desired optimizer and rule out the existence of Zeno behavior.