安全约束下持久监视的无记忆控制设计
Memoryless Control Design for Persistent Surveillance under Safety Constraints
AI总结：
针对有限二维网格中移动机器人的持久监视问题，提出一种基于熵最大化原理的有限参数凸规划方法，设计时间不变无记忆控制策略，在避免进入禁止区域的同时最大化被持久监视的状态数。
摘要：
本文研究在存在禁止区域的有限二维网格中移动的机器人的时间不变无记忆控制策略设计，这些机器人被任务为持久监视该区域。我们将每个机器人建模为一个受控马尔可夫链，其状态包括在网格中的位置和运动方向。目标是找到最少数量的机器人和相关的时间不变无记忆控制策略，保证在不访问禁止状态的情况下持久监视最大数量的状态。我们提出了一种基于熵最大化原理的有限参数凸规划设计方法。提供了数值示例。
英文摘要：
This paper deals with the design of time-invariant memoryless control policies for robots that move in a finite two- dimensional lattice and are tasked with persistent surveillance of an area in which there are forbidden regions. We model each robot as a controlled Markov chain whose state comprises its position in the lattice and the direction of motion. The goal is to find the minimum number of robots and an associated time-invariant memoryless control policy that guarantees that the largest number of states are persistently surveilled without ever visiting a forbidden state. We propose a design method that relies on a finitely parametrized convex program inspired by entropy maximization principles. Numerical examples are provided.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
