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RFID-Based Mobile Robot Trajectory Tracking and Point Stabilization Through On-line Neighboring Optimal Control

机译:基于RFID的移动机器人轨迹跟踪与在线邻居最优控制的点稳定

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摘要

In this manuscript, we propose an on-line trajectory-tracking algorithm for nonholonomic Differential-Drive Mobile Robots (DDMRs) in the presence of possibly large parametric and measurement uncertainties. Most mobile robot tracking techniques that depend on reference RF beacons rely on approximating line-of-sight (LOS) distances between these beacons and the robot. The approximation of LOS is mostly performed using Received Signal Strength (RSS) measurements of signals propagating between the robot and RF beacons. However, accurate mapping between RSS measurements and LOS distance remains a significant challenge and is almost impossible to achieve in an indoor reverberant environment. This paper contributes to the development of a neighboring optimal control strategy where the two major control tasks, trajectory tracking and point stabilization, are solved and treated as a unified manner using RSS measurements emitted from Radio Frequency IDentification (RFID) tags. The proposed control scheme is divided into two cascaded phases. The first phase provides the robot's nominal control inputs (speeds) and its trajectory using full-state feedback. In the second phase, we design the neighboring optimal controller, where RSS measurements are used to better estimate the robot's pose by employing an optimal filter. Simulation and experimental results are presented to demonstrate the performance of the proposed optimal feedback controller for solving the stabilization and trajectory tracking problems using a DDMR.
机译:在此手稿中,我们针对可能存在较大参数和测量不确定性的非完整差分驱动移动机器人(DDMR)提出了一种在线轨迹跟踪算法。大多数依赖于参考RF信标的移动机器人跟踪技术都依赖于这些信标与机器人之间的近似视线(LOS)距离。 LOS的近似主要是使用在机器人和RF信标之间传播的信号的接收信号强度(RSS)测量来执行的。但是,RSS测量值与LOS距离之间的精确映射仍然是一项重大挑战,并且几乎不可能在室内混响环境中实现。本文为开发一种相邻的最优控制策略做出了贡献,该策略使用射频识别(RFID)标签发出的RSS测量来解决和处理两个主要控制任务,即轨迹跟踪和点稳定化,并将其视为统一的方式。所提出的控制方案分为两个级联阶段。第一阶段使用全状态反馈提供机器人的标称控制输入(速度)及其轨迹。在第二阶段中,我们设计了邻近的最佳控制器,在该控制器中,RSS测量用于通过采用最佳滤波器来更好地估计机器人的姿势。给出了仿真和实验结果,以证明所提出的最佳反馈控制器用于使用DDMR解决稳定性和轨迹跟踪问题的性能。

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