Research on control of mobile robot cooperating with environment embedded sensors

本研究旨在实现智能空间机器人与移动的机器人的协作。在这里，我们把由分布式传感器和执行器组成的空间称为智能空间。智能空间的设计与实现，构建具有高度灵活性和可扩展性的系统我们通过RT中间件将分布式设备组件化，构建了面向组件的智能空间。讨论了智能空间的观测功能和移动的机器人导航功能的设计准则，成功构建了一个易于添加、更改或删除传感器和配置功能的系统.利用智能空间信息和机器人机载传感器的移动的机器人导航为实现智能空间中移动的机器人的辅助，研究了生活环境中移动的机器人的导航方法。首先提出了一种通过观察智能空间中人的行走路径来获取环境主路径的方法。其次，我们证明了移动的机器人可以通过整合主路径和自身位置的信息以及周围障碍物的信息来执行高效和自然的运动.智能空间和移动的机器人观测信息的集成我们提出不仅使用分布式传感器，而且使用机器人机载传感器。我们还提出了方法，有效地整合这两种信息导航移动的机器人。首先，我们表明，跟踪运动物体和环境地图的建设可以通过使用综合信息精确地完成。然而，由于测量值之间的相关性，存在计算和连接负荷增加的问题。为了解决这个问题，我们提出了融合方法。提出了一种利用粒子滤波器获取机器人模型的方法，并将其集成到系统中，以实现鲁棒性的提高和适用范围的扩大。

In this research, we aimed to accomplish the cooperation of intelligent space and mobile robots. Here we call the space composed of distributed sensors and actuators, intelligent space.1. Design and implementation of intelligent space to construct a system with high flexibility and extensibilityWe constructed component-oriented intelligent space by making distributed devices into component using RT-middleware. We discussed about design guidelines of observation function and mobile robot navigation function of the intelligent space and successed to build a system easy to add, change or delete the sensor and disposition functions.2. Navigation of mobile robot using information from intelligent space and robot onboard sensorsTo realize assistance of mobile robot in intelligent space, we investigated mobile robot navigation method in living environment. At first, we proposed a method to obtain main pathway of the environment by observing human walking path in intelligent space. Next, we showed that mobile robot can perform efficient and natural movement by integrating information of main pathway and own position with surrounding obstacle information.3. Integration of informations observed by intelligent space and mobile robotWe proposed to use not only distributed sensors but robot onboard sensors. We also proposed method to integrate both informations efficiently to navigate mobile robot. First, we showed that tracking moving objects and construction of environment map can be done precisely by using integrated information. However, there was a problem that the computation and connection load increases because of correlation between measurement values. To solve this problem, we proposed fusion methods. Moreover, we proposed a method to obtain robot model using particle filter and integrating it into the system to accomplish improvement of robustness and expansion of applicable scope.