An Autonomous Water Vapor Plume Tracking Robot Using Passive Resistive Polymer Sensors

A simple reactive robot is described which is capable of tracking a water vapor plume to its source. The robot acts completely within the plume and is endowed with no deliberate information about wind direction or speed, yet accurately tracks the plume upstream. The robot's behavior, results from the behavior of simple resistive polymer sensors and their strategic placement on the robot's body.     

一种移动机器人环境模型——灰色定性地图

针对移动机器人的环境建模问题,提出一种综合拓扑地图和儿何地图特点的混合环境模型——灰色定性地图.用凸剖分算法将环境中的自由空间分解为一组凸多边形.灰色定性地图的定性层由凸多边形及其之间的邻接关系构成,用于模拟人类在路径规划时的高层定性推理.定量层由凸多边形顶点的坐标和势场向量构成,用于决定机器人在连续空间中的运动方向和速度.理论分析和实验均表明:灰色定性地图可以模拟人类对环境认知的知识表达,并且可以仪由凸多边形邻接信息和顶点信息支持机器人完成路径规划且确保路径的平滑性,有效地降低了环境模型的空间复杂度.     

Graphical simulation for sensor based robot programming

The programming of robots is slowly evolving from traditional teach pendant methods to graphical Off-Line Programming (OLP) methods. Graphical simulation tools, such as OLP, are very useful for developing and testing robot programs before they are run on real industrial equipment. OLP systems are also used to develop task level programs. Traditional OLP systems, however, suffer from the limitations of using only position control which does not account for inherent robot inaccuracies and dynamic environments. This paper describes our work on improving and supplementing traditional position control programming methods. A baseline OLP system was implemented at NIST's Automated Manufacturing Research Facility (AMRF). Experience gained in implementing this system showed that an effective OLP system must accurately simulate the real world and must support sensor programming to compensate for real-world changes that cannot be simulated. The developed OLP geometric world model is calibrated using robot mounted ultrasound ranging sensors. This measurement capability produces a baseline geometric model of relatively good static accuracy for off-line programming. The graphical environment must also provide representations of sensor features. For this specific application, force is simulated in order to include force based commands in our robot programs. These sensor based programs are able to run reliably and safely in an unpredictable industrial environment. The last portion of this paper extends OLP and describes the functionality of a complete system for programming complex robot tasks.