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Techniques For Sensor-Integrated Robotic Systems: Raman Spectra Analysis, Image Guidance, And Kinematic Calibration

机译:传感器集成机器人系统技术:拉曼光谱分析,图像引导和运动校准

摘要

Robotics and sensor technology have made impressive advancements over the years. There are now robotic systems that help perform surgeries or explore the surface of Mars, and there are sensors that detect trace amounts of explosives or identify diseased human tissue. The most powerful systems integrate robots and sensors, which are natural complements to each other. Sensors can provide information that might otherwise be unavailable due to indirect robotic manipulation (e.g., images of the target environment), and robots can provide suitably precise positioning of an analytical sensor.To have an effective sensor-integrated robotic system, multiple capabilities are needed in the areas of sensors, robotics, and techniques for robot/sensor integration. However, for many types of applications, there are shortcomings in the current technologies employed to provide these capabilities. For the analysis of complex sensor signals, there is a need for improved algorithms and open platforms that enable techniques to be shared. For the path planning and tracking of integrated sensors and the visualization of collected information, image guidance systems that support advanced analytical sensors would be very beneficial. For robotic placement of a sensor, easily usable calibration procedures and methods to overcome limited feedback could help improve the accuracy.To help address these issues, some novel systems and techniques were developed in this research. First, a software system was created to process, analyze, and classify data from a specific kind of sensor (a Raman spectrometer). The system is open and extensible, and it contains novel techniques for processing and analyzing the sensor data. Second, an image guidance system was made for use with a sensor-integrated robotic system (a Raman probe attached to a surgical system). The system supports tool tracking, sensor activation, real-time sensor data analysis, and presentation of the results in a 3D computer visualization of the environment. Third, a kinematic calibration technique was developed for serial manipulators. It requires no external measurement devices for calibration, provides solutions for some limitations of existing techniques, and can significantly enhance the positional accuracy of a robot to improve sensor placement.The implemented techniques and systems were successfully evaluated using various data sets and conditions. Together, the contributions of this work provide important building blocks for an accurate robot with an integrated analytical sensor. This type of a system would be a powerful tool for many future applications, such as a surgical robot that automatically scans for diseased tissue and assists the surgeon in the necessary treatment. Ultimately, this work is intended to foster the development of advanced sensor-integrated robotic systems.
机译:多年来,机器人技术和传感器技术取得了令人瞩目的进步。现在,有机器人系统可以帮助进行手术或探查火星表面,还有可以检测痕量炸药或识别出患病人体组织的传感器。最强大的系统集成了机器人和传感器,它们是彼此的自然补充。传感器可以提供由于间接机器人操纵而可能无法获得的信息(例如目标环境的图像),并且机器人可以提供分析传感器的适当精确定位。要拥有有效的传感器集成机器人系统,需要多种功能在传感器,机器人技术和机器人/传感器集成技术领域。但是,对于许多类型的应用程序,用于提供这些功能的当前技术存在缺陷。为了分析复杂的传感器信号,需要能够使技术共享的改进算法和开放平台。对于集成传感器的路径规划和跟踪以及所收集信息的可视化,支持高级分析传感器的图像导航系统将非常有益。对于机器人的自动放置,克服有限反馈的易于使用的校准程序和方法可以帮助提高准确度。为了解决这些问题,本研究开发了一些新颖的系统和技术。首先,创建了一个软件系统来处理,分析和分类来自特定类型传感器(拉曼光谱仪)的数据。该系统是开放和可扩展的,并且包含用于处理和分析传感器数据的新颖技术。其次,制作了一个图像引导系统,用于与传感器集成的机器人系统(连接到外科系统的拉曼探针)。该系统支持工具跟踪,传感器激活,实时传感器数据分析以及在环境的3D计算机可视化中显示结果。第三,开发了用于串行机械手的运动学校准技术。它不需要外部测量设备即可进行校准,为现有技术的某些局限性提供解决方案,并且可以显着提高机器人的位置精度以改善传感器的位置。使用各种数据集和条件成功评估了实施的技术和系统。总之,这项工作的贡献为具有集成分析传感器的精确机器人提供了重要的构建要素。这种类型的系统将是许多未来应用的强大工具,例如自动扫描患病组织并协助外科医生进行必要治疗的手术机器人。最终,这项工作旨在促进高级传感器集成机器人系统的开发。

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    Reisner Luke Anthony;

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  • 年度 2012
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