A learning based approach to self modeling robots

2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) Pub Date : 2014-07-10 DOI:10.1109/ICCICCT.2014.6993060

Michael J. Mathew, Rachit Sapra, S. Majumder

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引用次数: 1

Abstract

Performance of a robotic system depends o n the mathematical model that is programmed within the software of the robot. Usually the mathematical model of a robot is calculated by the designer and is re-calibrated during the time of operation. Model of the robot is essential for its software to take intelligent actions. Equipping the robot with the capability to self model will help itself to re-correct the model and recover itself in case of minor damages during operation. This also avoids the need of re-calibration since the model gets refined with more number of iterations. This work tries to make a robotic system, where the robot attempts to model itself by making use of some basic prior information and deriving the rest of the required information by visually observing the result of its actions on the environment. The approach discussed in this paper is for robotic manipulators and is validated through an experiment using an Invenscience ARM 2.0 using a Point Grey stereo camera.

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基于学习的机器人自建模方法

机器人系统的性能取决于机器人软件中编程的数学模型。通常机器人的数学模型是由设计者计算出来的，并在运行过程中重新校准。机器人模型是机器人软件进行智能操作的基础。赋予机器人自我建模的能力，可以帮助机器人在操作过程中发生轻微损伤时重新修正模型并自我修复。这也避免了重新校准的需要，因为模型通过更多的迭代得到了改进。这项工作试图制造一个机器人系统，其中机器人试图通过利用一些基本的先验信息来建立自己的模型，并通过视觉观察其对环境的行为结果来获得所需的其余信息。本文讨论的方法适用于机械臂，并通过使用Invenscience ARM 2.0和Point Grey立体摄像机进行实验验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT)

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