ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)||7|
|Section||Session 1: Robotics|
|Published online||05 September 2017|
A Role Based Framework for Robot Sensor Management
National Lab for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China
Sensors play an important role in the execution of robot tasks as an important component of understanding the world. In this paper, we propose a role-based sensor management framework on ROS (Robot Operating System), in which the role is a set of information needed for robot sensor development. Firstly, we develop a sensor management interface to facilitate the development of sensor applications on robot. And a subscriber module is proposed to shield the details of the sensor driver and the communication between processes, the sensor drivers on the robot are effectively managed. Robot sensors have different executive ability in different situations when robot performing tasks. For example, we know that RGB camera can collect a clear image when the light is strong, while generate a blurred image in weak light environment. Secondly, we propose an environment based sensor role dynamic evaluation mechanism to get the confidence of sensor in a certain environment. The confidence represents the executive ability of sensor when performing tasks. The confidence provides the basis for switching the task schemes. In the mechanism we can dynamically configure the environment information and confidence calculation method. In the experiment phase, we verify the effectiveness of the framework through a robot patrol task. Experiment results show that our framework can effectively manage the robot sensors with a certain environmental adaptability.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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