Distributed Perception by Collaborative Robots
Distributed Perception by Collaborative Robots
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DOI：
10.1109/lra.2018.2856261
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发表时间：
2018-07
影响因子：
5.2
通讯作者：
Ramyad Hadidi;Jiashen Cao;M. Woodward;M. Ryoo;Hyesoon Kim
中科院分区：
文献类型：
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作者：
Ramyad Hadidi;Jiashen Cao;M. Woodward;M. Ryoo;Hyesoon Kim
识别能力，更广泛地说，机器学习技术使机器人能够执行复杂的任务，并允许它们在不同的情况下发挥作用。事实上，机器人可以很容易地访问实时记录的大量传感器数据，如语音、图像和视频。由于此类数据具有时间敏感性，因此必须对其进行实时处理。此外，众所周知，机器学习技术是计算密集型和资源密集型的。因此，在计算能力和能源供应方面，单个资源受限的机器人往往无法单独处理如此繁重的实时计算。为了克服这一障碍，我们提出了一个框架来获取多个低功率机器人的聚合计算能力，以实现高效、动态和实时的识别。我们的方法适应计算设备在运行时的可用性，并适应网络的继承动态。我们的框架可以应用于任何分布式机器人系统。为了进行演示，我们用几个基于Raspberry-PI3的机器人(最多12个)，每个机器人都配备了一个摄像头，我们实现了一个最先进的视频动作识别模型和两个图像识别模型。我们的方法允许一组低功率机器人获得与高端嵌入式平台NVIDIA Tegra TX2类似的性能(就每秒处理的图像或视频帧数量而言)。
Recognition ability and, more broadly, machine learning techniques enable robots to perform complex tasks and allow them to function in diverse situations. In fact, robots can easily access an abundance of sensor data that are recorded in real time such as speech, image, and video. Since such data are time sensitive, processing them in real time is a necessity. Moreover, machine learning techniques are known to be computationally intensive and resource hungry. As a result, an individual resource-constrained robot, in terms of computation power and energy supply, is often unable to handle such heavy real-time computations alone. To overcome this obstacle, we propose a framework to harvest the aggregated computational power of several low-power robots for enabling efficient, dynamic, and real-time recognition. Our method adapts to the availability of computing devices at runtime and adjusts to the inherit dynamics of the network. Our framework can be applied to any distributed robot system. To demonstrate, with several Raspberry-Pi3-based robots (up to 12) each equipped with a camera, we implement a state-of-the-art action recognition model for videos and two recognition models for images. Our approach allows a group of multiple low-power robots to obtain a similar performance (in terms of the number of images or video frames processed per second) compared to a high-end embedded platform, Nvidia Tegra TX2.