论文标题
基于摄像机的自适应轨迹指导通过神经网络
Camera-Based Adaptive Trajectory Guidance via Neural Networks
论文作者
论文摘要
在本文中，我们介绍了一种新颖的方法，用于捕获使用流图像数据在动态设置中导航室内机器人的视觉轨迹。首先，提出了图像处理管道，以准确地从嘈杂的背景划分轨迹。接下来，捕获的轨迹用于设计，训练和比较两个神经网络体系结构，以预测在机器人实时在连续空间上进行连续空间的线路的加速和转向命令。最后，实验结果表明，机器人的神经网络与人类近距离的性能以及系统在具有遮挡和/或低光条件的环境中的可行性。
In this paper, we introduce a novel method to capture visual trajectories for navigating an indoor robot in dynamic settings using streaming image data. First, an image processing pipeline is proposed to accurately segment trajectories from noisy backgrounds. Next, the captured trajectories are used to design, train, and compare two neural network architectures for predicting acceleration and steering commands for a line following robot over a continuous space in real time. Lastly, experimental results demonstrate the performance of the neural networks versus human teleoperation of the robot and the viability of the system in environments with occlusions and/or low-light conditions.
