论文标题
人群中移动机器人的路径计划模型
Path planning model of mobile robots in the context of crowds
论文作者
论文摘要
本文分析了基于RNN和视觉质量评估的机器人路径计划模型。移动机器人路径计划是机器人导航的关键，也是机器人研究中的重要领域。让机器人的运动空间为二维平面，当将人造电位方法用于路径规划时，机器人的运动被视为虚拟人造电势场力下的一种运动。与简单的图像获取相比，复杂的人群环境中的图像采集需要首先进行图像进行预处理。我们主要使用OPENCV校准工具来预处理获取的图像。在Hemathodology设计中，进行了基于RNN的视觉质量评估对过滤背景噪声。校准后，图像中仍存在高斯噪声和其他一些影响后续操作的冗余信息。基于RNN，开发了一种新的图像质量评估算法，并在此基础上执行DeNoising。此外，设计和模拟了新颖的路径计划模型。与最先进的模型相比，经验表明该模型的鲁棒性。
Robot path planning model based on RNN and visual quality evaluation in the context of crowds is analyzed in this paper. Mobile robot path planning is the key to robot navigation and an important field in robot research. Let the motion space of the robot be a two-dimensional plane, and the motion of the robot is regarded as a kind of motion under the virtual artificial potential field force when the artificial potential field method is used for the path planning. Compared to simple image acquisition, image acquisition in a complex crowd environment requires image pre-processing first. We mainly use OpenCV calibration tools to pre-process the acquired images. In themethodology design, the RNN-based visual quality evaluation to filter background noise is conducted. After calibration, Gaussian noise and some other redundant information affecting the subsequent operations still exist in the image. Based on RNN, a new image quality evaluation algorithm is developed, and denoising is performed on this basis. Furthermore, the novel path planning model is designed and simulated. The expeirment compared with the state-of-the-art models have shown the robustness of the model.
