开发和评估用于真实世界环境中机器人导航和物体识别的计算机视觉系统

Malene Helgo
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引用次数: 0

摘要

文章讨论了计算视觉框架,其中包括图像识别、分类、优先级排序和导航控制模块。在这一框架中,用户模型用于向机器人控制器提供信息,而机器人控制器在动态虚拟环境中的性能会得到提高。相比之下,视觉模块使用的是多级感知神经网络,能够进行高效的图像分割、物体识别和颜色分割,并使用控制模块 "基于位置的视觉服务"(PBVS)和诸如 "避免碰撞"()、"前进"()和 "跟随"()等操作。它控制机器人的运动,因此该系统成功地进行了测试,并满足了 Antimedia Robotics Pioneer I 机器人的要求。此外,它还与现实生活一致。结果表明,该系统在提供有效引导和避开障碍物方面非常有效。此外,该研究还探讨了使用人工神经网络进行图像识别和分类的问题。此外,它还要求使用 SpCoMapping 为有用信息添加语言地图。总之,研究强调了计算机视觉和神经网络在改善机器人通信和语言学习方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and Evaluation of a Computer Vision System for Robot Navigation and Object Recognition in Real-World Environments
The article discusses the vision framework for computing that includes image recognition, classification, prioritization, and navigation control modules. In this framework, a user model is used to feed the robotic controllers, whose performance improves in dynamic virtual contexts. In contrast, the vision module uses a multi-level perceptual neural network capable of efficient image segmentation, object recognition, and color segmentation, using the control module Position-Based Vision Serving (PBVS) and actions such as Avoid Collision (), Go-Ahead (), and Follow( ). It controls the motion of the robot, so the system successfully tested and met the requirements of the Antimedia Robotics Pioneer I robot. In addition, it was consistent with real life. The results show the effectiveness of the system in providing effective guidance and avoiding obstacles. Furthermore, the study investigates the use of artificial neural networks for image recognition and classification. In addition, it requires the use of SpCoMapping to add language maps to useful information. In summary, studies have emphasized the potential of computer vision and neural networks to improve robotic communication and language learning.
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