CAREER: Motion Planning and Active Vision Strategies for Optimizing Visual Feedback in Robot Control

该项目研究了两种互补的方法来改进基于计算机视觉的机器人控制，特别是在不确定和变化的环境中。首先，通过开发考虑视觉数据属性的运动规划技术——利用可用的视觉反馈并克服传感限制。这需要扩展机器人构形空间的概念以包括图像特征空间，并使用拓扑保持学习方案来处理建模的不确定性。其次，通过开发主动视觉策略来优化机器人控制中的视觉反馈——系统地扩展机器人操作范围，超越静态摄像机的可能范围。定义摄像机运动下的不变性可以使主动摄像机控制与机器人控制解耦。将探讨相机运动与机器人运动计划配合的确定性和随机策略。这项研究将与辅助教育活动交织在一起，包括开发机器人和人机界面的新研究生课程，修订相关领域的本科课程，以及开发两种新的教育工具。第一个工具将用于网络上的协作小组项目，特别是涉及视频图像的项目。第二个工具将用于训练和可视化，使用增强现实界面，允许虚拟和真实对象的交互混合。

This project investigates two complementary ways of improving computer vision based robot control particularly in uncertain and changing environments. First, by developing motion planning techniques that account for properties of the visual data -- exploiting the available visual feedback and overcoming sensing limitations. This requires extending the notion of robot configuration space to include image feature space and using topology-preserving learning schemes to handle modeling uncertainties. Second, by developing active vision strategies for optimizing visual feedback in robot control -- systematically extending the range of robot operation beyond that possible by static cameras. Defining invariances under camera motion would allow the decoupling of active camera control from robot control. Both deterministic and stochastic strategies will be explored for exploiting the camera motion in cooperation with the robot motion plan. This research will be interleaved with supporting educational activities which include developing new graduate courses in robotics and human-computer interfaces, revising undergraduate courses in related areas, and developing two new educational tools. The first tool will be for collaborative group projects on the web especially for projects involving video images. The second tool will be for training and visualization using an augmented reality interface that allows interactive mixing of virtual and real objects.