Visibility-Based Motion Planning

在许多环境中，出现了为具有基于可见性的感知能力的真实和虚拟机器人系统生成运动策略的问题。最后一个术语指的是传感器(例如，摄像机或距离传感器)沿着穿过自由空间的一条或几条视线射线检测物体的能力。除了以往规划者处理的经典避碰约束外，还需要一个新的概念框架和一系列具体的新算法来执行可见性约束下的运动规划。这些算法的输出将是将运动命令与基于可见度的感知操作相结合的运动策略。虽然机器人学已经广泛地研究了在完全已知的环境中操作的机器人的运动规划问题，但主动视觉已经研究了局部摄像机的运动/控制问题以获取所需的数据，而可见性感知与运动规划的集成仍然相对较少。然而，机器人的各种重要任务都可以用可见性语言来表达。“计划一次旅行，以便看到所有感兴趣的区域”，“在看到目标的同时移动到想要的位置”，“尽管移动物体阻碍视线，仍保持移动的目标在视线内”只是这类任务的一些例子。由于基于可见度的感知允许机器人在部分未知的环境中操作，这些技术允许在线规划适用于动态环境。制造业和医药业都可以从这些方法的应用中受益，特别是在那些在严重遮挡和动态工作环境中发生的操作(例如，部件交付、组装、监测)中。

In many environments the problem arises of generating motion strategies for real and virtual robotic systems with visibility- based sensing capabilities. This last term refers to the ability of a sensor (e.g., a video camera or a range sensor) to detect objects along one or several line-of-sight rays through free space. Both a new conceptual framework and a collection of specific new algorithms are needed to perform motion planning under visibility constraints, in addition to the classical collision avoidance constraints dealt with by previous planners. The output of these algorithms will be motion strategies that integrate motion commands with visibility-based sensing operations. While robotics has extensively studied the motion planling problem for robots operating in fully known environments with little or no sensing ability, and active vision has studied the problem of local camera motion/control in order to acquire needed data, the integration of visibility sensing with motion planning has remained relatively unexplored. Yet a variety of important tasks for a robot can be expressed in the language of visibility. `Plan a tour so as to see all of the region of interest,' `move to the desired location while keeping sight of a target,' `keep a moving target into sight despite view- obstructing mobile objects' are just some examples of such tasks. As visibility-based sensing allows robots to operate in partially unknown environments, these techniques allow on-line planning applicable to dynamic environments. Both manufacturing and medicine can benefit from application of such methods, especially in those operations (e.g., part delivery, assembly, monitoring) that occur in heavily occluded and dynamic work environments.