Abstract Robotic obstacle avoidance is an important issue in robotic navigation for unknown or partially known, dynamic environments. A good number of techniques have already been proposed to navigate obstacles in this kind of environment. They include a series of velocity space methods that have been successful implemented in several applications. They formulate the problem as one of constrained optimization in the velocity space of the robot. The constraints include the obstacles in the environment assuming they are static. In this paper, we present an efficient, real-time method (BCM-DO) to include the restrictions imposed by dynamic objects. The optimization function has also been adapted to include these new restrictions. The new function is evaluated in two sets of points. A first set is obtained from a coarse sampling in the reachable window of velocities and a second set is selected in the limits of each curvature interval to avoid missing small openings between static objects. The whole system has first been extensively tested in several simulated robots and finally applied to a hotel assistant robot (BellBot) resulting in an efficient, real-time obstacle avoidance method that produces smooth and reliable routes.

中文翻译：

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基于速度空间优化的动态环境避障

摘要 机器人避障是机器人导航未知或部分已知的动态环境中的一个重要问题。已经提出了许多技术来在这种环境中导航障碍物。它们包括一系列已在多个应用中成功实施的速度空间方法。他们将该问题表述为机器人速度空间中的一种约束优化问题。约束条件包括环境中的障碍物，假设它们是静态的。在本文中，我们提出了一种有效的实时方法 (BCM-DO)，以包含动态对象施加的限制。优化功能也经过调整以包含这些新限制。新函数在两组点中进行评估。第一组是从可到达速度窗口中的粗采样中获得的，第二组是在每个曲率区间的范围内选择的，以避免遗漏静态对象之间的小开口。整个系统首先在多个模拟机器人上进行了广泛测试，最后应用于酒店助理机器人 (BellBot)，从而形成了一种高效、实时的避障方法，可生成平稳可靠的路线。