论文标题
全球反应性障碍物避免机器人操纵器的知情圆场
Informed Circular Fields for Global Reactive Obstacle Avoidance of Robotic Manipulators
论文作者
论文摘要
在本文中，提出了一个在复杂动态环境中的机器人操纵器的全球反应运动计划框架。特别是，来自Becker等人的圆场预测（CFP）计划者。 （2021）扩展以确保避免机器人操纵器的整个结构的障碍。为此，开发了一个运动计划框架，该框架利用了有关任意配置的空间运动计划者的有希望回避指示的全球信息，从而改善了全球轨迹，同时反应避免动态障碍并降低所需的计算能力。与现有运动计划方法相比，在多个模拟中测试了最终的运动计划框架，并具有巨大的潜力。
In this paper a global reactive motion planning framework for robotic manipulators in complex dynamic environments is presented. In particular, the circular field predictions (CFP) planner from Becker et al. (2021) is extended to ensure obstacle avoidance of the whole structure of a robotic manipulator. Towards this end, a motion planning framework is developed that leverages global information about promising avoidance directions from arbitrary configuration space motion planners, resulting in improved global trajectories while reactively avoiding dynamic obstacles and decreasing the required computational power. The resulting motion planning framework is tested in multiple simulations with complex and dynamic obstacles and demonstrates great potential compared to existing motion planning approaches.
