无碰撞运动规划中机器人沿路径可操作性最大化

Sascha Kaden, Ulrike Thomas
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引用次数: 3

摘要

运动规划的主要任务是寻找具有大可操作性的合适运动,同时必须保证无碰撞操作。这一条件在人与机器人的协作中变得越来越重要,因为必须保证机器人能够随时避开人类或动态障碍物。为此,运动规划中的路径必须根据可操控性和与障碍物的距离进行优化。由于具有较大的可操纵性,机器人在任何时候都具有较大的运动自由度,因而具有规避的可能性。或者,可以使用笛卡尔阻抗控制将机器人推开。为了实现这一目标,我们开发了一种综合方法。首先,我们引入了一种快速探索随机树,该树通过状态代价对可操作性进行扩展和优化。其次,我们使用STOMP方法和高斯混合模型进行优化。通过这种双重方法,我们能够在狭窄的通道中找到路径,同时根据可操作性对路径进行优化。
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Maximizing Robot Manipulability along Paths in Collision-free Motion Planning
A major task in motion planning is to find suitable movements with large manipulability, while collision-free operation must be guaranteed. This condition is increasingly important in the collaboration between humans and robots, as the capability of avoidance to humans or dynamic obstacles must be ensured anytime. For this purpose, paths in motion planning have to be optimized with respect to manipulability and distance to obstacles. Because with a large manipulability the robot has at any time, the possibility of evading due to the greater freedom of movement. Alternatively, the robot can be pushed away by using a Cartesian impedance control. To achieve this, we have developed a combined approach. First, we introduce a Rapidly-exploring Random Tree, which is extended and optimized by state costs for manipulability. Secondly, we perform an optimization using the STOMP method and Gaussian Mixture Models. With this dual approach we are able to find paths in narrow passages and simultaneously optimize the path in terms of manipulability.
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