A major task in motion planning is to find paths that have a high ability to react to external influences while ensuring a collision-free operation at any time. This flexibility is even more important in human-robot collaboration since unforeseen events can occur anytime. Such ability can be described as mobility, which is composed of two characteristics. First, the ability to manipulate, and second, the distance to joint limits. This mobility needs to be optimized while generating collision-free motions so that there is always the flexibility of the robot to evade dynamic obstacles in the future execution of generated paths. For this purpose, we present a Rapidly-exploring Random Tree (RRT), which applies additional costs and sampling methods to increase mobility. Additionally, we present two methods for the optimization of a generated path. Our first approach utilizes the built-in capabilities of the RRT*. The second method optimize the path with the stochastic trajectory optimization for motion planning (STOMP) approach with Gaussian Mixture Models. Moreover, we evaluate the algorithms in complex simulation and real environments and demonstrate an enhancement of mobility.

运动规划中的一项主要任务是找到对外部影响具有高反应能力的路径，同时确保随时无碰撞运行。这种灵活性在人机协作中尤为重要，因为任何时候都可能发生无法预料的事件。这种能力可以被描述为机动性，它由两个特性组成。首先，操纵能力，其次，到关节极限的距离。这种移动性需要在生成无碰撞运动的同时进行优化，以便机器人在未来执行生成的路径时始终具有避开动态障碍物的灵活性。为此，我们提出了一种快速探索随机树 (RRT)，它应用额外的成本和采样方法来提高移动性。此外，我们提出了两种优化生成路径的方法。我们的第一种方法利用 RRT* 的内置功能。第二种方法通过使用高斯混合模型的运动规划的随机轨迹优化 (STOMP) 方法来优化路径。此外，我们在复杂的模拟和真实环境中评估了算法，并展示了移动性的增强。