We propose a new method for collision-free planning using Conditional Generative Adversarial Networks (cGANs) to transform between the robot's joint space and a latent space that captures only collision-free areas of the joint space, conditioned by an obstacle map. Generating multiple plausible trajectories is convenient in applications such as the manipulation of a robot arm by enabling the selection of trajectories that avoids collision with the robot or surrounding environment. In the proposed method, various trajectories that avoid obstacles can be generated by connecting the start and goal state with arbitrary line segments in this generated latent space. Our method provides this collision-free latent space, after which any planner, using any optimization conditions, can be used to generate the most suitable paths on the fly. We successfully verified this method with a simulated and actual UR5e 6-DoF robotic arm. We confirmed that different trajectories could be generated depending on optimization conditions.
翻译:我们提出一种新的不碰撞规划方法,使用条件生成反反转网络(cGANs)来改造机器人联合空间和潜在空间,这些空间只捕捉到联合空间的无碰撞区域,并以障碍图为条件。产生多种可信的轨迹在机器人臂操纵等应用中是方便的,通过允许选择避免与机器人或周围环境碰撞的轨迹来操作机器人臂。在拟议方法中,通过将生成的潜伏空间的起点和目标状态与任意线段连接起来,可以产生各种避免障碍的轨迹。我们的方法提供了这种无碰撞潜势空间,在此之后,利用任何优化条件,任何规划者都可以在苍蝇上生成最合适的路径。我们成功地用模拟和实际的 UR5e 6-DoF 机器人臂来验证了这一方法。我们确认,根据优化条件,可以产生不同的轨迹。