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：我们提出了一种基于条件生成对抗网络(cGANs）的碰撞规避规划新方法，该方法可在机器人关节空间与仅包含无碰撞区域的隐空间之间进行映射转换，并以障碍物地图为条件约束。在机器人手臂操控等应用中，通过生成多条可行轨迹能够为规避机器人与环境碰撞的轨迹筛选提供便利。所提方法可在生成的隐空间中连接起始与目标状态任意线段，从而生成各类避障轨迹。该方法构建的无碰撞隐空间可兼容任意规划器及优化条件，实现实时生成最优路径。我们通过仿真与实际UR5e六自由度机械臂验证了该方法，并证实不同优化条件下可生成差异化轨迹。