Collision-free Path Planning on Arbitrary Optimization Criteria in the Latent Space through cGANs

We propose a new method for collision-free path planning using Conditional Generative Adversarial Networks (cGANs) to transform between the robot joint space and a latent space that captures only collision-free areas of the joint space, conditioned by an obstacle map. When manipulating a robot arm, it is convenient to generate multiple plausible trajectories for further selection. Additionally, it is necessary to generate a trajectory that avoids collision with the robot itself or the surrounding environment for safety reasons. In the proposed method, various trajectories to 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 can be generated depending on the choice of optimization conditions.