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

Abstract

We propose a new method for collision-free path planning by Conditional Generative Adversarial Networks (cGANs) by mapping its latent space to only the collisionfree areas of the robot joint space when an obstacle map is given as a condition. When manipulating a robot arm, it is necessary to generate a trajectory that avoids contact with the robot itself or the surrounding environment for safety reasons, and it is convenient to generate multiple arbitrary trajectories appropriate for respective purposes. In the proposed method, various trajectories to avoid obstacles can be generated by connecting the start and goal with arbitrary line segments in this latent space. Our method simply 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 according to different optimization conditions. 4

A Simulator and First Reinforcement Learning Results for Underwater Mapping

“…Classical, collision-free planning [ 6 , 7 ] or shortest-path planning, such as the well-known Dijkstra’s method [ 8 ], do not work for IPP, since the dynamic evolution of the overall map must be considered instead of only the state of the robot.…”