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

TL;DR

This paper introduces a novel collision-free planning approach using cGANs to map robot joint space to a collision-free latent space conditioned on obstacle maps, enabling flexible trajectory generation.

Contribution

The method leverages cGANs to create a collision-free latent space conditioned on obstacle maps, allowing arbitrary optimization-based trajectory planning in real-time.

Findings

Successfully verified on simulated and real UR5e robot.

Generated diverse collision-free trajectories based on different optimization criteria.

Demonstrated real-time trajectory planning capability.

Abstract

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.