English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a new method for collision-free planning using ConditionalGenerative Adversarial Networks (cGANs) to transform between the robot's jointspace and a latent space that captures only collision-free areas of the jointspace, conditioned by an obstacle map. Generating multiple plausibletrajectories is convenient in applications such as the manipulation of a robotarm by enabling the selection of trajectories that avoids collision with therobot or surrounding environment. In the proposed method, various trajectoriesthat avoid obstacles can be generated by connecting the start and goal statewith arbitrary line segments in this generated latent space. Our methodprovides this collision-free latent space, after which any planner, using anyoptimization conditions, can be used to generate the most suitable paths on thefly. We successfully verified this method with a simulated and actual UR5e6-DoF robotic arm. We confirmed that different trajectories could be generateddepending on optimization conditions.

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