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

To control how a robot moves, motion planning algorithms must compute pathsin high-dimensional state spaces while accounting for physical constraintsrelated to motors and joints, generating smooth and stable motions, avoidingobstacles, and preventing collisions. A motion planning algorithm musttherefore balance competing demands, and should ideally incorporate uncertaintyto handle noise, model errors, and facilitate deployment in complexenvironments. To address these issues, we introduce a framework for robotmotion planning based on variational Gaussian Processes, which unifies andgeneralizes various probabilistic-inference-based motion planning algorithms.Our framework provides a principled and flexible way to incorporateequality-based, inequality-based, and soft motion-planning constraints duringend-to-end training, is straightforward to implement, and provides bothinterval-based and Monte-Carlo-based uncertainty estimates. We conductexperiments using different environments and robots, comparing against baselineapproaches based on the feasibility of the planned paths, and obstacleavoidance quality. Results show that our proposed approach yields a goodbalance between success rates and path quality.

A Unifying Variational Framework for Gaussian Process Motion Planning