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

Sampling-based motion planners perform exceptionally well in roboticapplications that operate in high-dimensional space. However, most works oftenconstrain the planning workspace rooted at some fixed locations, do notadaptively reason on strategy in narrow passages, and ignore valuable localstructure information. In this paper, we propose Rapidly-exploring RandomForest (RRF*) -- a generalised multi-trees motion planner that combines therapid exploring property of tree-based methods and adaptively learns to deploysa Bayesian local sampling strategy in regions that are deemed to bebottlenecks. Local sampling exploits the local-connectivity of spaces viaMarkov Chain random sampling, which is updated sequentially with a Bayesianproposal distribution to learns the local structure from past observations. Thetrees selection problem is formulated as a multi-armed bandit problem, whichefficiently allocates resources on the most promising tree to accelerateplanning runtime. RRF* learns the region that is difficult to perform treeextensions and adaptively deploys local sampling in those regions to maximisethe benefit of exploiting local structure. We provide rigorous proofs ofcompleteness and optimal convergence guarantees, and we experimentallydemonstrate that the effectiveness of RRF*'s adaptive multi-trees approachallows it to performs well in a wide range of problems.

Rapidly-exploring Random Forest: Adaptively Exploits Local Structure with Generalised Multi-Trees Motion Planning