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 novel centralized and decoupled algorithm, DDM, for solvingmulti-robot path planning problems in grid graphs, targeting on-demand andautomated warehouse-like settings. Two settings are studied: a traditional onewhose objective is to move a set of robots from their respective initialvertices to the goal vertices as quickly as possible, and a dynamic one whichrequires frequent re-planning to accommodate for goal configurationadjustments. Among other techniques, DDM is mainly enabled through exploitingtwo innovative heuristics: path diversification and optimal sub-problemsolution databases. The two heuristics attack two distinct phases of adecoupling-based planner: while path diversification allows the more effectiveuse of the entire workspace for robot travel, optimal sub-problem solutiondatabases facilitate the fast resolution of local path conflicts. Extensiveevaluation demonstrates that DDM achieves high levels of scalability and highlevels of solution optimality.

DDM: Fast Near-Optimal Multi-Robot Path Planning using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics