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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Computing kinodynamically feasible motion plans and repairing them on-the-flyas the environment changes is a challenging, yet relevant problem inrobot-navigation. We propose a novel online single-query sampling-based motionre-planning algorithm - PiP-X, using finite-time invariant sets - funnels. Wecombine concepts from sampling-based methods, nonlinear systems analysis andcontrol theory to create a single framework that enables feedback motionre-planning for any general nonlinear dynamical system in dynamic workspaces.  A volumetric funnel-graph is constructed using sampling-based methods, and anoptimal funnel-path from robot configuration to a desired goal region is thendetermined by computing the shortest-path subtree in it. Analysing and formallyquantifying the stability of trajectories using Lyapunov level-set theoryensures kinodynamic feasibility and guaranteed set-invariance of thesolution-paths. The use of incremental search techniques and a pre-computedlibrary of motion-primitives ensure that our method can be used for quickonline rewiring of controllable motion plans in densely cluttered and dynamicenvironments.  We represent traversability and sequencibility of trajectories together inthe form of an augmented directed-graph, helping us leverage discretegraph-based replanning algorithms to efficiently recompute feasible andcontrollable motion plans that are volumetric in nature. We validate ourapproach on a simulated 6DOF quadrotor platform in a variety of scenarioswithin a maze and random forest environment. From repeated experiments, weanalyse the performance in terms of algorithm-success and length oftraversed-trajectory.

PiP-X: Online feedback motion planning/replanning in dynamic environments using invariant funnels